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Building GA4 custom reports (you’re going to need them)

Sep 29, 2022

In the golden era of Universal Analytics (UA), Google pre-packaged a comforting array of reporting right out of the box. But, as companies transition to Google Analytics 4 (GA4) in preparation for UA’s planned sunset in 2023, marketers have been surprised to find far fewer of those pre-set analysis tools, and many are scrambling to rebuild the reports they rely on for key metrics. For example, if you check your UA account for acquisition, you’ll find roughly 25 different reports you can tap into right away. If you check acquisition in Google Analytics, on the other hand, you’ll see an overview screen and…two reports. But there’s no need to panic. While switching from UA means you give up those pre-packaged reports, what you gain from GA4 is the opportunity to collect data and analyze it in ways that make the most sense for your business. In this article, we’ll point you to places where you can find the UA reports you’re used to in GA4, and then we’ll show you how to build GA4 custom reports that fit your business needs. Forget the good old days. The best is yet to come. Find your favorite UA reports in GA4 While you might not be able to find a one-to-one match for everything you’re used to using in UA, GA4 does offer some reasonable facsimiles, although the naming may be different. Acquisition Reporting → Traffic Acquisition If you use UA’s Acquisition Reporting to answer questions about website traffic, you can find some similar metrics in GA4’s Traffic Acquisition. You’ll notice that Traffic Acquisition is set up in a similar format, but — and this is a big hurdle — you won’t be able to drill down into the data with a few quick clicks in GA4 like you can in UA. In GA4, instead of clicking around to find information, you use the plus sign (+) to set up secondary dimensions when you want to drill down into information. As you set up secondary dimensions, you’ll be able to search and narrow down the data to determine the best way to answer the questions your business is asking. In this case, GA4 shows you the same information you found in UA, but in a more targeted, deliberate format. Bounce Rate → Engagement Rate At first, it seemed bounce rate had bounced out of the analytics arsenal entirely with GA4, but now it seems the metric most responsible for marketing panic attacks is back, but in a slightly different form. Bounce rate does exist in GA4, but it’s calculated a bit differently because of GA4’s different data model. So, if you compare your current UA bounce rate to GA4, you will see a difference, and you’ll need to set new benchmarks. GA4 introduced a new metric to try and give us better information about how visitors use our websites: engagement rate. Unlike UA’s bounce rate, GA4’s engagement rate measures people who stay on your site and actually stay engaged. It’s a bit more dimensional than bounce rate, but also a little more difficult to manipulate. You can export engagement rate data into Excel if you’re up for doing a little more digging, but this report is one that might benefit from customization. Audience Overview → Demographics Overview Similar name, same functionality! As in UA, in GA4, Demographics Overview gives you a quick snapshot of your users, including: New vs returning users Demographic data Browser and operating system access Content Drill-Down → Pages & Screens Report In UA, the Content Drill-Down report gives a view of how site content performs at a hierarchical level within the URL structure. In GA4’s Page & Screens Report, on the other hand, you see your content by page title, but not by section. You can change the GA4 report view to page path, which allows a little bit more clarity, but the interface doesn’t support clicking around into different sections and paths. A few workarounds may help: Use the search function to look up different sections of your website, like “blog,” “about,” “services,” and so forth Export content to Excel to group and compare different sections against each other Use Explorations rather than Pages & Screens to dig into specific content performance questions Explorations When you can’t find a 1:1 match for a UA report you used to rely on, you could use GA4’s Explorations function to rebuild an exact match, but you could also take the opportunity to fine-tune the report to answer questions in an even better way. Within the GA4 Explore tab, users can build their own detailed reports, called Explorations, from a gallery of templates. We expect that this library will continue to grow but the baseline options are already quite useful. Of course, as you build out your own custom GA4 reports, you’ll want to start from a list of defined questions that serve your own internal goals, KPIs, and requirements. But to help you get the hang of how to create your own Explorations, we’ll outline a few examples here, based on UA reports you may be used to using. Behavior Flow → Path Exploration This report delivers a segmented view of website traffic. For example, to find out how many site visitors get to your contact page via organic search, you can create a path exploration by: Navigating to the GA4 Explore tab Choosing the path exploration template Clicking organic search sector Clicking the path to find how many of the visitors in this segment visited the contact page Behavior Flow → Funnel Report You can create a similar version of the path exploration report with a funnel report, adding more detail about the steps you want to analyze. You can set this report up by: Navigating to the GA4 Explore tab Choosing the path exploration template Clicking organic search sector Clicking the path to find how many of the visitors in this segment visited the contact page Adding form submission as a requirement Editing and adding steps to change the desired action This report can give you a better idea of common visitor behavior flows on your website. It’s a highly customizable GA4 report, both by steps and the dimensions of behavior you can track those journeys across the site. Exit Pages → Free Form Explorations Although you can’t find exit pages and exit page percentages out-of-the-box with GA4 as you can in UA, you can use free-form explorations to create a custom GA4 report to get you that data. Here’s the setup process at a high level: Navigate to the GA4 Explore tab Select the Free Form Exploration template Set up the page path you’re tracking, including exits and sessions Compare which pages have the most exits to total sessions to get the percentage While you can’t find the percentage in a calculated column, this report is a helpful replacement if you need to find this data quickly. Using Free Form Explorations, you can take any of your metrics and add any dimensions to home in on your data at a very close level. More options for building GA4 custom reports As you dig into GA4, you may find that you can add new dimensions and metrics to the tables for some of GA4’s limited out-of-the-box reports, but you may find that the results lack the depth you need. Depending on the types of reporting you need, you may also find that Explorations give you enough common views to replace most of what you find in UA. However, most marketers will need to get a step further in their GA4 reports before completely moving away from UA. With GA4 still in flux and new features and functionality shifting, Looker Studio (formerly Google Data Studio) may offer your team a way to find consistency and recreate some of the views you were used to using in UA with your GA4 data. Shifting from UA outputs to GA4 custom reports isn’t easy To make the switch from UA to GA4 seamless, you might need to call in reinforcements. Check out our GA4 resources or let us know if you have a specific question. Our team is helping mid-size businesses and enterprise-level organizations handle every aspect of the GA4 transition, and we’re happy to help you get what you need to be successful. [Match-up] Understand the differences between Universal Analytics and Google Analytics 4 [Path] What you need to know to upgrade to GA4 [Plan] 6 steps to make your Google Analytics 4 transition easier [Video] Meet the new Google Analytics 4 [Contact] Ask us anything

Wearables are changing healthcare: Impact for our most vulnerable

Aug 15, 2022

When it comes to understanding how wearables are changing healthcare, consumer brands serve as a solid leading indicator. Popularized by brands like Apple Watch, FitBit, and Garmin, the global wearable healthcare market was estimated at $16.2 billion in 2021, and is projected to double in the next five years. Healthcare wearables in daily life Although most users rely on healthcare wearables to check texts during spin class or crush their friends’ daily step records, an increasing number of users rely on smartwatches and other medical wearables for life-saving medical information. As the technology evolves, healthcare wearables can now give minute-by-minute EKG readings, monitor blood sugar, check oxygenation levels, and help people use real-time data to manage their health while they go about their regular activities. Wearables also deliver oversight and peace of mind to caregivers, as when diabetic children wear devices that monitor insulin and food intake and link to mobile apps monitored by their parents. These breakthroughs allow patients of all ages more autonomy while providing reassurance to caregivers that the person is safe. Learn more about what wearable devices make possible >> Healthcare wearables in long-term care settings Long-term care presents a gap between that at-home monitoring scenario and the tech-saturated acute care space of a hospital or clinic. Historically understaffed, nursing homes and long-term care facilities struggle with high turnover, increasing rates of preventable errors, and unnecessary escalation of avoidable medical events. In addition to the impact on the patient and their loved ones, these realities impact the facility itself through lower reimbursement rates and increased cost of care. A 2021 American Healthcare Association and the National Center for Assisted Living survey on staffing in these facilities showed that 99% of nursing homes and 96% of assisted-living facilities face a staffing shortage. Harvard University professor David Grabowski says the pandemic only worsened that already critical situation. He notes, “We’ve overlooked and undervalued this workforce for a long time and now we’re at a full-blown crisis…We’re in a crisis on top of a crisis.” Ensuring the right level of care for high-risk and elderly patients amidst staffing constraints formed a critical use case for transformation. Healthcare wearables emerged as a leading option that would give staff the ability to monitor more patients, get notifications when care is needed, and escalate when necessary. Overcoming obstacles to adoption Implementing a program for wearable devices in nursing homes introduced more stringent requirements than consumer wearables, including: Privacy protection: Patient medical information, covered under HIPAA, requires more protection than off-the-shelf iOS and Android systems offer. Usability concerns: Patients under care in nursing homes and long-term care facilities often lack experience with technology, and/or the dexterity to manage new devices. Cost considerations: In addition to the cost of patient wearables and devices allowing nursing staff to monitor and communicate alerts, facilities must also invest in secure data infrastructure and information architecture beyond standard integrations in market-ready smartwatches. Creating a targeted solution Realizing that nursing home and long-term care facilities faced unique barriers to implementing wearable devices, BioLink Systems set out to create a solution. Initially, the company devised a device that could be attached to an adult brief to monitor urination levels and body position. However, early issues with the prototype limited production scalability. Fusion worked with BioLink to architect a cloud-based IoT solution that uses machine learning to exceed the company’s initial vision. Designed with a minimalist aesthetic and user experience to fit the target demographic, the BioLink bracelet and adult brief wearables: Meet HIPAA requirements Monitor patient fluids Track patient vital signs Alert nursing staff when patient vitals fall outside their customized range Escalate alerts if patients are not attended within an allotted timeframe Initial testing and rollouts in nursing homes delivered immediate results, including: Improved patient care Decreased response times Fewer avoidable events such as medication errors Decreased escalation of care level, including hospitalizations Improved oversight Increased compliance with state, federal, and agency regulations Better experiences for the patient and their loved ones Learn more about how BioLink’s wearables are changing healthcare >> What’s next for wearable healthcare devices As facilities gather more data from using these devices, the machine learning algorithm BioLink and Fusion designed will continue to refine unique vital sign ranges for each patient, resulting in more targeted care. Future iterations of the BioLink device will integrate that information with the patient’s electronic medical record, enabling further customization of care. While each device starts with a baseline for normal with each of these vital signs, the more data that is collected, the better the facility can care for the patient. For example, if a patient’s oxygen level is continuously high, the device eventually creates a new threshold for that patient’s vitals and only sends notifications accordingly. Especially with the elderly population, there are many people that can’t communicate what they need or when they feel a certain way. There are endless possibilities for being able to provide better care under these circumstances. With options like dehydration sensors, nursing care staff is better able to not only bring water to patients but ensure that they are actually consuming it. The more variables, the better Ultimately, the more variables, the better the information — resulting in better care and better outcomes. The correlation and combination of all the data from a patient can detect changes and allow for more timely, preventative care. And the more information included, the better the insights from the algorithm. With the right information, staff can prevent different medical events by predicting problems and eventually creating better remedies and treatments to avoid costly medical interventions or catastrophic incidents. As industry leaders and healthcare facilities see the impact of devices like BioLink’s bracelets, we expect to see greater adoption of healthcare wearables to elevate patient care, reduce facility costs, and find operational efficiencies even during times of staffing crises. With the right technology and innovation, we can change outcomes and save lives — with a wristband.

