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Articles about Digital Analytics
GA4 transition meltdowns, and how to deal with them
Sep 29, 2022
For many, the approach of the Google Analytics 4 takeover brings nothing but dread. And we get it. Although GA4’s new insights and methodology can be an incredible boon to businesses that want to improve their user targeting, it’s a huge adjustment, and there’s no simple solution. Below, we cover the most commonly voiced pain points of a GA4 transition and offer ideas on how to adapt, so you can keep your team from burning their laptops and heading for the hills. I can’t export my data from UA into GA4. Transitioning from Universal Analytics to Google Analytics 4 isn’t a simple, one-to-one migration. Why? Because GA4 operates on an entirely new user-based model. UA uses a session-based model that collects data from actions taken by the user within a particular session. GA4, on the other hand, relies on an events-based approach: meaning, instead of looking at goals per session, you’re looking at events per user. This allows us to track user behavior across multiple devices and platforms. While this means better insights into the target audience, it also means migration isn’t as easy as just importing your UA data into GA4. You’ll need to audit the metrics you’re currently using and rethink them in light of GA4’s new data tracking methods. Get tips for GA4 migration without the migraine >> My views are no longer available. If you’ve spent any time in Google Analytics 4, this loss may be the most jarring discovery you’ve made. Raw, test, or official — all the most commonly-used views are gone. Before you have a panic attack, breathe into a paper bag and read on. GA4 doesn’t use views because the interface is set up for you to create custom data filters. For those of us that aren’t data experts, this can seem daunting at first, but it gives the opportunity for you to track the information that’s going to be most valuable to your company. Here are three things you should know about data filters: The filters you create are permanent. This means you should be certain of what you’re tracking before you set up a filter. Use “test mode” to preview your filtered data before you set up the filter for good. You can set up to 10 filters. You likely won’t have to worry about maxing out your filters, but again, do some internal testing to prioritize what filters you need so you’re not taking up real estate with a data view that’s not going to provide long-term insights. You can filter out internal IP addresses. No more worrying about throwing off metrics every time you test a lead form! You can set data filters to exclude your team’s activity so you’re only seeing what’s happening from external visitors. Learn more about building custom reports in GA4 >> Bounce rate is changing. Bounce rate is one of the elite metrics that many look to as a gauge for page effectiveness. But the truth is, bounce rate may not be as helpful as we’ve believed it to be. The GA4 transition doesn’t quite do away with bounce rate, but you’ll find the metric in a different form. Instead of a 1-for-1 replacement of UA’s bounce rate, GA4 provides a different way to measure page effectiveness, in the form of “engaged sessions.” So, instead of tracking who comes to your site and doesn’t interact, it measures those that come to your site and do interact, and monitors which actions they take. Thus, you can calculate bounce rate by looking at it as the inverse of engagement rate. However, bounce rate is much less insightful than engagement rate, so although the drastic change may be awkward at first, we’re confident that most users will warm up to this method and even find it more beneficial in the long run. The GA4 user interface is difficult to use. For users who’ve spent the past 15 years using UA’s out-of-the-box reporting, GA4 can be an upsetting experience, to say the least. UA’s pre-defined reports provide valuable data, but GA4 has a serious leg-up over its elder counterpart: custom reporting. You can recreate most of UA’s reporting by creating custom reports with GA4’s Exploration tool. What this means is, you’re not getting less functionality — you’re getting more ways to view your data, and you can tailor them to your business and desired outcomes. Will that make the interface any less weird or uncomfortable? Sadly, no. But with some training and a little practice, users will acclimate to the change and begin to see the benefits of custom reporting and the far-more-accurate data it provides. Adapting to the wide world of GA4 Although GA4 will take some getting used to, the new model gives a simultaneously holistic and granular view of user behavior that offers better insight into your target audience — and how to reach them. If you’re ready to take the plunge into GA4 but still apprehensive, our Google Analytics 4 Survival Guide can give you the tools and tips you need to start out on the right foot. Or, if you want a real person to talk you through the process and help you get set up, talk to a Fusion Alliance analytics expert today. Get the GA4 Survival Guide >>
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
Cage Match: Google Analytics 4 vs Universal Analytics | Fusion Alliance
Aug 24, 2022
Marketers at mid-market companies are going through the stages of grief over Google Analytics 4. Some are still in denial (“Whatever, we have Universal Analytics until July 2023, and they will probably extend the deadline.”) and others have moved on to anger (“Google Analytics sucks!”) If you’re actively wrestling with the transition, you’re probably squarely in that cage match stage. We get it. Our team has been helping businesses with GA4 implementations since it first rolled out, and making the switch isn’t easy. Along the way, we’ve found four key matchups that might help you avoid getting KO’d by your Google Analytics 4 transition. Google Analytics 4 vs Universal Analytics Google Analytics 4 Universal Analytics Round 1: Tracking Users Events Sessions Goals Round 2: Data Engagement Bounce rate Round 3: Interface Low clickability Drill down analysis through setting up secondary dimensions High clickability Drill down analysis through ad hoc clicking in results Round 4: Reports Explorations Embedded reports Round 1: Tracking In Universal Analytics, we track metrics by sessions tied to behavioral goals. Here’s one common scenario: Patricia visits your website home page on her phone one day and bounces. A few days later, she scrolls an article from your site on her desktop and bounces. The following week, she reads a case study on her iPad while waiting in the carpool pickup line and fills out a contact form. At this point, UA documents three sessions, and one goal achieved. But is that the best way to document what actually happened here? Google thought not, and pivoted. Google Analytics 4 tracks metrics according to users and events. Using the same scenario, GA4 documents that one user took four actions on your website: home page, article, case study, form fill. You see the progression, and the impact your site has, at a more granular and yet more holistic level. With GA4’s user-based data model, you see what users do when they interface with your brand — regardless of the device they use, location they come from, or platform they visit. Instead of goals per session, you’re now able to see events per user. Round 1 winner: GA4 Round 2: Data When July 2023 rolls around, a lot of people are going to be caught by surprise when they can’t just upload their Universal Analytics data directly into Google Analytics 4. Next, they will try to export their data from UA to GA4 and that will…also not be possible. Sorry, folks, it simply will not work. The data models are different, and Google has not laid down a path to make a simple data port happen. How can you counter that punch? Your best move is to get started in GA4 as soon as possible, so you can begin to collect new data and understand how those metrics will influence your overall reporting. And the sooner you start collecting GA4 data, the sooner you’ll be able to use it in your year-over-year metrics. Because the data points aren’t the same, your old UA data won’t be of much use in year-over-year comparisons. Learn more about how switching to GA4 impacts YOY reporting >> The shift that impacts almost everyone changing over from UA to GA4 is the shift in bounce rate. Long used as a benchmark for user engagement, UA’s bounce rate metric didn’t make it to GA4 in quite the same format. Instead, GA4 measures engagement in more absolute terms. GA4’s new “engagement” metric takes the guesswork out of bounce rate. Instead of saying “people are leaving immediately, probably because the content wasn’t relevant or the load speed lagged,” GA4 tells you how many people stay on the site long enough to take another action. Before, a low bounce rate could mean someone read your entire blog archive going back to 2007 OR that they had 39 tabs open for a week. Now, you’ll know if a site visitor was idle or active — giving a clearer picture of whether or not they were truly engaged. The bottom line is that GA4 offers a little more accurate view of engagement, but your UA to GA4 bounce rate numbers won’t match up. Your stakeholders are still going to ask about it for a while, and that’s going to be annoying to handle in reporting meetings. Round 2 winner: draw Round 3: Interface If you’re a long-time user of Universal Analytics, you’re probably used to being able to dig into your data as the mood seizes you. Wondering which URLs drove the most traffic last month? Click, click, answer. Want to find out how you got so many hits from Denmark yesterday? Click, click, answer. Puzzled as to why your website suddenly ranks for terms like “can horses drink beer” (or maybe that’s just us)? You get it. The UA interface’s high degree of