Accelerating digital transformation in finance and banking

Aug 11, 2022

From online transactions to mobile payment apps like Venmo, today's consumers increasingly look for digital access to funds — and they expect a seamless experience. As customer expectations and the economy continue to evolve, digital transformation in finance and banking needs to keep pace. Banking culture hasn’t always kept up with what digital customers are actually looking for. The World Banking Report 2021 reported that, “Despite being vocal about improving the customer experience, the banking industry’s delivery of the key components of a strong customer experience, such as improving transparency and social responsibility, improving customer support, and reducing the cost of services, falls far short of customer expectations.” Three common barriers to digital transformation in banking There are several common challenges to digital transformation that keep banks from pivoting quickly to meet customer expectations. Roadblock #1: Technical debt As a highly regulated industry, traditional banking relies on complex and siloed legacy technologies that are often expensive to maintain. Over time, technical investments compound, making it increasingly difficult to find time or resources to shift to more modern or scalable platforms. When banks grow through mergers and acquisitions, attempting to integrate additional legacy systems adds to that technical debt. At the same time, banks face increasing competition from the fintech sector — online-first financial institutions that aren’t encumbered by aging platforms. Traditional banks saddled with technical debt may feel that they lack the time or resources to fully integrate, modernize, or replace their legacy technologies. But the longer this debt persists, the harder it is to compete with digital natives, leaving banks less agile in the marketplace. How platform modernization helped make an annuity organization more competitive >> Roadblock #2: Organization size Like many enterprise-level organizations, larger banks often create internal digital teams that combine business, IT, and marketing capabilities and develop expertise in their own technologies, systems, and processes. Faced with competing internal priorities and hampered by regulatory constraints, these internal teams may struggle to get alignment and prioritization for a banking digital transformation strategy and may lack the breadth of expertise necessary to implement a comprehensive modernization effort. Smaller banks, on the other hand, may be more nimble and successful at shifting internal priorities, but they may not have the resources to staff dedicated teams. While organization size is often called out as a hindrance to effective digital transformation in banking, the underlying problem may not actually be a headcount issue. Regardless of size or industry, most companies miss their digital transformation goals due to lack of clarity and strategy. “Digital transformation” in finance or any sector can be hard to define, implement, and measure. A more strategic approach starts with identifying concrete problems or issues, understanding customer needs, and developing solutions that bridge the gap with action steps that are clear, dynamic, and measurable. How technology strategy comes to life >> Roadblock #3: Relying on assumptions about customer needs and wants Understanding customers’ needs, pain points, and experiences can be difficult, and as users adapt to technology their preferences continue to change. This makes audience research even more critical when defining your bank’s digital transformation strategy. After a surge in remote work due to Covid, comfort levels with technology are at an all-time high. Research from McKinsey found that 75% of people using digital channels for the first time during the pandemic indicate that they will continue to use them when things return to “normal.” Not only are customers more comfortable with banking technology, but it has also become an important factor in choosing which bank to use. According to Mobiquity’s 2021 digital banking report, 40% of respondents agreed that they are likely to switch accounts to get better digital tools. Investing in both qualitative and quantitative data can dispel assumptions about your audience, while also revealing specific ways to improve the customer experience. As those opportunities are identified, banks can prioritize technology and services that will have the biggest impact. What goes into a successful customer experience strategy >> How to approach a digital transformation strategy in banking Given these challenges and the continuous evolution of customer expectations, several technologies offer significant potential gains and can help financial institutions stay competitive. Mobile app enhancements Mobile banking apps typically offer the ability to check balances, transfer funds, pay bills, and chat online with a bank representative. By building applications that go beyond these basic services, banks can increase their new customer base while improving customer retention and lifetime value. Leaders in the banking space now include peer-to-peer payments, lending inquiries, and chatbots as part of their applications. However, in addition to monitoring what competitors are doing, it’s important to implement a robust discovery process to see what the target audience wants from a banking app. This could include developing target personas and performing pain point analysis to find unique solutions and services that will better appeal to customers’ needs. From there, financial institutions are better poised to tackle the next layer of technology for the app space — personalization. Many banks are investing in personal financial management tools and customized product offerings in their apps, making banking more accessible and valuable than before. These user-friendly applications and their customization capabilities are an integral part of digital transformation in banking. Refine your mobile applications and provide a better customer experience >> Machine Learning Historically, machine learning engagements have required substantial data science and model training investments. But major ML platforms have evolved, lowering the barrier of entry for these projects. Now, midsize and even smaller banks can use machine learning models to better understand their customers and drive a more personalized experience. And machine learning isn’t just valuable for deepening current relationships; it can also help banks target and acquire new business by identifying trends and opportunities. This means higher quality leads, improved retention, and an increase in business with more potential for high lifetime value. [On-Demand] Reimagining customer insights, risks, & relationships through machine learning >> Data management strategy Traditional lending institutions underwrite loans by using a system of credit reporting. Banks that process loan applications evaluate the risk by looking at credit scores, homeownership status, and debt-to-income ratios. Today, three major credit bureaus provide this information. But these reports can sometimes contain erroneous information, and the information comes at a high cost since it can only be found in three places. And while banks often collect their own internal data, if that data is incomplete or disorganized it cannot offer useful insight. With structured data management strategies, financial institutions can mitigate losses by generating more data and using it to recognize trends and potential liabilities. See how one bank improved ROI by 1054% through strategic data management >> Robotic Process Automation (RPA) Some banking processes are still highly manual. Consider routine tasks like opening an account or reporting a stolen credit card — it takes time to get through the questions, and usually requires a phone call from the customer. With robotic process automation (RPA), in the case of a stolen credit card, the workflow process can automatically cancel the old card, issue a new card, and confirm the mailing address of the new card. RPA can also identify bots or theft with greater accuracy than a human analyst. RPA even has the potential to assist with workload transformation. In addition to streamlining and automating internal processes, RPA can be used to manage the cloud technologies that institutions rely on for their everyday tasks. This leads to more refined workload placement — and therefore a more productive workforce. The bottom line on digital transformation in banking From highly-personalized service offerings to easy-to-use applications, consumer expectations are high in the banking sphere. To keep up with these expectations, banks must position themselves to adapt quickly. Traditional banks are often at a disadvantage to digital-only competitors. Newcomers operate without the burden of legacy systems and outdated business models. But a digital-first attitude can help financial companies effectively implement technologies that enable digital transformation in banking. Find out how one financial services firm successfully handled digital transformation >> Ready to boost your productivity and customer engagement? Let us know your questions and find out how a strategic approach to digital transformation can help your bank thrive in a digital-first world.

How to choose a CDP

May 13, 2022

Organizations collect data from a wide range of sources and store it in any number of solutions, which can be spread across the business. For marketing departments trying to deliver personalized customer experiences, those silos present a problem, and a customer data platform (CDP) can seem like an easy answer. That could be the right conclusion, but it also might be premature. If you’re wondering how to choose a CDP, it makes sense to start with the basics. How to choose a CDP: 1. Understand the benefits and limitations of CDP solutions 2. Get internal alignment around the need and timing for a CDP 3. Surface your requirements, and prioritize your top 3-5 4. Map your core requirements to functionality you need in a CDP 5. Get stakeholder buy-in and secure a budget 6. Create a CDP selection matrix 7. Get external help if you need it 1. Understand the benefits and limitations of CDP solutions The CDP Institute defines a customer data platform as “packaged software that creates a persistent, unified customer database that is accessible to other systems.” This simple definition goes a long way toward unpacking the pros and cons of CDPs. A CDP is packaged software. PRO: A CDP is prebuilt and doesn’t require as much help from IT to implement and maintain as other solutions like custom platforms and data warehouses. CON: Because it’s packaged software and not a platform, a CDP will be less customizable and less efficient than adding customized functionality to an existing architecture. A CDP must create a persistent, unified customer database. PRO: A CDP ingests data from multiple sources across the business, standardizes it, and uses it to build an identity graph to enable real-time customer identification that fuels targeted multi-channel marketing efforts. CON: A CDP overcomes some of the difficulty of identity resolution but obscures the methodology. You get a unified customer view, but not a lot of certainty that it’s accurate. A CDP must be accessible to other systems. PRO: You can use the aggregated data and outputs from a CDP with your downstream systems. CON: The CDP you select must either come with the API connections you need for the rest of your martech and enterprise tech stacks or you’ll need to factor in costs for custom APIs. 2. Get internal alignment around the need and timing for a CDP Today’s martech stacks are growing fast, and it’s not always clear whether the functionality your marketing team needs already exists in the broader enterprise architecture. Overlapping features and blindspots are a problem — but also an opportunity. Gathering a cross-functional group that includes marketing, IT, data, and product teams can help you figure out how to get the most from your technology investments across the business. If you’re wondering how to choose a CDP, you might be surprised to find that many of the core functions you’re looking for in a CDP are already available in your CRM, MDM, data warehouse, analytics, and BI solutions. For example: Some analytical CRMs can track real-time online events like website browsing, adding to cart, and the like, much like CDPs. Your data warehouse may allow for an identity graph overlay and machine learning algorithms that can play a key role in enterprise-wide customer identity resolution. Your IT and data teams may already have identity and access management processes in place, and that single source of customer data truth could be integrated with your existing martech stack. So, how do you know if your organization needs a CDP? Your cross-functional group can explore potential use cases in light of departmental needs, budget, and existing functionality. While every organization is different, here are some ways to frame the conversation. You might need a CDP if: Your organization has a large volume of customer data stored in multiple places, and you either can’t or haven’t been able to integrate it into a single, real-time view Your marketing team can’t access customer data or perform data tasks without help from data and IT teams You can’t unify your online metrics, CRM data, and offline touchpoint and transactional data, making it hard to build a 360-degree view of your customer You’re moving to a first-party data strategy, but you don’t have systems in place to use your data to inform audience segmentation and personalized campaigns You have functionality gaps in your current martech stack that match up to CDP features You might NOT need a CDP if: You have a minimal and well architected martech stack Your customer data is simple or straightforward enough to analyze easily without additional tools Your marketing plan doesn’t require a lot of personalization, either because your products and services don’t require it, or because your roadmap doesn’t call for it in the short term Your customer data strategy has already mapped your needs to existing solutions and your roadmap doesn’t include a CDP Your budget doesn’t allow for duplicating storage costs, building and operating data ingestion processes, or keeping up with the steep total cost of CDP ownership Your security requirements don’t allow for third-party customer data storage Your internal IT and data teams find more affordable and secure means to implement identity and access management, democratize data access, and connect data storage with martech tools 3. Surface requirements, and prioritize your top 3-5 As you meet with your cross-functional team and discuss your need for a CDP, you can also surface requirements and use cases for a CDP solution. From your larger list, choose 3-5 top priorities to help you choose a CDP. Some common examples of CDP use cases include: Streamlining identity resolution, and making those outputs more accessible and actionable Combining online and offline data Creating more personalized content experiences on your website Using more strategic targeting in your multichannel campaigns Integrating and standardizing data across systems and making it easier to use those outputs in omnichannel marketing efforts 4. Map your core requirements to features you need in a CDP After you identify your top 3-5 use cases, map those core requirements to features you need in a CDP. For example, if one of your core requirements is enabling more targeted multichannel marketing campaigns in the EU, you might need to look for a CDP that offers GDPR-compliant identity resolution processes. Be sure to note which systems and solutions you’ll need to connect to your CDP both in terms of data ingestion and output sharing with downstream systems. Your CDP will need integrations and APIs to enable those connections. Common CDP integrations include CRMs, analytics tools and dashboards, advertising platforms, BI tools, data warehouses, and data lakes. Meet with your cross-functional group again to confirm your conclusions and assumptions. This meeting is a good time to clarify any security and data governance implications for your CDP selection as well. 5. Get stakeholder buy-in and secure a budget Your next step is to get stakeholders on board for the CDP acquisition and to establish funding to make the purchase. Leadership needs to know the full report from your cross-functional team, but they’ll also have questions about what kind of return the organization can expect from the investment As you frame up the value story, consider thinking about what not having a CDP costs your company: Is your marketing team using a significant amount of IT or data team hours looking for information? If you were to solve your data connectivity issues with custom-built APIs, how much would that cost? How much are you losing in customer value by not providing a customized experience? How much time is your marketing team spending manually moving data from one system to another? 6. Create a CDP selection matrix The vendor selection process doesn’t have to be painful. Your team can feel more confident about choosing a CDP because you’re going into the process with defined use cases and requirements, as well as support from the organization. That said, with an ever-growing CDP vendor landscape, it helps to have a process for narrowing down your choices. To accomplish that task, many companies use an internally framed selection matrix or decision tree, customized to fit CDP-specific needs and requirements. These tools can help you think through solution options using your own criteria. However you handle the vendor review, plan to bring a short list of possibilities back to your cross-functional team for a final discussion before making a purchasing decision. 7. Get external help if you need it. Figuring out how to choose a CDP and where the solution fits into your larger customer data strategy isn’t easy. Fusion Alliance helps companies navigate changing customer data environments with a unique methodology that fosters collaboration, transparency, and shared ownership of digital transformations.   Whether you’re just getting started or stuck in the messy middle of a CDP selection process, we help you unpack your processes and partnerships to identify risks and opportunities so you can take the right next steps toward a future-focused customer data strategy.   Let’s talk. Keep reading: The Ultimate Guide to Creating Your Customer Data Strategy

Customer data privacy compliance

Apr 29, 2022

Building trust into your customer data strategy In the rapidly changing regulatory environment around customer data privacy and security, it’s easy to get so caught up in specifics that you miss the big picture opportunity to build trust with your customers. Fundamentally, every box you check with your data: consent, collection, storage, use, sharing, and more is reflective of your respect for your customer. After all, how you treat their data tells customers a lot about how you’ll treat them. To take a customer-centric view of your data privacy compliance policies and procedures, we recommend a series of concrete steps applicable to any business or organization that deals with customer data in any form. While not exhaustive, this list forms a solid foundation for building trust in your data strategy. Know where you’re vulnerable One reason businesses are uniquely susceptible to data breaches and privacy omissions is the ubiquity of customer data across business units. While different departments may source, store, and use data in different ways, its presence demands compliance. The best way to get a handle on the issue is to create a data map. This unified view of the information you have, where it’s stored, and how it flows within your organization is not only helpful from a security and privacy compliance perspective, but it can also be a helpful resource for the business. Build a comprehensive data map in our Customer Data Strategy workshop >> Set guardrails When it comes to collecting customer data, just because you can doesn’t mean you should. Using your data map, think through your customer data requirements, and only plan to collect, process, and store what you really need to meet your goals. Many organizations find it helpful to spell out this less-is-more approach to data collection in a formal policy, and then make it part of their data culture and training. Pro Tip: Having written policies around customer data collection, use, storage, transmission, and sharing is important, but not sufficient. Building policies into your corporate culture takes focused planning and effort, but it pays off in compliance. Understand the current landscape Depending on your industry, location, and customer base, your company may be subject to different privacy laws, regulations, and requirements. Some common standards include GDPR, CCPA, and HIPAA. Your legal team probably already has a good handle on which of these apply in your current context, but do your security, IT, data, and business units have complete understanding of those implications? Consider cross-referencing relevant requirements to your data map to be sure your organization is fully compliant. Build an agile data privacy program What if your company is not currently subject to those regulations? As you build your data privacy program, it may be wise to look into today’s privacy standards as a near-future view. Legislatures and courts continue to support customer privacy, and policy changes at Apple, Google, and other large tech and search companies forecast trends to greater restrictions on customer data collection and use. Using current standards to anticipate future changes makes sense. Keeping your data privacy program agile and flexible not only ensures that you’ll stay ahead of costly changes in the future, but also builds customer trust — a valuable goal regardless of external factors. Establish a security and governance framework Depending on your industry, this may be a formal, full-time responsibility for an individual, team, or an entire department. Or you might opt for clear policies and procedures shared across business units. In any case, your customer data security and governance framework should include, at minimum: What types of customer data are collected How customer data is protected, during transmission, in use, and at rest What standards you follow for data security, quality, access, and retention Which protection measures you use when data is transferred, stored, or used, such as data masking, tokenization, format-preserving encryption, or keys Improve the user’s experience We commonly think of user experience on digital properties in terms of how a website or landing page looks and functions. But how you interact with your customers about their data is also part of their experience of your brand. Commit to clarity: rather than burying consent information in lengthy legalese, be up-front, clear, and simple in how your structure and format your consent management and opt-in features. As you think about the ways your customers experience your privacy program, some topics to consider clarifying include: How individuals can consent to (or opt out of) your company collecting and processing their data Why customers might want to share their data — that is, what they get out of the exchange in terms of improved experience, personalized discounts, and the like How you’ll establish that a user is over 16 or over 18, if your industry or topic requires that distinction How a customer can request to have their data deleted, and how your organization will comply Put the customer first Data privacy protections show no sign of slowing down, but companies with strong customer data strategies don’t need to worry. Whatever the future holds, building a customer-first approach to collecting, storing, and using data pays off in terms of strengthened relationships across the buyer’s journey and throughout the customer lifecycle. Not sure how to get started? We can help. Our team of digital, data, and technology experts partners with you to get your customer data strategy going — or back on track. Let’s talk >> Learn more about customer data strategy >>

How a customer data strategy comes to life

Apr 27, 2022

After investing in martech solutions — often layering in new platforms and software over time — many organizations find themselves stuck. Whether the root issue is technology, processes, or capabilities, teams get frustrated when their tools don’t deliver. If you’re in a similar position, the best plan is often to step back and review your customer data strategy. It might be time to re-evaluate in light of changing circumstances and shifting organizational goals. You might need a new roadmap to accommodate new privacy regulations. Or you might need a fresh take on how your martech stack fits into your enterprise architecture. Customer data strategies come to life in different ways, but smart implementations always start with well-aligned use cases and clear expectations. In this article, we’ll look at three real-life examples of how organizations we work with got unstuck by creating or refreshing their customer data strategies. Transformation 1: From scattered data to always on marketing Our client managed customer data across multiple platforms, with no connectivity between digital and on-premises touchpoints. Lacking a unified view of customer behavior, the client defaulted to scatter-shot marketing, with disappointing results. As part of a customer data strategy engagement, Fusion helped this client: Define what wasn’t working and identify root causes Align business objectives, technical requirements, and key use cases Recommend near-term remediation and future-state strategies Establish a roadmap with incremental steps toward the solution Then, we worked with the client to implement, test, and refine the customer data strategy, bringing the new solution to life in a way that fit the company’s culture and environment, including: Developing a Master Data Platform Customizing multiple platform APIs to unify customer engagement data Integrating multiple digital platforms Implementing PowerBI for data visualization As a result, the client now has a consolidated view of real-time customer behavior and multi-channel marketing activities, which enables an “always on” approach to customer engagement. Transformation 2: From customer churn to customer retention Another client was experiencing high rates of customer turnover but because they couldn’t discover the cause, they couldn’t develop a strategic plan for turning the trend around. Our team suspected that the key was in the client’s customer data. To identify root causes for the customer churn, we: Assessed the client’s customer data, which was housed in various locations and at different levels of quality across the organization Implemented a centralized data platform to reconcile and unify customer data from different systems of record Consolidated and cleansed the customer data, making it easier to use and analyze Designed machine learning models to test high-value use cases like identifying warning signs of customer churn, flagging high-risk customers that fit the indicators As a result of centralizing and standardizing customer data, and using machine learning to quickly analyze significant current and historical information, our team helped the client flag customers likely to leave and put retention strategies into action to reduce the churn rate.  Transformation 3: From disconnect to martech maturity Another client we worked with had invested in powerhouse martech tools but wasn’t seeing the return they had expected. Overwhelmed by the disconnect between expectations and results, the organization asked us to help sort out what had gone wrong. Our team helped the client re-evaluate their customer data strategy to determine the best path forward. Some of our work included: In-depth analysis of existing technology platforms, software, and services Clarifying the customer journey and identifying friction points both for internal and external users Optimizing technology configuration and integrations, including key architectural changes Cleansing data to remove duplicate information and give the client greater confidence in the quality and reliability of the data they collected Implementing process and governance improvements As a result, the client’s marketing team now works faster and more independently of IT, confidently using customer data to automate and personalize marketing touchpoints, and speeding up time to execution for their outreach and campaigns. Get your transformation back on track Ready to do more with your customer data and martech solutions? Defining a customer data strategy and bringing it to life doesn’t have to be so daunting. Whether you need a quick consultation or an in-depth engagement, our team can help you identify opportunities, outline a path forward, and put you on track to optimize the ways you collect, store, and use your customer data. Let’s talk >> Get the Ultimate Guide to Creating a Customer Data Strategy >>