clickability that enables ad hoc analysis. Not so with Google Analytics 4. When you log into your dashboard, you’ll see a similar view, but all of your clickability is gone. No digging into traffic cohorts. No in-the-moment drill-downs. But all is not lost. If you click the plus sign on a measurement in GA4, you can set up secondary dimensions. So if you want to check which referral URLs are driving traffic, you can build that query in. Maybe you want to set up secondary dimensions to narrow down data here and dig in deeper there. With some time and patience, you can eventually get to a view similar to something you might have seen in UA. But your “click, click, answer” days are over. Sorry about that. Round 3 winner: UA Round 4: Reports We know people who have used Universal Analytics their entire working lives and never built a custom report. If you open your UA account, you’ll find dozens of out-of-the-box reports to meet your needs. While most mid-market to enterprise businesses do have some custom UA reports, they certainly aren’t required to use the tool. Now open your Google Analytics 4 account. Where UA shows you report after report to help you review your website health, traffic, campaigns, networks, landing pages, and so forth, GA4 shows you an overview screen and…two reports. Two. If that prospect leaves you feeling like you got the wind knocked out of you, you’re not alone. Once you get over the inability to migrate your data from UA to GA4, you’ll need to plan to migrate your reports. And that’s going to be a bigger problem for a lot of medium- to large-sized businesses. Take a deep breath and start by cataloging every report you use in your UA account. Map out exactly what you measure, and how the results are displayed. Note how you use the results, and which business units rely on the outputs for operational and strategic decisions. Then, start building custom reports in GA4 to answer those needs. Learn more about building reports in GA4 >> No, it’s not going to be a one-for-one replication. Yes, it’s going to be time-consuming and frustrating. Depending on your timeframe, budget, and bandwidth, you might need to look into partnering with a third party to get this work done right and in time for the switch. On the bright side, though, this process may alert you to reporting you’ve used out of habit that wasn’t exactly right. Building new reports offers you a fresh start, and a chance to really understand what your analytics needs are, and how data can help drive better decisions across the business. Round 4 winner: draw Spoiler alert: GA4 wins Ultimately, the Google Analytics 4 vs Universal Analytics match isn’t a fair fight. Come July 2023, Universal Analytics is out for the count. But what you choose to do in the meantime could mean the difference between emerging victorious or limping out of the ring with broken ribs (proverbially speaking, we hope). Your approach to the GA4 transition could make all the difference. Increase your odds of GA4 success by checking out our (cage-free) resource: 6 steps to a successful Google Analytics 4 migration.
There's no crying in baseball. Or in GA4.
Jul 19, 2022
Rub some dirt on it, marketers. When a behemoth like Google rolls out a wholesale change like GA4 while the new product is still in beta, it’s understandable if you miss some fly balls or pull a muscle. New reports. New features. The vague sense that you should be grateful for the opportunity while also feeling dumb that you don’t exactly get the nuances. If all this leaves you feeling like you’re getting yelled at by Tom Hanks in 1993’s A League of Their Own, we understand. Universal Analytics won’t be available for much longer, and GA4 is a moving target. But you don’t have to beat yourself up about it. We’ve got a team digging into the transition to help you stay in the game. Get smart. Not sure which play comes next? Need to catch some GA4 basics? Here are a few resources to get you started: [Deep Dive] Build a solid game plan: 6 steps to making your GA4 transition easier [Offer] Get your GA4 migration back on track [30-Min Webinar] Maximize your GA4 opportunities
Reimagining the skillset supply chain
Jun 2, 2022
The pace of change and unpredictable circumstances of the past couple of years have led many companies to rethink their just-in-time approaches to resourcing tangible goods and materials. But why stop there? To scale and adapt fast, companies also need a new approach to how they resource skillsets. One of our clients, PRECISIONxtract, did just that. By taking a just-in-time approach to their shifting skillset needs, the company was able to scale up fast — and minimize risk — in a changing business environment. A right-fit-first approach PRECISIONxtract’s transformative healthcare market access solutions offer patients and providers unprecedented connection to the right medication and resources in clinical settings. To bring that vision to life, PRECISION could have