CDP vs CRM: 5 key questions to inform your decision

Apr 22, 2022

The difference between a customer data platform (CDP) and customer relationship management (CRM) solution may be difficult to determine at first, because both options collect, store, and put customer data to use in support of business goals. While their functions may overlap, the CDP vs CRM debate becomes easier when you get clarity about the people, processes, and use cases for each option. How to make the CRM vs CDP decision 1. What is a CDP? 2. What is a CRM? 3. What data is collected by a CRM vs CDP? 4. Who uses a CDP vs CRM and for what purpose? 5. What do we need: a CDP, CRM, or both? 1. What is a CDP? A CDP unifies and standardizes large and detailed data sets from a wide variety of sources, resulting in robust customer profiles that enable real-time personalization. The CDP Institute defines a CDP as “packaged software that creates a persistent, unified customer database that is accessible to other systems.” Additionally, a CDP must have the following capabilities: Ingest data from any source Capture full detail of ingested data Store ingested data indefinitely (subject to privacy constraints) Create unified profiles of identified individuals Share data with any system that needs it Through the process of identity resolution, the CDP can match, merge, and deduplicate data into a single customer view that can be segmented and analyzed — by human analysts or with the assistance of machine learning. 2. What is a CRM? A CRM and a CDP are both software solutions that handle customer data, but they differ in how, why, and who for. The difference came about organically, as organizations adopted different use cases for their customer data over time. “CRM solutions were often proposed to tackle customer data management problems. The idea was that you could get ‘all of your data in one place’ to use for sales, marketing, and customer service. The promise was they’d break down silos in enterprises and design a view of the customer that wasn’t specific to sales or marketing or customer service. That sounds familiar to the promise of CDPs, doesn’t it?” — Lizzy Foo Kune, senior director analyst at Gartner A CRM helps organizations manage customer relationships by consolidating what is known about customers from one-to-one touchpoints and transactional details into a single database, giving sales and service teams personal and actionable insights. According to the Microsoft Dynamics 365 website “CRM systems help you manage and maintain customer relationships, track sales leads, marketing, and pipeline, and deliver actionable data.” Sound similar to a CDP? There’s a key difference: CRMs only apply to known customers and contacts. Moreover, they don’t cleanse, combine, standardize, or deduplicate the customer records, so they can’t give a business a “single customer view” across channels. 3. What data is collected in a CRM vs CDP? That key difference reflects the two business silos that CRMs were developed to unite: marketing and sales. Marketing needs a high volume of customer data across touchpoints in a single, unified view to understand your customers and their behavior. CDPs collect digital data automatically using integrations and code snippets embedded in digital touchpoints, gathering customer data from websites, laptops, mobile devices, apps, and even CRMs into one place. The CDP then cleans it, and produces consolidated customer. Sales needs customer data to help manage the customer relationship. CRMs store historical data about customer interactions in order to inform future interactions. The data CRMs collect is usually entered manually and its purpose is tightly focused on logging an interpersonal or transactional interaction — for example, notes from the latest sales call – to inform future interactions. The data inputs are simple, although difficult to standardize or automate, and are usually done manually by sales (and service) people to track the progress of the relationship. 4. Who uses a CDP vs CRM and for what purpose? Your organization’s CDP vs CRM discussions may come down to who needs to use the system to accomplish critical business tasks. As we’ve said above, marketers need a unified view of the customer’s entire experience of the brand over time. A CDP’s ability to ingest, cleanse, manage, and analyze large volumes of data from many digital sources makes that task easier. But for sales and support teams, the key driver is managing customer relationships. In these customer-facing roles, contact management is critical, so a CRM’s ability to capture notes and manual inputs about one-to-one interactions facilitates that function. 5. What do we need: a CRM, CDP, or both? While choosing between solutions isn’t easy, it’s not necessarily an either/or decision. You might find a both/and solution serves your business better. How do you make the call? If your business primarily needs to manage customer relationships in a more detailed, efficient, and personalized way, you might choose a CRM. In fact, over the last few years, CRMs have been innovating and evolving to function more and more like CDPs so it might be prudent to wait and/or choose vendors carefully. Gartner predicts that 70% of independent CDP vendors will be acquired by larger technology vendors or will diversify by 2023.  “CRM systems have seen the competitive threat that CDPs brought to the table,” Gartner’s Foo Kune said. “As CRM technologies recognize that they need to update their aging databases to meet the needs of modern business functions, including marketing, augmenting your CRM with a CDP may be unnecessary.” If your business primarily needs to have a broad view of who your customers are and how they engage with your business, you may opt for a CDP. “Companies seeking a new strategy to form personalized customer experiences through data will need a CDP as it offers the resources to create a comprehensive view of the customer across each platform they interact with in real-time — whether it’s social media, apps or mobile,” says Heidi Bullock of Tealium, a CDP provider. “CRMs, on the other hand, help manage sales-focused customer data rather than collecting data across different channels.” And, if your business is broad you can choose both a CDP and a CRM.  While CDPs and CRMs offer two different marketing and sales data management solutions with differing strengths, you don’t necessarily have to choose between them. “CDPs and CRMs can actually operate simultaneously, as they work to fulfill different business goals,” Tealium’s Bullock notes. It’s possible to use a CRM as an input and output channel to a CDP, and, in turn, use a CDP to provide a 360° customer view data set within the CRM. Choosing both a CDP and a CRM can deliver both an amazing customer experience and tremendous business value: achieving high marks in customer satisfaction and providing integrated tracking and engagement. The CDP vs CRM choice depends on your roadmap. Fusion works with clients to define a customer data strategy that fits each organization’s unique strategic objectives, operational needs, and timeline. From there, our team creates a tactical roadmap to define actionable steps toward those goals. Whether you’re just getting started or trying to get your digital transformation back on track, we can help. Ask a question >> Book a workshop >> Learn more about customer data strategy >>

The call to create first-party customer data strategies

Mar 9, 2022

Navigating the new data landscape Today’s consumers expect increasingly personalized experiences. To deliver that customized experience and increase market share, business leaders rely on personalization. Ever wondered how ads seem to follow you around the internet? That’s personalization at work. And the engine that drives it is data. The changing data landscape makes it essential for companies to re-evaluate their customer data strategies — and fast. Let’s get started. Where we are now As part of building a direct relationship with your customers, you probably collect some form of data with their consent. This information, known as first-party data, can include purchase history, application and website interactions, opt-ins and subscriptions, and the like. A customer provides a business with first-party data with the understanding that the business —and only that business — will use that data to better serve them. But not all data is collected through active consent. All your customer’s online activity and search history is currently tracked by cookies. That information, known as third-party data, can be sold from one business to another, without the customer’s knowledge, with the goal of getting more information about aggregated pools of consumers with similar behaviors. Think about the last time you shopped for a new car. You probably looked up options online or Googled dealerships nearby. Then, BAM! Suddenly all the ads you saw on Facebook, YouTube, and other websites were from car dealerships, with information targeted to the very model you were thinking about. That’s third-party cookies at work! Using third-party cookies, advertisers pay to get one step closer to consumers’ brains, gaining access to the professional status, consumer preferences, and personal interests that are otherwise outside of their first-party scope. Data in real-life While most organizations collect first-party data from their customers, many still rely heavily on third-party data and use a combination of the two to build their personalized customer experience. For example, using in-store Wi-Fi, Walmart collects a combination of first- and third-party data on close to 145 million Americans. Individual profiles include what customers buy, which stores they visit, and where they linger while they shop. Analyzing every clickable action the customer takes on Walmart.com, social media activity, and even weather data completes a full customer profile. By aggregating and analyzing all this data, Walmart makes inferences about highly personal circumstances, knowing that if you’re buying newborn diapers today, they should start hitting you with advertising for toddler sizes in two years. Even without a loyalty program, Walmart’s data and analytics ensure they know their customers and they have what they need to create a personalized shopping experience. Why these trends matter While customers may enjoy the customization of their experience, they don’t necessarily appreciate the creepiness factor of how often their privacy is invaded. The amount of information that consumers provide — knowingly or unknowingly — on a regular basis, which is then sold to other companies, creates a huge risk of data breaches and identity theft. Many organizations and advertisers see the coming deprecation of third-party cookies as a win for privacy, but a loss for businesses. But it doesn’t have to be a one-sided victory. Smart companies are figuring out how to use first-party data to achieve a personalized customer experience, turning the challenge into a win-win opportunity. Leveraging your first-party data To make up for the loss of third-party data, you need your customers to feel comfortable sharing their data with you. First-party data requires customers to be actively involved. Many customers are willing to give their information if they know it is being used responsibly by the business and that they will receive a better customer experience because of it. Your goal is to communicate how the information customers provide your company enables you to deliver a valuable, relevant experience that fits their needs and leaves them in control of their data. Focus on customer retention over acquisition When you deliver a relevant experience to your customers, they’re more likely to stay customers. First-party data allows you to deliver a more customized experience by gaining an understanding of each customer’s preferences for your products and services. You’re then able to re-engage through your owned channels and increase the lifespan of the customer relationship. And it doesn’t mean you have to leave acquisition behind. Just think … happy, loyal customers are likely to refer friends and family. Offer value in exchange for data Customer loyalty programs are an excellent example of successfully using first-party data, especially where there is a value-add such as accumulating points, getting a discount, early-bird notifications, and the like. In this case, customers are incentivized to provide their data and you can build a unique customer profile that will allow you to customize their interaction with your brand in the future. Create personalized content First-party data helps you discover who your customers are and find out about their habits and preferences directly from the source. Insights gained through first-party data analysis allow you to create different targets and strategies for delivering content personalized to your most loyal customers. It’s a trade-off The reality is, by increasing consumer privacy, businesses lose access to aggregated information and the ability to mass personalize and customers potentially lose the personalized experiences they’ve come to expect. The biggest question remains: can organizations fill the gap and start collecting and leveraging quality first-party data? The time is now Data and marketing teams must work together, and fast, to create first-party customer data strategies ahead of the cookie deprecation timeline. Building processes to collect quality data, keep it safe, analyze it correctly, and use it to create meaningful customer experiences won’t be easy, but laying that groundwork now can position your company for success in the new customer data landscape. This article was originally published on CDO Magazine.

Wearables and mobile machine learning change the face of healthcare

Jul 8, 2021

Advances in mobile health technology have transformed the entire landscape of healthcare, including the ability of physician groups, employers, nursing home facilities, and pharmaceutical companies to capture data in the healthcare space. Gone are the days of paper tracking for your glucose levels and blood pressure. Instead, wearable devices like watches and trackers can seamlessly provide real-time data streams to applications and third parties. The data collected from wearables can be used for clinical research, patient monitoring, and wellness tracking, among other uses. Each data point collected can add complexity to your broader data set. Because of the amount and complexity of data, turning to machine learning (ML) can help organizations leverage their data to identify patterns and make data-driven decisions. By applying machine learning techniques to wearable device data, we can now surface patterns in big data and make predictions about behavior. Machine learning enables healthcare-related industries to leverage wearable device data and identify trends, improve recommendations, and define research outcomes. Popularity of wearable devices Wearables are popular, and their adoption continues to grow. Globally, the wearable technology market is expected to grow from $69 billion in 2020 to $81.5 billion in 2021, an 18.1% increase, according to the latest forecast from Gartner. What’s fueling the growth? Demand for smart devices in the healthcare sector is rising, as is demand for Internet of Things (IoT) devices. Many devices are not fitness-specific, featuring notification of text messages, push notifications for mobile apps, and the ability to pay for items by scanning a QR code with Google Pay or Apple Pay. As such, they have broad appeal. “As a result of the pandemic, we have seen wearable devices become much more than just activity trackers for sports enthusiasts. These devices are now capable of providing accurate measurements of your health vitals in real time. Improved measurement accuracy coupled with the latest advancements in ML make it possible to detect abnormalities before they lead to a major health event.” – Alex Matsukevich, Fusion Alliance Director of Mobile Solutions Types of data collected by wearable devices There are a variety of brands and categories of wearable devices, from mass-market consumer versions to highly specialized types created for niche uses. Apple, Fitbit, Google, Samsung, Garmin, LG, Sony, and Microsoft dominate the market. Though the concept of “wearables” includes a focus on wristwatches, while exercise equipment, glasses, and textile sensors are also becoming more common. Wearable devices can measure: Sleeping patterns Heart rate Irregular heart rhythms Location/route during exercise Pace, stride, and distance while moving Blood oxygen levels Falls Limitations of wearable device accuracy Wearables do have limitations, and accuracy is a concern. Healthcare decisions made using erroneous data could have outcomes detrimental to a patient’s overall health. A study from the University of Michigan reviewed 158 publications examining nine different commercial device brands. In laboratory-based settings, Fitbit, Apple Watch, and Samsung appeared to measure steps accurately. Heart rate measurement was more variable, with Apple Watch and Garmin being the most accurate, and Fitbit tending toward underestimation. But for energy expenditure (calories burned), no brand was deemed accurate. This does not mean that the results are invalid, but that there is a significant difference between results from wearables and clinical results in a lab setting. Wearable devices are constantly upgraded and redesigned as technology improves. And data collected by wearables does not provide a clinical diagnosis. As such, this data is just part of the larger picture of health and can be used only in conjunction with other factors to evaluate your overall wellbeing. Overcoming the biggest challenge of wearable device data analysis Healthcare professionals are already using ML to analyze data for patients. Research published in the International Journal of Research and Analytical Reviews confirms that ML techniques are successful in predicting health conditions such as heart disease, diabetes, breast cancer, and thyroid cancer. The biggest hurdle to incorporating device data into broader data sets is the addition of new inputs, such as hours of sleep or total steps walked per day. Traditional data points such as total cholesterol or blood pressure readings are less frequent, so there is a smaller amount of data overall. The challenge is finding how to best incorporate it into other data sets to create a more comprehensive picture of health. The future of wearable device data and machine learning We can glimpse into the future of wearable device data and machine learning with Microsoft’s recent patent filing. Their potential product aims to provide wellness recommendations based on biometric data, such as blood pressure and heart rate, pertaining to work events. To do this, Microsoft requests access to applications used by employees. Microsoft then tracks data points such as: Duration of time spent writing emails Number of times a user refreshes their inbox Time spent reading emails Number of corrections made when writing emails Recipient list for emails Number of meetings in a day Tone of language in emails By combining this information with biometric data (from a secondary device such as a Fitbit or Apple Watch) and machine learning, Microsoft could begin to understand what work events trigger a response. For example, suppose an employee received an email from their manager. Microsoft might observe that the employee spent a higher-than-average amount of time reading the email and that the employee’s heart rate was also elevated during this time. Based on these insights, Microsoft could propose recommendations for helping employees manage stress levels, highlighting events that trigger anxiety. Patent filing sample image from Microsoft outlining tips and recommendations to improve employee wellness With a broad user base using both Office and Teams already, Microsoft has a deep understanding of work-related events. As Facebook built their business making sense of our social lives, Microsoft has the potential to optimize our work lives. “Wearables combined machine learning will become the new standard in personalized consumer electronics, rapidly increasing in popularity and scale every year until then. An integrated device of the future will be able to get a baseline of your health and will alert you to any abnormalities present. We already see this happening with the new Apple Watch, and it will be very soon that this technology becomes commonplace.” – Michael Vieck, Fusion Alliance Software Developer Wearable devices will transform healthcare experiences Data is the key to predicting, understanding, and improving health outcomes. IBM Research anticipates that the average person will generate more than 1 million gigabytes of health-related data in their lifetime, equivalent to 300 million books. The sheer volume of data means that machine learning will be vital in making sense of it. Paired together, wearable devices and machine learning have the potential to transform healthcare experiences. Today’s applications and uses are only the beginning. Read more: Top 3 reasons to invest in machine learning for mobile Machine learning and wearable devices of the future Wearing Your Intelligence: How to Apply Artificial Intelligence in Wearables and IoT

What’s next for banks? Reimagining the customer relationship with machine learning

Jun 16, 2021

The pandemic made it clear that traditional banking is a thing of the past. Online banking had already been on the rise, but a 200% jump in new mobile banking registrations in April 2020 established that customers are able and willing to change. As more Americans bank virtually, banks are fighting to meet customer demand? And beyond the challenges set forth by the pandemic, “digital natives” like Rocket Mortgage, Venmo, Stripe, and Robinhood are all vying for business. These technology-forward organizations position themselves differently from traditional financial institutions and are attracting a younger user base for their services. But a traditional bank has advantages over these challengers: Familiarity and history: Your personal relationships and history with customers mean that your bank is often more aware of their history. And customer questions can be answered in person instead of being routed through a call center. Deep and rich data: Historical data can prove invaluable for ML efforts. Customer deposit amounts, payments, and balance information can be used to predict future behavior. Preference for personal banking: Customers, especially those with a high net-worth, may have discomfort with digital channel dependency for wealth management. A new brand might be a risk, and they could feel uncomfortable not having a specific person to call if something goes awry. As we get back to our “new normal,” traditional banks can use the rich data and relationships they have with customers to their advantage. Forward-thinking leaders are reimagining what it looks like to do business, and they’re using machine learning to elevate the customer experience. Discover how you can use machine learning to create engaging and profitable relationships with your customers. Every bank can find value from machine learning Machine learning might sound like a type of data analysis useful to only the largest of organizations, but its concepts can scale to meet the needs of small and mid-sized banks too. When we use the word machine learning (ML), we are referring to machines and systems that can learn from “experience” supplied by data and algorithms. In banking specifically, ML algorithms can be used to identify patterns in data beyond what humans are capable of observing, and these learnings can be applied to new data sets. It is now possible to improve the customer experience using ML. By parsing customer transaction data, ML can identify clues and patterns ahead of time, even before the customer considers taking action. For example, the process of buying a home and obtaining a mortgage might begin with small savings accumulation or an increase in deposit amounts from wages. ML models can assess banking-specific data like credit patterns, risk tolerance, and price sensitivity, and can be coupled with demographic data like age, median income, and distance to branch. The goal of using ML data in this use case is to target prospective customers with offers most relevant to their situation and stay ahead of customer demands. Knowing where to begin — and where to focus efforts Machine learning has such a wide variety of applications, it can be difficult to know where to start. Identifying a use case for customer-focused ML expenditures is a good first step. In general, we have found that you can benefit from starting with a use case with low or medium relative complexity. Examples focused on improving the customer experience include: Predicting service line interest (HELOC, mortgage refinance, etc.) Streamlining loan approval processes Increasing lines of credit Improving fraud alert notifications With so many use cases to choose from, it can be easy to get lost in the planning for each example. Instead, try focusing on one area at a time. Using your strengths, combined with ML concepts, you can deliver an optimal customer experience that digital challengers just can’t match. Need help getting started? Check out our Machine Learning Jumpstart program. Cross-selling across the relationship with machine learning You can leverage machine learning to determine not only which customers would be a good fit for a mortgage loan, but also the other products that customers might need. Thinking about a mortgage loan, a Home Equity Line of Credit (HELOC) might be a good match for a new homeowner. In any case, the message and product can be tailored to meet the customer’s specific needs. Another part of cross-selling is to personalize the offer based on the customer’s history and propensity to buy. Perhaps an interest rate would be meaningful for one type of customer, while a waived application processing fee would entice another. For individuals who identified as being interested in high revenue products, the marketing effort can be even more personalized, like a phone call or an in-person event invitation. Applying machine learning in real life The following illustration is an example of how an internal dashboard might appear to a banker or service representative. For any specific product, each person has a percentage likelihood that they will take action. Individual model scores are shown, along with next steps, such as outreach about an investment account, or mailing a promotion about mortgage rate refinancing. In this example, marketing inputs, like website data, are combined with transaction and deposit information. When a banker or service representative encounters a customer, either in person in on the phone, they can suggest specific next steps, or ask if the customer has questions. Having a dashboard with this information enables banking employees to be empowered to guide the conversation with data in real time. Related Case Study: Machine learning predicts outcomes at Primary Financial How does a financial services firm improve sales targeting to predict its clients’ desires to invest? Machine learning was the answer for PFC. Learn more. FAQs about machine learning and banking Does the machine learning process work fast enough to enable real-time benefits? For all but the most complex scenarios, yes! Normally, ML is fast enough to be integrated into real-time transactions. Does machine learning get in the way of compliance requirements? In general, no. By using existing data that you obtained or using your data in coordination with third-party data, you are not running amiss of privacy and compliance concerns. How do we ensure the use case we pick machine learning is right given that there are so many to choose from? We recommend focusing initially on those low-cost, high-ROI use cases with a low-medium relative complexity. Given additional experience, context, pipelines, and an understanding of how advanced analytics programs operate, then more complex initiatives can be undertaken. Data reliability can be a concern. Using low-quality data is not advised, but it is possible to start projects with small data sets. Engaging with a third party to evaluate your situation is advised in situations like this. Reimagining customer insights & relationships Banks that employ machine learning will have a portfolio of more customers than ever who are positioned for a variety of banking products delivered in a digital, personalized, and meaningful way. Now is the time to act and implement machine learning to meet customers where they are, using the contact methods that they desire and delivering the products and services to best meet their needs. Need help building your use case and plan? Access our Machine Learning Use Case Guide for Banks. Want to dig deeper? Check out our webinar on this topic.

Driving digital transformation forward

Mar 1, 2021

Everyone is talking about digital transformation, but there’s a lot of confusion and misinformation about what that term means. Much more than just a buzzword or doing things “digitally,” digital transformation means reimagining your business and driving it forward in a better way. When done correctly, digital transformation can fundamentally change how you deliver value to your customers. That’s what you need to focus on if you want to thrive in this market. Customers today have high expectations and a lot of options, so carefully considering your customer journey is critical to success. Before you get started, here’s an overview of what you need to know about digital transformation and how to continue to drive it forward: What exactly is digital transformation? The evolution of digital transformation Cloud computing fuels digital transformation More than just technology Keys to successful digital transformation The challenges of digital transformation Where are you in your journey? What exactly is digital transformation? Digital transformation is the integration of technology into all areas of a business, fundamentally changing how you operate and deliver value to customers. It can often be confused with digitization or digitalization, but they are all different things altogether. Digitization is the process of taking information and converting it from a physical format to a digital one, like typing up paper notes and putting them into Microsoft Word or scanning into a PDF. Digitalization, on the other hand, uses these digital files to take the processes already in place and make them more efficient using the new digital format. Digitalization allows you to make your processes faster but doesn’t evolve the process itself. Digital transformation does more by enabling you to interact with your customers in a new way that is constantly evolving to meet both their needs and your business needs. By ideating and implementing better business processes and technologies, you’ll not only create an elevated customer experience that will result in increased profitability, but you’ll also save significant time and money in operating costs. The evolution of digital transformation Digital transformation might be a popular term today, but this was also true in the late 1990s through the mid-2000s. It started with companies computerizing processes 30 years ago, and as the internet was established, websites started to connect companies with their customers. That’s when Fusion Alliance got started, and we’ve been focused on the end goal of developing solutions for clients ever since. Digital processes emerged to support customer interactions, from sending emails to managing online ordering. As digital ambitions grew, companies realized a need for dedicated digital teams to manage social and mobile channels. Connected to customers, suppliers, and other stakeholders, companies realized the need to connect all of these data silos. Seeing the potential in connectivity, organizations focused on digital platforms connecting all systems. Then they started to experiment with new digital ways of doing business, leveraging data more effectively, and creating greater agility. Today, we live in a world where customer expectations have never been greater. They demand personalized experiences in every interaction with a company’s products and services. Because of this, companies must innovate quickly and deliver. In addition, the pandemic has forced IT leaders to adapt yet again, with many adopting cloud software for video collaboration and building apps that enable workers to enter offices governed by social distance practices and contact tracing. Technologies such as cloud computing, the Internet of Things, and Artificial Intelligence all power the innovation that is able to deliver value. An important element of digital transformation is technology. But often, it’s more about doing away with outdated processes and legacy technology than it is about adopting new tech. It should also about enabling innovation. Cloud computing fuels digital transformation Companies are increasingly moving toward a hybrid cloud infrastructure. From SaaS applications and on-premise solutions to a mix of public and private clouds, hybrid cloud strategies help companies find the right balance for their unique infrastructure needs. Over the past year, cloud providers like AWS, Azure, Google, IBM, and Oracle have made investments to support hybrid strategies. OEMs like HPE, Dell (VMware), and Cisco have also increased investments in tools that enable simpler connectivity between on-premises data centers and the cloud. These investments are all centered on meeting the customer where they are in the moment. Hybrid cloud adoption was already underway before 2020’s pandemic, but the sudden disruption sped things up. Being agile and nimble was, and still is, a significant business advantage. More than just technology Although the focus of digital transformation is often on emerging technologies moving the business forward, true transformation has to encompass much more. Business leaders at the top level must be involved in this change and must be willing to invest in and empower your employees, as well as focus on building your culture. As employees begin to see changes occurring around them, they might begin to wonder how it will affect them and their coworkers. They may question their own position within the organization and try to figure out their own best next steps. During any transition, leadership must communicate with employees to help them feel secure in their own positions as well as the direction of the organization. Head off potential issues by making sure your employees understand what digital transformation is, what it’s not, and how they can be a part of it. Additionally, it may be best to communicate how rapidly things can change throughout this process. Being as transparent as possible and preparing your employees for future changes can make all the difference in ensuring your digital transformation is successful. Keys to successful digital transformation Data from McKinsey shows that when companies achieve transformation success, they are more likely to have digital-savvy leaders in place. Less than one-third of all respondents say their organizations have engaged a chief digital officer (CDO) to support their transformations. Those that do are 1.6 times more likely than others to report a successful digital transformation. Companies that have already invested in data and infrastructure to support technology efforts are more adept at again succeeding. The keys to finding success with digital transformation projects vary because no two companies are the same. However, we do have a few recommendations: Define what digital transformation means to your business Create a map of where your business is now, including people, process, data, and technology. Then define where your business needs to be. The gap identified becomes a roadmap. Defining the gaps are in the business is the first key step in your digital transformation process. Identify and involve the right internal stakeholders Leaders and decision-makers might not have the insider knowledge to execute technical challenges. Your stakeholders should be throughout the organization, across departments, and exist at different leadership levels. Align with a partner to shepherd the process You might find your business well-suited to tackle projects outside your normal scope of work, but many companies lack the internal resources to be able to undertake larger projects. Finding an outside partner might be helpful to keep things moving and provide direction with next steps. The challenges of digital transformation While digital transformation is worth it and necessary to the survival of your organization, there are challenges that come along with reimagining any new business process. To make your transformation truly successful, ensure that you have a good understanding of your brand and your customers. Without that, the entire digital transformation strategy you build will be misguided, and you’ll end up back on square one. Budget can also be a big hindrance for a lot of companies looking to begin their digital transformation. Often, there are additional resources and training needed upfront. Although the cost savings and increased profitability are worth it in the end, the initial expense can be intimidating. Additionally, poor data quality can become a huge challenge for organizations as they go down the digital transformation road. Poor data analytics often leads companies to make important decisions based on misguided data. Poor data also prevents companies from being able to use emerging technologies like artificial intelligence and machine learning, since they prove useless when fed with bad data. Sound strategy and good data will ensure you start your digital transformation journey on the right foot. Where are you in your journey? Whether you’re an established business or a startup, the perfect time for digital transformation is now. No matter how old your business is, your digital transformation will be unique to your organization. It is helpful to think ahead to the future — where do you see your company in the next 5 years? Create a roadmap of where you want to be in terms of customer experience, technology, and data insights, and involve those team members in your planning discussions. By involving decision makers across the organization, you can ensure that you are aligning all parts of your business with the end goal. And, if you can, bring in an experienced third-party to help streamline planning, bring in additional insights, and ensure that your projects run smoothly. Digital transformation allows your organization to deliver the right customer experience to the right people and not only remain relevant in your market, but actually build your brand. One thing is consistent: customers today expect a flawless and customized customer experience, and you’ll need digital transformation to deliver it. Your digital transformation partner Customers today expect a customized customer experience, and you’ll need digital transformation to deliver. When you’re ready, we’re here to help you execute. Need help addressing or assessing where are on your organization’s digital transformation journey? Let us know.