found a series of single-skill vendors or taken the time to recruit and onboard new employees. Instead, they looked for a cross-functional partner that would be a seamless fit with their company culture and that had the right mix of scalable skills. They found that fit with Fusion Alliance. Fusion quickly became an integral part of PRECISION’s team, assembling a group of more than 20 strategy, data, and technology experts to deliver responsive support for a growing set of initiatives. Boosting surge capacity across disciplines Knowing that their flagship product, Access Genius, needed design and functionality upgrades, PRECISION called on Fusion to assess and modernize the application without disrupting the existing business. To avoid downtime and increase speed to market, our team used an Agile process and a model-driven design, in which models from the source code informed modernization efforts. Streamlining the overall architecture not only saved development time, but also made Access Genius easier to deploy to PRECISION’s clients. And, to make the product easier to maintain and cheaper to run, we applied containerization through a microservices model and moved Access Genius to a distributed cloud hosting framework. Our solution provided real-time customer insights that were delivered across a variety of digital channels, in lieu of a people-driven process. This helped take Access Genius: From a complex, cumbersome, legacy monolith into a lightning-fast, distributed, cost-effective, cloud-native solution From a user-driven, database-centric format to a distributed API-based framework, enabling immediate data updates for important cost and coverage changes From a time-intensive customer engagement portal to an intuitive, streamlined, automated process Equipped with a modern, stable, extensible platform, PRECISION was free to explore opportunities for more radical innovation. Disrupting the market with frictionless access to timely data Although Access Genius successfully broke down barriers with data, the solution’s interface required users to navigate a complex dashboard with manual clicks and drop-downs. For pharma teams with limited time to connect doctors to information, seconds count. Working with PRECISION’s product team, Fusion technology experts analyzed the friction point of manual navigation and explored ways to make Access Genius more seamless for the user. Drawing on deep expertise deploying cutting-edge technologies into highly regulated spaces, Fusion suggested exploring a shift away from a traditional web-based interface to an AI-enabled voice functionality that would connect users to the most relevant data and messaging right in the flow of conversation. Changing the way pharma enablement tools go to market At the same time, other Fusion consultants were hard at work rethinking the way PRECISION’s products reached, empowered, and retained customers. We brought in a range of specialists to bring new strategies to life, including: Instructional designers and training developers created an interactive training platform to equip pharma sales reps with greater confidence in provider interactions by deepening their understanding of the Access Genius tool. RESULT: Access Genius IQ, a new training tool that helps PRECISION customers see faster ROI for their Access Genius investment Brand experts, visual designers, content strategists, and web developers elevated visual brand elements and created websites, editorial content, and outreach campaigns. RESULT: New website architecture, design, and content; long-form lead generation content; prospect cultivation email marketing Digital marketing strategists, creative designers, and ad teams implemented innovative ad campaigns in rapid succession as PRECISION had more time to develop and roll out new products. RESULT: LinkedIn ad campaigns generating 3X leads, including 100 qualified leads in the first 90 days Read more about the success of Fusion’s marketing partnership with PRECISION >> Reimagining the skillset supply chain Partnering with Fusion gives PRECISION access to a huge team of experienced consultants with a wide range of skillsets — allowing the company to surge and scale as their business needs and market realities shift. With Fusion bringing in the right people at just the right time, PRECISION saves valuable time and resources, enabling them to be more innovative, more agile, and more impactful for their customers, healthcare providers, and patients. Ready to explore how Fusion skillsets can help your team succeed? Our ongoing work with PRECISIONxtract is just one example of how we help companies build momentum for a digital-first world. We bring big-picture thinkers, technology-minded creatives, data scientists, and technical experts to work alongside our clients, providing a force-multiplying effect that leads to scalable, future-focused solutions for the most complex challenges. Ready to get started? Let’s talk.