Unlock customer credit insights with machine learning

Sep 19, 2020

Executive summary The credit card industry is becoming more complex. Advanced loyalty, targeted offerings, unclear rate conditions, and many other factors can often make it difficult for banks to identify the right customer. Ultimately, the financial services firms that will succeed in this environment will engage the right customers with the right message at the right time. Market leaders will be those who can accurately forecast the revenue and risk for each prospective and existing customer. While the credit card environment has changed, the analytics and modeling techniques have largely remained the same. These models are highly valuable, but do not offer flexibility to evaluate granular and complex customer behaviors incumbent in a financial services firm’s data and other public and private data sets. Machine learning and deep learning (collectively, machine learning) change the paradigm for predictive analytics. In lieu of complex, expensive, and difficult to maintain traditional models, machine learning relies on statistical and artificial intelligence approaches to infer patterns in data, spanning potentially billions of available patterns. These insights, not discoverable with traditional analytics, may empower the financial industry to make higher-value, lower-risk decisions. In this brief article, we discuss three potential opportunities that Fusion expects should add high value to the financial services industry. Advanced analytics for banking Machine learning uncovers patterns in complex data to drive a predictive outcome. This is a natural fit for the banking industry as firms are often working with imperfect information to determine the value of incoming customers. How it works: Traditional models vs. machine learning Credit scorecards represent the basis of most credit card issuance decision making. Whether a firm leverages off-the-shelf models or applies bespoke modeling, Fusion expects the following is representative of a credit scorecard: In the aggregate, these models are highly valuable. But on a per-applicant basis, patterns and details are lost. In machine learning, we can explore detailed and expansive public and private data about segmented applicants for marketing purposes in real time. For example, we can supplement our existing models with data that can be used to segment potential customers such as: Regional FICO trends Educational attainment Social media sentiment analysis Mortgage and equity analysis Much, much more Machine learning can apply artificial neural networks to uncover patterns in your applicants’ history across millions of data points and hundreds of model statistical training generations. When detecting these patterns, machine learning models can uncover risk in approved applicants and value in sub-prime applications. For example, by exploring existing customers, machine learning could potentially reveal that applicants with low FICOs but high educational attainment for a specific city suburb have historically resulted in minimal write-offs. Conversely, a potentially high FICO applicant may have recently moved into a higher-net-worth neighborhood, requiring a high expenditure on a financial institution’s credit lines, resulting in repayment risk. Ultimately, your customer data can tell a far richer story about your customers’ behavior than simple payment history. Machine learning opportunities Financial services firms can gain more insight and capitalize on the benefits of machine learning by applying their marketing dollars towards customers who are more likely to fit within their desired financial portfolio. Lifetime customer value for customer with limited credit data Currently, credit score is determined based on traditional data methods. Traditional data typically means data from a credit bureau, a credit application, or a lender’s own files on an existing customer. One in 10 American consumers has no credit history, according to a 2015 study by the Consumer Financial Protection Bureau (Data Point: Credit Invisibles). The research found that about 26 million American adults have no history with national credit reporting agencies, such as Equifax, Experian and TransUnion. In addition to those so-called credit invisibles, another 19 million have credit reports so limited or out-of-date that they are unscorable. In other words, 45 million American consumers do not have credit scores. Through machine learning models and alternative data (any data that is not directly related to the consumer’s credit behavior), lenders can now directly implement algorithms that assess whether a banking firm should market to the customer segment, thereby assigning customer risk and scores, even to credit invisibles (thin-file or no-file customers). Let’s look at a few sources of alternative data and how useful they are for credit decisions. Telecom/utility/rental data Survey/questionnaire data School transcript data Transaction data – This is typically data on how customers use their credit or debit cards. It can be used to generate a wide range of predictive characteristics Clickstream data – How a customer moves through your website, where they click and how long they take on a page Social network analysis – New technology enables us to map a consumer’s network in two important ways. First, this technology can be used to identify all the files and accounts for a single customer, even if the files have slightly different names or different addresses. This gives you a better understanding of the consumer and their risk. Second, we can identify the individual’s connections with others, such as people in their household. When evaluating a new credit applicant with no or little credit history, the credit ratings of the applicant’s network provide useful information. Whether a bank wants to more efficiently manage current credit customers or take a closer look at the millions of consumers considered unscorable, alternative data sources can provide a 360° view that provides far greater value than traditional credit scoring. Alternate data sets can reveal consumer information that can increase the predictive accuracy of the credit scores of millions of credit prospects. This allows companies to target consumers who may not appear to be desirable because they have been invisible to lenders before, which can lead to a commanding competitive advantage. ON-DEMAND WEBINAR: Learn how to turn data into insights that drive cross-sell revenue Optimizing marketing dollars to target customers Traditional marketing plans for credit card issuers call to onboard as many prime customers that meet the risk profile of the bank. However, new customer acquisition is only one piece of the puzzle. To drive maximum possible profitability, banks can consider not only the volume of customers, but also explore the overall profitability of a customer segment. Once these high-value customer segments are identified, credit card marketers can tailor specific products to these customer segments to deliver high value. Machine learning can assist both in the prediction of total customer value, as well as the clustering of customers based on patterns and behaviors. Identifying high-risk credit card transactions in real time Payments are the most digitalized part of the financial industry, which makes them particularly vulnerable to digital fraudulent activities. The rise of mobile payments and the competition for the best customer experience push banks to reduce the number of verification stages. This leads to lower efficiency of rule-based approaches. The machine learning approach to fraud detection has received a lot of publicity in recent years and shifted industry interest from rule-based fraud detection systems to machine-learning-based solutions. However, there are also understated and hidden events in user behavior that may not be evident but still signal possible fraud. Machine learning allows for creating algorithms that process large datasets with many variables and helps find these hidden correlations between user behavior and the likelihood of fraudulent actions. Another strength of machine learning systems compared to rule-based ones is faster data processing and less manual work. Machine learning can be used in few different areas: Data credibility assessment – Gap analytics help identify missing values in sequences of transactions. Machine learning algorithms can reconcile paper documents and system data, eliminating the human factor. This ensures data credibility by finding gaps in it and verifying personal details via public sources and transactions history. Duplicate transactions identification – Rule-based systems that are used currently constantly fail to distinguish errors or unusual transactions from real fraud. For example, a customer can accidentally push a submission button twice or simply decide to buy twice more goods. The system should differentiate suspicious duplicates from human error. While duplicate testing can be implemented by conventional methods, machine learning approaches will increase accuracy in distinguishing erroneous duplicates from fraud attempts. Identification of account theft, unusual transactions – As the rate of commerce is growing, it’s very important to have a lightning-fast solution to identify fraud. Merchants want results immediately, in microseconds. We can leverage machine learning techniques to achieve that goal with the sort of confidence level needed to approve or decline a transaction. Machine learning can evaluate vast numbers of transactions in real time. It continuously analyzes and processes new data. Moreover, advanced machine learning models, such as neural networks, autonomously update their models to reflect the latest trends, which is much more effective in detecting fraudulent transactions. Summary Bottom line: machine learning can leverage your data to develop patterns and predictions about your customers and applicants. These machine learning models are typically simpler to develop and deploy and may be more efficacious than traditional financial services modeling. These models also enable a more detailed forecast about your customers, allowing you to reduce risk while targeting more profitable customers through their lifetime with your credit card services. Related resources Case study: Machine learning predicts outcomes in financial services Case study: How Donatos uses machine learning to retain customers 5 tips to keep the wealth in your company Fusion Alliance has extensive experience in the financial services industry and serves as a preferred solutions provider for many prominent financial services institutions, including Fortune 500 firms. If you’d like to discuss your organization, let us know.

Align digital analytics with your business strategy

Sep 2, 2020

With increasing customer demands and competition a click away, access to data-driven responses in real time has become a necessity for business users and marketers. Today, the market is full of digital analytics tools for measuring your customer experience across web and mobile applications, customer relationship management (CRM) systems, and point of sales (POS). When used correctly, these digital analytics tools can provide businesses with a wealth of insights into the performance of their digital platforms. To best leverage digital analytics, you will first need to set clear business objectives and define how your organization intends to measure success on your digital platforms. An in-depth look into your current measurement strategy, if one exists, including metrics and key performance indicators (KPIs) will reveal if digital analytics are providing the data and insights necessary to ensure business and customer needs are met. Identifying metrics Organizations often make the mistake of using out-of-the-box metrics like page views, sessions, bounce rates, and session duration as KPIs. These basic metrics are not representative of actual business objectives and can prove useless without the right context. For example, if a marketer wanted to understand the value of a landing page, they would want to look at the number of leads generated by the page or the long-term business impact that customers who came to the site through that page brought. Instead, reports focus on the number of people that saw the page or the bounce rate for the content. While this is helpful information, it doesn’t mean anything if you can’t tie the analysis to ultimate business success. Regardless of industry, website visits and page views do not increase bottom lines, nor should they be used as KPIs. If these are the types of metrics you’re seeing in reports or using in your analysis instead of relying on KPIs like leads, transactions, revenue, or conversions, then it may be time to re-examine your digital measurement strategy. Developing a measurement strategy Successfully integrating digital analytics into business processes requires a clear measurement strategy. A measurement strategy outlines business objectives, what should be tracked on the website or mobile app that will inform these objectives, the types of reporting that will be available, and to whom it will be exposed to once the implementation is complete. Depending on current processes, analytics tools, technology, and available resources, the process of uncovering this information can take several months, but it is a vital step that should not be overlooked or rushed, as the end result is a digital measurement model that provides the framework to align digital analytics with business strategy. The digital measurement model A digital measurement model is a high-level, visual summary that links your core business objectives, such as increasing brand awareness, customer acquisition, or increasing sales, to the digital strategies used to achieve these objectives and their requisite goals. From there, specific KPI and targets will be identified for each digital strategy, helping business and marketing stakeholders understand whether their efforts are trending in the right direction. These elements should be captured in a matrix that can be used to inform the tracking strategy, reporting development, and ultimately gauging the health of your digital practice. Benefits of a measurement strategy Creating a measurement strategy that aligns your business goals with the activities of the digital teams can have a significant impact on how the business operates. With clearly defined objectives and KPIs for measuring digital outcomes, digital teams can focus their efforts on producing measurable value, instead of opting for a shotgun approach that hopes some portion of their efforts will drive outcomes. A well-defined digital measurement strategy encourages an environment of accountability. With KPIs to measure the gap between real-time digital outcomes and targets, executives gain greater visibility into the progress (or lack thereof) being made toward business objectives. It also creates a baseline of expectations, helping digital team members to better prioritize work to produce measurable value. Most importantly, developing a digital measurement strategy gets people talking. Shaping strategy to reflect business objectives encourages collaboration among business operatives and leaders across the board, from marketing analysts to the CMO. Gaining alignment on what matters most helps an organization instill confidence in teams and helps team members gain a better understanding of how their day-to-day work contributes to the overall mission of the company. Final thoughts All too often, digital analytics are completely overlooked within marketing teams. This could be due to lack of expertise around robust measurement implementations, or analytics has been under-prioritized in favor of more tactical activities. Whichever the case, overcoming hurdles to generate actionable business insights from your digital platforms is vital to the health of your digital practice and the needs of your customers. To successfully leverage digital analytics, organizations need to take a deep look into their current measurement strategy and reframe as needed to align their implementations with their established business strategy. Ultimately, having a clearly defined digital measurement strategy paves the way for receiving lasting, meaningful insights from your digital platforms and provides a system of accountability for team members and leadership to unite around.

How to build your digital analytics capabilities

Sep 2, 2020

When digital analytics do not produce useful outcomes to inform business decisions, digital teams often point to reporting and analysis as the culprit. But an in-depth investigation of the measurement strategy and digital analytics implementation often reveals a much different truth: digital marketing teams often don’t fully understand the capabilities of the tools at their disposal. This common issue is born out of inconsistent technical implementations, lack of analytics expertise within the team, or a general misunderstanding of the types of metrics and data that the business should collect. As long as digital teams maintain that reporting and analysis are at the heart of the issue, organizations will not be able to leverage the full feature set available with analytics tools like Google Analytics or Adobe Analytics. This leaves digital leaders questioning whether they should invest in more expensive or specialized tools to get the “right” data that will create new and incremental value. The often surprising reality is that an updated implementation with more focused tracking would suffice to provide digital teams with the valuable data they seek across all digital platforms (e.g., mobile app, CRM and point of sale). With the recent addition of tools like Google Data Studio (a lightweight BI dashboard tool) and Google Optimize (an optimization experimentation tool for the free Google Analytics suite), the vast majority of businesses don’t need a paid analytics solution. You can invest money usually spent on expensive data management tools into analysts or other digital marketing efforts. In most cases, the free versions of tools like Google Analytics and Google Tag Manager are more than sufficient for the needs of an organization, but teams don’t have a true understanding of the tools’ capabilities or don’t implement their tracking in a way that works within the limitations of the free toolsets. 9 questions that can clarify you digital analytics capabilities If you wonder whether your digital analytics toolset is up to par, start by asking some basic questions. The answers will divulge the true extent of your digital analytics capabilities and identify areas for improvement from both technical and expert standpoints. 1. Are we combining data that we already have about our customers with their on-site activities? Many digital teams who use only the out-of-the-box versions of tools like Google Analytics are unaware that the tools come with powerful custom features. Custom dimensions, for example, provide valuable context to information being collected. You might have data, including gender, zip code, customer segment, persona type, etc., about a specific user based on their digital profile. Populate these values into analytics code, along with everything that is tracked using custom dimensions, in order to create meaningful user segments that provide insights as opposed to just metrics. 2. How do specific sections of the website compare to others? Many teams stop their measurement at the page level. This is a natural inclination, considering all interactions are recorded with the single page associated with it. However, there are available features, such as content groups and custom dimensions, that allow you to combine the data from specific pages into predefined site sections or groups. You can then compare these page groupings to each other to understand how they impact conversion and acquisition. 3. How does a specific content type impact conversion? By using the aforementioned features alongside conversions and goal tracking, analysts can point out which content types have the greatest impact on conversion, user drop-off, and other key performance indicators (KPIs) reflecting business objectives. Organizations can then use this information to better allocate resources. For instance, if you learn zero percent of your blog traffic converts on the site, you need to shift resources toward a more conversion-friendly design, engaging content, or traffic sources that are converting. 4. Can your analysts easily set up event tracking or conversion tracking without developers? Teams should use tools like Google Tag Manager or Dynamic Tag Manager to implement their analytics platforms whenever possible. These platforms allow marketers to have control over what is tracked and how that data is expressed in the analytics tool. Many companies do not use these implementation tools. Instead, they still rely on developers to add code, adding significant time to the tagging process and deterring analysts from tracking in some cases. 5. What are our most efficient sources of traffic for conversion? Analysts should be able to relay the traffic sources that generate the most conversions and which sources are the most efficient at doing so. An SEM program may generate the most conversions, while organic search might have a drastically higher conversion rate. In these instances, it’s worth exploring what an investment in the organic search channel could do versus making a similar investment in SEM or social media. 6. How are key segments of customers converting against other key segments on the website? Analysts can create meaningful segments within the analytics tool to understand how different types of customers utilize the website. These segments can be developed using existing customer data or even basic demographic data, like age and gender. Segmentation yields the necessary data to understand how different types of users are being impacted online to help identify areas for improvement. 7. Can we run A/B or multi-variate tests on the website today? A key part of optimizing your digital strategy should be conducting experiments with your website or app content. Many analysts don’t invest in the development of a testing program because of time constraints or simply because they aren’t aware of how easy it can be to run A/B or multivariate tests. Tools like Google Optimize are free and provide a robust feature set that integrates with Google Analytics and Google Tag Manager. 8. What are the primary drop-off points for customers prior to conversion? Analysts should have a clear understanding of what keeps users from converting on the website, no matter the type of site or conversion. With goal funnels or some elbow grease and expertise, analysts can identify where users drop off the site and what might be impacting their experience. With this visibility into customer needs, you can optimize the user experience and generate more conversions. 9. What are the main reasons users come to our site? Often teams don’t take time to understand the specific intentions of why users come to the website because the team members assume they know the reasons. However, their assumptions are often based on their own internal knowledge of the business. For example, you may see a significant increase in traffic to the website, only to find users are going to the careers page or reading a specific piece of content that doesn’t necessarily impact conversion. By understanding keywords, traffic sources, and landing pages that drive users to certain parts of your site, analysts can create user segments based on their intent. Understanding your digital analytics capabilities is the first step Uncovering your digital analytics capabilities can be the difference between a measurement strategy that ensures the continual improvement of online customer experiences or an expensive data tool that produces outcomes non-representative of business objectives. Before deciding to invest in a data management tool, assess whether your digital team has the expertise to leverage your current analytics tools. In most cases, we find the free versions of tools like Google Analytics and Google Tag Manager are sufficient for the needs of an organization. By getting answers to the right questions, you can discover your organization’s hidden capabilities and begin to leverage digital analytics to meet customer and business needs. Want to explore your organization’s digital analytics capabilities or dive deeper? Let us know.

CCPA data privacy enforcement has begun: Use our checklist and plan to make sure you’re compliant