CDP vs DMP: How to choose the right solution for your business
Apr 28, 2022
In the crowded field of martech solutions, finding the right tools can be challenging. Businesses not only need to identify the right customer data strategy to fit their goals, but then source or upgrade the right software and systems to bring that strategy to life. In this quick comparison, we’ll define two commonly misunderstood tools, help you sort through the CDP vs DMP conundrum, and explore how they might fit into your technology stack. What is a Customer Data Platform (CDP) and what does it do? A CDP is not technically a platform; it’s a software solution that collects and streamlines customer data primarily from first-party sources to improve marketing operations. Because they are designed in support of long-term customer engagement, CDPs store data longer and can provide a single source of truth for customer records. Learn more about how CDPs fit into a customer data strategy >> What is a Data Management Platform (DMP) and what does it do? A DMP is a data warehouse that collects, segments, analyzes, and stores primarily third-party customer data for use in advertising campaigns. This adtech component plays a critical role in targeting and retargeting for short-term leads and customer conversions, but is not set up to support historical analysis. Learn how third-party cookie deprecation is impacting adtech >> How CDP and DMP solutions can work together A CDP and DMP can work together in a modern martech stack. A DMP can be one source of data for a CDP, and the CDP can also share information back to the DMP. When approached strategically, the question isn’t CDP vs DMP, but how the two systems can support each other. With the right processes in place, a DMP can help bring in new prospects, a CDP can help brands connect and engage, and retargeting and customer cultivation can continue in a seamless loop. More resources for your martech stack Also wondering about the CDP vs CRM debate? We’ve got five factors to consider >> Wondering how to choose a CDP? Check out our approach >> Need to get a big picture view? Get the Ultimate Guide to Creating a 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 >>
Make your Google Analytics 4 migration easier: 6 steps to a smooth GA4 transition
Apr 20, 2022
Although Google plans to phase out its Universal Analytics (UA) tool in favor of Google Analytics 4 (GA4) in July 2023, if you’re like most companies, making the switch to GA4 isn’t high on your priority list. That might be a mistake. Because UA and GA4 operate from very different frameworks, your GA4 migration isn’t going to be a quick and easy one-for-one shift. Companies will find significant differences in how data is tracked and measured, which will impact existing tags, metrics, KPIs, and reports. These differences take time and planning to work through successfully. Making the switch to GA4 might be something you can handle with your in-house data, IT, and analytics subject matter experts. But depending on the complexity of your current UA setup and the role analytics plays in your digital strategy, you might need a more strategic plan to get the GA4 transition right. How to switch: setting a realistic roadmap for your GA4 migration 1. Audit your current Google Analytics usage and metrics 2. Stand up your new GA4 property 3. Map your previous metrics to new GA4 options 4. Create, customize, and integrate dashboards 5. Train and explain to get your team on board with the change 6. Iteratively improve Step 1: Audit your current Google Analytics usage and metrics Before you start your GA4 transition, you need a clear view of what you’re collecting now, and where and how you’re using your UA outputs today. First, list the metrics you track in UA. Survey business units with access to find out how your organization uses those metrics, particularly where metrics influence KPIs. Your audit should also include the other systems and tools that connect to your UA account, such as Google Ads, Google Search Console, Looker Studio (formerly Google Data Studio), and the like. Be sure to map functionalities, customizations, or enhancements your organization has developed for your UA instance over time. You may not be able to reproduce them exactly in GA4, but you’ll need to understand the use cases so you can replicate the results. The audit may also be a good time to evaluate the processes your company has in place around tracking, measurement, and analytics in general, as well as the dashboards you use. Any dashboard that relies on UA will shift with GA4, so it makes sense to take the time up-front to identify any other improvements or efficiencies that might advance your business goals. Step 2: Stand up your new GA4 property Google provides detailed instructions for setting up a new GA4 profile and connecting it to your website. Once your profile is established, begin connecting the other systems and tools you identified in Step 1 of this article. You’ll also need to convert your UA goal metrics to GA4. In some cases, this is as simple as figuring out different naming and labeling conventions – for example, if you tracked Signups in UA, you’ll set up the same thing as Conversions in GA4 – while in others you might need to create something new in GA4 or add