Sep 1, 2020

It’s official. As of July 1, 2020, California began enforcing its California Consumer Protection Act (CCPA), meaning complacency about protecting consumer data is no longer an option in the U.S. CCPA regulates how companies that do business with California residents must handle their data. If you don’t comply, you’ll face some hefty fines. The type of compliance required for security, transparency, data sharing, and data lifecycle management under CCPA is not for the faint of heart. Execution is key, and that’s where our experienced team can help you. In this article, we’ll explain what CCPA means to you, how all this came about, what you should consider, and how you can ensure CCPA compliance. We’ll even give you a checklist that details what you need to have done and a plan for you to move forward. You’ll gain some valuable insights about the E.U.’s General Data Protection Regulation (GDPR), which preceded CCPA. It’s important to understand what transpired there so you’ll know what to expect in the U.S., including the issues companies are facing. If you already have a firm grasp of what CCPA means and how it came about, skip to the “Checklist: 6 Steps to CCPA Compliance” section. That explains what will make you compliant. To get right to the action plan, skip to “How to develop your customized CCPA plan,” which gives you the time-tested formula for how to become compliant. 2020 CCPA update The actual regulations that you must abide by for CCPA were finalized in mid-summer 2020, nearly two years after the act became a law. And now enforcement is upon us, meaning businesses need to quickly ensure they are compliant with the final regulations. Numerous businesses and groups did band together to ask the California attorney general to defer enforcement because of the strain the pandemic has put on resources, but that went nowhere. Already in 2020, 27 data breaches that were reported to the attorney general have moved to litigation surrounding CCPA non-compliance. What are CCPA regulations? Let’s go back to the beginning. The June 2018 passage of the California Consumer Protection Act (CCPA) created new rules about how certain companies must handle California residents’ data. Who is affected? Any organization that conducts business in California, markets to California residents, has customers in California, or collects any data about California residents. Why GDPR and CCPA? A new compliance landscape The digital world has created many new frontiers, and massive data collection is one. It started in an unregulated, anything-goes kind of way, then ramped up as companies began to digitally acquire consumer data to pursue the personalization of messaging. Much of this personal data, which has been (and is being) captured about each of us, is stored, shared, sold, and too often compromised in a preponderance of data breaches. With so many moving parts and widely publicized negative consequences to the sharing of personal data, it’s no wonder that the pendulum has swung toward privacy and regulation. Enter the European Union’s (EU’s) General Data Protection Regulation (GDPR), which went live in May 2018 to create protections for EU citizens and businesses. GDPR enables individuals to have a say in how or if their data will be used, and they must opt in to consent to the selling of their data. Any public or private organizations that do business with European citizens, whether based in the EU or not, are regulated by GDPR. This means many American corporations that will be affected by CCPA may have already undergone a transition to adapt to meet GDPR guidelines. This brings us to CCPA. Sometimes called America’s GDPR, CCPA was passed in June 2018, just one month after GDPR went live. CCPA is a bit different from its European counterpart. CCPA broadens the definition of personal data and includes individuals in a household. The law also defines data “selling” (which has a much broader meaning than you’d think) and allows consumers to opt out of the sale of their personal data though a mandatory “Do not sell my personal information” link on the homepage of the business’ website. But there are other important differences, as well. Let it be noted that nearly half the states have already proposed data privacy legislation, which would create a patchwork of compliance regulations state by state. As a result, it’s widely speculated that some sort of federal legislation will evolve in a year or two. If that does not transpire, we may see a regulatory environment similar to insurance, where consumer data protection laws are regulated state by state. If that occurs, businesses would need compliance teams who understand each state’s regulations. CCPA penalties and fines Penalties imposed by both GDPR and CCPA are based on your company’s size, revenue, and such, so large firms face penalties that are orders of magnitude greater than for medium-sized firms. With GDPR, regulators can assess fines as high as 4% of a company’s annual revenue. Two notable cases of hefty fines after data breaches include one proposed in July 2019 against Marriott that is 2.5% of the company’s global revenue and another against British Airways that is 1.5% of its revenues. Both companies are contesting the fines, but it’s prudent to watch GDPR events unfold and apply those lessons to CCPA. CCPA law at a high level Without going into too much detail, here are some critical CCPA requirements. At or before collecting data, organizations must indicate to consumers the categories of personal information they collect, disclose, or sell. Consumers have the right to see what personal information is being collected, disclosed, or sold and can request the company delete their data or stop selling/disclosing it within 45 days. California residents have a right to take legal action up to certain dollar amounts when non-encrypted or non-redacted personal information is compromised. Organizations can’t give deferential treatment, such as better pricing or services, to consumers who agree to share their personal information over consumers who don’t. Organizations need to know where their consumer data comes from, how it is used, and where it is going, and they must track any selling of personal information. Organizations must ensure that suppliers, vendors, and other third parties with which they share consumer data can also respond to consumer requests to delete or stop selling their data. This includes cloud providers. How does this apply to me? Generate business value through compliance So how does all that apply to your specific organization? Whether or not you’re at the starting point of your data compliance journey, CCPA presents you with an opportunity. Even if your company falls under rigorous regulatory compliance requirements, meaning you have already put in place the appropriate controls, technology, and data management capabilities to demonstrate in an audit that you can effectively comply, all those measures are defensive. If you don’t have things under control already, CCPA should put a fire under you to line up your efforts in compliance, data management, and data governance in an offensive way. At some point, every company will fall under such legislation, so it’s time to better develop your data environment and to create higher quality, well-managed data that is fit for purpose and can contribute to revenue generating efforts. You can use that data to help influence your marketing strategy and to build digital products/services that your customers might want to consume, which, in turn, you can monetize. Your data environment and higher quality data become business assets that can generate returns. Make a pivotal decision at the very beginning If you do have California customers and must comply with CCPA, you may have already made a strategic scoping decision: should you design a system that complies with CCPA for only Californians or should you build a data management environment that provides these protections and services to all your customers, regardless of whether they come from California? Hopefully, you weighed numerous factors. What if you implemented just for California residents, and one by one, other states eventually pass similar laws? You’d have to incur the cost of implementing compliance over and over again. And would your non-California customers take issue that you are providing strong privacy protections to some consumers’ data, but not to theirs? On the other hand, what if you went full throttle and decided to manage all your customer data in a CCPA-complaint way? You could still recognize that there are California customers, but this might be the time to implement across your data landscape. If you went that route, you would incur certain costs to manage your data the way CCPA requires, but you could leverage that investment to better understand and serve all customers. If you haven’t already, you need to set up an operational, cost-effective service for managing data for all your customers. As previously mentioned, you’d be subject to audit, and the compliance requirements are rigorous. Which brings you back to the question of whether you chose to take on the cost of managing all your customer data the same or make plans to segregate it. Some companies’ legal teams advised them not to move forward with an all-in approach so quickly. Also consider how CCPA may have changed your business model. Does your company actually need to collect personally identifiable information on your customers? Why are you collecting it? What do you plan to do with the data you collect? Use it internally? Sell it to generate revenue? It’s smart to examine what your data is being used for and if it is necessary. For companies whose business model is to heavily monetize their data, such as Facebook, these regulations may be seen as a threat to their existing business strategy. Those organizations may be understandably resistant to putting too much of their data under this kind of management at this point. Bottom line, make sure your company has thoroughly examined all the legal, regulatory, and business factors in play to come to the right decision for your business. Our Checklist: 6 steps to CCPA compliance Whatever choice you made, you needed to take a data governance-based approach to compliance. Here is a proven checklist or roadmap of six basic steps you should have completed by now to meet the requirements of CCPA and sustain compliance over time. Take a look to ensure you’ve got it covered. Update privacy notices and policies. Update data management strategy, data inventories, and business processes. Implement protocols to ensure compliance with consumer rights. Make security updates. Update third-party processor agreements. Provide training. As you embark on your journey, be mindful of the unofficial best practices that have emerged across industries to define what you need to do to effectively manage customers across industries. If personally identifiable information (PII) is a focus, your data governance and stewardship planning should include a PII roadmap to ensure that compliance will occur. Also note that Step 6 is critical, since you don’t want to throw all your time and effort out the window if your employees and suppliers don’t actually execute what you have carefully and painstakingly implemented. Organizational change management can be the crucial difference between compliance and financial penalties. How to develop your customized CCPA plan Even though you understand CCPA and GDPR and you know your end goal, you still may be concerned, despite having the checklist in hand and process underway. Fear not. While the checklist explained what will make you compliant, you now need to use a tried-and-true plan that tells you how to get there. This plan has been used successfully to help organizations achieve GDPA compliance and applies to CCPA compliance, as well. You can employ and customize it as you set up for CCPA compliance. Given the complexities of regulatory compliance, the majority of companies choose to work with someone experienced in the full lifecycle of data. If you chose this route but aren’t sure if it was the right way to go, work with a company that can help you understand how CCPA affects your business and who will walk alongside of you in evaluating your approach, performing or evaluating your implementation, and successfully managing and advising you to ensure you can sustain compliance. If you need help in this area, let’s chat. Discover and analyze. For companies early in their data journey who don’t know where to begin, a discovery process helps you understand the data and regulatory landscape and then plan for an approach to address what your compliance activities might be. Ask yourself how familiar you are with the new regulations. What must your business do in order to comply? What kind of timeline are you facing? Where do you stand on data governance and data management? Do you understand the quality and lineage of your data (where it is generated, changed, and stored)? Do you have the capability to change your data (in the case of legacy systems that captured the data years ago)? Assess and recommend. Perform an audit of your privacy and data management-related program capabilities and assess it against the key requirements of GDPR and CCPA. This will identify where you have gaps and exposure in order to create recommendations to move forward. Strategize and plan. Your success depends on bringing all relevant business, technology, and compliance stakeholders on board with a comprehensive plan. This includes existing digital, analytics, or data governance teams, to create a cross-functional program capability. Design and build. Focus on the following seven workstreams to enable sustainable compliance: 1) privacy policy management, 2) data lifecycle management, 3) individual rights processing, 4) privacy by design, 5) information security, 6) data processor accountability, and 7) training and awareness. Operate and monitor. Establish an ongoing data governance and privacy compliance monitoring program to promote continued accountability. Our customized plan at work: Manufacturing case study An international manufacturing company used this plan to become GDPR compliant. What they learned through the process was that it’s never too early to start preparing. There are many considerations, including how data flows, email processes, notifications of data collection, changes to the user experience, and so much more. It takes a multi-functional team to get ready. In their case, compliance was achieved through a cookie notification, an opt-in email campaign, an opt-in form for data collection, and data is no longer being stored on the website. The process and solution are different for each company, but based on their experience, this client would say don’t wait, prepare now. Summing it up CCPA is just another development in a privacy revolution that will ultimately help protect the data generated about each of us. While different generations have different perceptions of privacy, the passing of this regulation signals that the floodgates have been opened in the U.S. for more action. Larger firms who already have internal risk and compliance teams might have the horsepower to handle the decisions and strategy required, but, as with GDPR compliance, they may still turn to a partner to help implement the recommendations. The data privacy landscape in the U.S. has changed with the enforcement of CCPA, and the costs of a breach will now extend to penalties for not following regulations. Take action and ensure that you are adhering to the law and protecting the consumer data in your organization. Wondering where to start? Let’s chat.

SEO measurement: Beyond the rankings

Aug 13, 2020

One of the most difficult aspects of search engine optimization is measuring the success of a campaign. Historically, SEO providers give their clients a list of “valuable” keywords and the position of the client’s website for each keyword. Unfortunately, this kind of ranking data provides a comparatively narrow view of search engine activity because it only measures expected results. In order to get maximum insight into an SEO campaign, key performance indicators (KPIs) need to be established to capture the complete picture. Keeping score Search engines include more than 300 data points when they are calculating the score or rank of a page. Measuring how your site, page, or campaign will perform based on a specific request is a very challenging problem. A good starting point is to begin recording and measuring a variety of on-site and offsite indicators in order to develop a custom SEO solution based on the competitive landscape. You’ll need to test and develop metrics that provide relevant insight into your site’s ability to rank versus competing sites currently ranking for targeted traffic. On-site measurements On-site measurement begins by scoring the content of your website based on quantity, quality, and structure: The quantity is the number of pages of unique content and the rate that new pages are added to the website The quality score is more subjective, but relates to the relevance of the content to questions being asked by the target demographic The structure score looks at items like URLs and HTML tags to determine the ease in which the content could be indexed All of these measures are then combined into a content score, which is compared to top-ranking competitors. The content scoring also identifies gaps in subject matter and opportunities for new topics. User experience is scored by measuring bounce rate, pages per visit, and time on your site. This data is evaluated on a device-type basis in order to make sure that all visitors have a similar experience. It is also important to understand your site’s performance by checking PageSpeed and Yslow scores. In addition, the actual response time of each page and supporting assets determine a speed score. Offsite measurements Search engines rely extensively on outside factors to determine website relevance. Measuring several leading metrics to identify potential opportunities can expand the online reach of your site. Begin by evaluating incoming links to the website (follow and no-follow) to determine the number and quality of external websites linking to your site. During the backlink analysis, links that should be disavowed because of potential penalties related to Google Panda must be identified for future action. Finally, check your social media activity to determine what content is being shared and/or discussed and the overall reach of your site on social media platforms. Business factors In order to measure the return on investment (ROI) for any online marketing activity, it is important to have well-defined goals for your website and visitors. Start by developing a value for each type of conversion. On an e-commerce site, it is very easy to define the value because the visitor has put items into a shopping cart and either completed the purchase or abandoned the cart. If your website supports a large brand or collects leads, the definition of value is more difficult. However, value always exists and it is important to agree on a value in order to report on the business success from web activities. Once the business goals are defined, you’ll need a method for segmenting different online marketing activities so each channel can be measured independently. SEO traffic can be defined as a website visitor who arrives from organic search results and didn’t include a brand name in the search terms. However, with Google’s update to secure search, the availability of keyword data has been reduced significantly and the exclusion of brand name keywords has become more difficult. In order to compensate for the lack of keyword data, you should now consider an SEO visitor as any visitor who enters your site from organic search with a landing page that is not the home page (and possibly a few other pages). This is when the fun begins. You can now measure visitor traffic from SEO and compare it to other marketing activities. The ability to show a financial return on SEO is possibly the most important factor to business stakeholders and executives. Measuring the success of your campaigns with KPIs Using the metrics outlined above can provide a clear picture of where SEO effort is being applied and how it impacts your business financially. The reporting also identifies successful strategies that you can expand on based on your KPIs. It is important to remember that measurement and execution of an SEO campaign never ends. Search engines are testing changes to their ranking algorithms on a daily basis. New content, links, and social media content activity are also constantly in flux, and without continuous monitoring even successful SEO campaigns may fail if they are discontinued.

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