custom functionality. Because GA4 is still evolving, new developments and features are added regularly. Look for ways to replicate the customizations you rely on in UA and be aware that you might need third-party help to identify or develop workarounds. Depending on the complexity of your current analytics, customizing your GA4 account could take considerable time, so be sure to leave enough of an overlap with UA in your roadmap and timeline. Step 3: Map your previous metrics to new GA4 options Allow 3-6 weeks for your new GA4 instance to collect and measure data before you compare it to your UA results. Due to the significant differences between UA and GA4, be prepared to see differences in the type of data collected and the ways key factors are measured. You may not be able to compare the results in a 1:1 fashion, but mapping out the differences can help you to refine your customizations and ensure that you have time after your GA4 migration to mitigate or explain discrepancies. Your team is probably used to analyzing UA data to determine the reasons behind significant changes: launching a campaign, a spike in bot traffic, differences in browser and device use, and so forth. The same factors impact GA4 information. By comparing your metrics over a few months, you’ll be better prepared to understand those impacts when Google sunsets UA. Step 4: Create, customize, and integrate dashboards With a robust internal report builder comparable to more sophisticated third-party tools, GA4 offers more options for business reporting, automated reports, and dashboard creation. While the enhanced functionality will be helpful, you’ll still need to analyze and customize every dashboard and report you rely on, whether basic internal reports or external tools like Looker Studio, to map to GA4 data tracking. Remember that the UA to GA4 transition is not a 1:1 switch. Not all UA measurements are available in GA4 and others can be extracted but require updating and customizing your reports.rs can be extracted but require updating and customizing your reports. KEY NOTE: Since GA4 is still in beta, be sure to keep an eye on the updates Google continues to make to features and functionality. As recently as July 2022, Google announced conversions, bounce rates, and UTM parameter tracking — none of which were originally part of the beta platform — will be a part of the new update although not in exactly the same UA form. At this stage, comparing reports between UA and GA4 for a longer period of time will be helpful. You’ll see measurement differences as GA4 becomes your new source of truth but look for indications that trends or analyses aren’t mapping between your profiles. That could be an indication that you need to do more in-depth work on data collection, goals, and reporting. Because you will not be able to access your historical UA data after December 2023, you must build in time for this comparison sooner rather than later. Once Google officially sunsets UA, you won’t be able to see your UA reports in the dashboard or access your historical UA data via the API. Build in time to do your analysis before the end of 2023 – and consider downloading your UA history to keep as a reference. Don’t rush this step. Being able to trust your data is critical, and while you will see differences in numbers during your GA4 migration, it’s important to ensure that you replicate business reporting requirements so that your metrics yield reliable and actionable insights. Step 5: Train and explain to get your team on board with the change Throughout the process of migrating to GA4, keep your end-users informed. Effective change management requires more than a heads-up about a dashboard change. Your training and communication plan should include: An overview of the timing and requirements associated with the move from Universal Analytics to Google Analytics 4. A tutorial on new vocabulary, especially as it relates to changed metrics. An explanation of how different metrics can be used to find the insights teams rely on. A tour of dashboard changes, with step-by-step instructions as needed. A presentation of the parallel reporting analysis you conducted during Step 4, so that your team understands trade-offs, replacements, what the new numbers mean, and how to conduct their analysis differently in the new GA4 environment. Step 6: Iteratively improve As GA4 continues to evolve, be prepared to continue optimizing your data collection, reporting, and dashboards to be sure you’re collecting accurate information and getting the most out of your data. And since GA4 is still a beta platform, it's important to stay on top of the new add-ons, features, and changes so you can adjust your measurement strategies accordingly. We are monitoring the news and sharing updates as they come. Follow Fusion Alliance on social media and/or subscribe to our Fuse: Marketing newsletter for more. Feeling stuck with GA4? We can help. Wherever you are in your switch to GA4, you can always get back on track. Our team helps organizations with end-to-end GA4 migrations, but we also step in for more tightly scoped problem-solving like custom integrations, dashboard creation, and training. Let us know what you’re dealing with, and we can set up a free discovery call to help you work through it.
Data executives empowered: The importance of the data community and elevating the CDO
Jul 22, 2021
Almost 20 years ago, Capital One recognized the need for one person to oversee their data security, quality, and privacy, and the role of the Chief Data Officer was born. Now reports show that 68% of organizations have a CDO (Harvard Business Review, 2020). And while the role has become more common and has significantly evolved, many data executives are still struggling to get a seat at the table or bring data to the forefront of their organization. In fact, in a recent survey, only 28% of respondents agreed that the role was successful and established. Company leaders agree that there needs to be a single point of accountability to manage the various dimensions of data inside and outside of the enterprise, including the quality and availability of that data. But now we are at a crossroads — what is the best way to align the work that the CDO does with the strategy of the business as a whole? The reality is that CDOs often struggle to find the internal and external support and resources needed to educate others to align with the organization’s goals. Implementing enterprise data governance, data architecture, data asset development, data science, and advanced analytics capabilities — such as machine learning and video analytics — at scale, is not an easy task. To be successful, data executives need support, resources, and communities focused on the elevation of data. We are proud to continue to help these communities come to life for the benefit of our colleagues and clients, establishing local resources here in the Midwest with global scale, reach, and impact. Read on as Mark Johnson, our Executive Leader for Data Management and Analytics, provides insight on the current state of data and the CDO, and provides details on multiple opportunities for data leaders of different levels to get more involved in the data community. Q: How has the role of data changed/evolved for organizations? The reality is that information is everything. This global pandemic proved that to many organizations. For some, it showed that their digital network was ready, and they were aptly prepared to take on COVID. For others, it has forced them to recognize their own immaturity with data and analytics. On its own, managing data is not exciting — the information just sort of exists. To give data value, you have to put it to use. And so, I think we are going to see the Chief Data Officer and/or Chief Data Analytics Officer really find their own in the coming years. It’s time for their seat at the table. The C-suite is now asking questions that can only be answered with data, and now they truly understand both the value and consequences of the data game. Q: What do you think are the biggest challenges facing CDOs/data leaders today? I think that the biggest challenge for data executives today is the acquisition of talent that is seasoned and experienced where you need them to be for your organization. Higher education hasn’t necessarily kept up with the data world, and often times it takes additional training to reach the right levels. The reality is that right now the talent is manufactured in the real world. Data executives have to be connected and equipped to mentor, train, and keep the right people. Q: You’ve mentioned that data leaders need to connect with each other. What value can people expect from these data communities? I think there is tremendous value. As we are seeing the power of data evolve in organizations, and the role of data leaders evolve as well, I think coming together to collaborate and share elevates the leader, the organization, and the view of data as a whole. In these communities, it gives people a safe space to talk about how they are doing, what they are doing, what their biggest challenges are, and what solutions are working for them. These communities have truly become both a learning laboratory and an accelerator for data. Q: As a big proponent of connecting data leaders, you have been involved in creating different opportunities for people to get together. What groups/events would you recommend, and how can people get involved? I personally have been involved with the MIT Chief Data Officer and Information Quality Symposium (MIT CDOIQ), which is such a great opportunity to start with for connection. It has developed into additional opportunities for data leaders at all levels to get involved and create the kind of community we need to truly elevate the value of data. Organizations like the CDO Magazine, the creation of CDO roundtables across the nation, and the International Society of Chief Data Officers (isCDO) all evolved from connecting data leaders and identifying common challenges. MIT CDOIQ: The International MIT Chief Data Officer and Information Quality Symposium (MIT CDOIQ) is one of the key events for sharing and exchanging cutting-edge ideas and creating a space for discussion between data executives across industries. While resolving data issues at the Department of Defense, the symposium founder, Dr. Wang, recognized the need to bring data people together. Now in its 15th year, MIT CDOIQ is a premier event designed to advance knowledge, accelerate the adoption of the role of the Chief Data Officer, and change how data is leveraged in organizations across industries and geographies. Fusion has been a sponsor of this symposium for seven years now, and we are so excited to see how the event has grown. Designed for the CDO or top data executive in your organization, this is a space to really connect with other top industry leaders. CDO Roundtables Fusion has always been focused on building community and connecting people. And when one of our clients, a Fortune 500 retailer, mentioned wanting to talk with other data leaders from similar corporations, we realized that there was a big gap here — there was no space that existed where data leaders could informally come together, without sales pitches and vendor influence, and simply talk. That’s how the CDO roundtables were born — a place that allows data leaders to get to know each other, collaborate, accelerate knowledge growth, and problem solve. We just started two years ago in Cincinnati, but now, now we’ve expanded to multiple markets including Indianapolis, Columbus, Cleveland, Chicago, and Miami. These groups are designed for your CDO/CDAO and truly create an environment for unfiltered peer-to-peer discussion that helps solves data leadership challenges across industries. If you’re interested in joining one of these roundtables or starting one in your market, email me or message me on LinkedIn. I’m here and ready to get these roundtables started with executives in as many communities as I can. The more communities we have, the more data leaders and organizations we can serve. International Society of Chief Data Officers (isCDO) Launched out of the MIT CDOIQ symposium, the isCDO is a vendor-neutral organization designed to promote data leadership. I am excited to be a founding member of this organization, along with our Vice President of Strategy, David Levine. Our ultimate goal is to create a space that serves as a peer-advisory resource and enables enterprises to truly realize the value of data-driven decision making. With multiple membership options available, isCDO is the perfect opportunity for data leaders looking to connect with their peers and gain a competitive advantage by focusing on high-quality data and analytics. CDO Magazine I am really proud to be a founder of the CDO magazine, as it really is a resource for all business leaders, not just the CDO. We designed the magazine to be a resource for C-suite leaders — to educate and inform on the value proposition, strategies, and best practices that optimize long-term business value from investments in enterprise data management and analytics capabilities. Check out the publication here. And if you’re interested in contributing content or being interviewed, let me know at mjohnson@fusionalliance.com. Closing: The role of the CDO is integral to organizations, but it’s still evolving. Now more than ever, it is important that data leaders come together to collaborate and problem-solve. Fusion is excited to be a part of each of these initiatives, and we are committed to being an agent of change in the communities we serve and beyond. By connecting global thought leaders we believe that organizations will realize the value of data to power their digital transformation. If you’re interested in joining any of these data communities or just have questions, feel free to reach out to Mark via email or on LinkedIn.
Data and analytics in the modern world: How to maximize ROI
Jul 7, 2021
Return on investment (ROI) is top of mind for everyone. With so many competing priorities, how you spend your time and money, and what you get for it, matters more than ever. The focus used to completely be on the level of your investment. But the paradigm is shifting because of the data capabilities that now exist. In this article, we’ll explore how the definition of ROI has changed because of modern technology and approaches, where your data ROI comes from, and how to accelerate it. Setting goals for your data analytics efforts Data without analytics is ultimately an investment without return. Most organizations sit on troves of data, but can’t do anything with it. But analytics is a progression. Each of the levels on the data analytics maturity model represent questions you can begin to answer. To go from nothing to cognitive takes a lot — your investment increases substantially. Descriptive: What happened? Diagnostic: Why did it happen? Predictive: What will happen? Prescriptive: How can it happen? Cognitive: What can be suggested? With each step up the model, you add more information and complexity. For example, the descriptive level of maturity can be answered with a look at history. As you progress, you will need more information and stronger data relationships to better understand the “why.” Your data quality and integrity are also important. When you get to the cognitive step, you’re expanding outside your universe of data, and the contextual element of what you’re doing gets broader. For this step, consider Microsoft’s Cortana or IBM’s Watson. But in the modern data world, there doesn’t have to be a huge upfront investment. Shifting your focus from a return on investment to a return on insights can drastically impact how you invest in your data and your results. Calculating the ROI of data and analytics projects Ultimately, ROI is realized from leveraging effective data management to enable access to: more and better data maximizing visualizations advanced analytics actionable insights for outcomes For data management, that means: Improved quality and completeness Confidence and trust Accountability through governance Improved stewardship Advancing culture change to help stakeholders understand the importance and value proposition By improving your data management, the insights from your data become better and more actionable, including: Access to more data and the inclusion of new sources Faster and easier access to data Greater integration of disparate data Easier standup and use of analytics technology In years past, insights from data analytics might have been limited to data scientists or experts in the field. But now, with analytics tools and technologies, data insights are useful to — and actionable for — people across the entire organization. The larger the investment in time and money, the more emphasis on ROI, how quickly it can be realized, and the amount of trailing value. Data leaders have to work with their organizations to understand what the best strategy is – whether that be a smaller investment with a slower return or a big investment that allows you to realize your ROI sooner. It is critical to evaluate your organization’s needs, expectations, and goals before making decisions on strategic data management. Understanding the classic data ecosystem In a classic data ecosystem, the setup might look something like this. In a classic data ecosystem, it requires deep analysis to understand sources and the definition of data, and a considerable amount of time and effort to reach the gold standard you need for your data to be utilized for BI and analytics. Investments are required on all layers. There is no real way to invest in one aspect of your data and analytics and still find value. There is also significant effort required to ensure that as you introduce new systems, you don’t break legacy systems and processes already in place. Quite often, work must be done upfront to ensure that changes (even upgrades) will not cause disruption. In addition, significant “time-to-market” factors need to be considered with classic data ecosystems. Often, the slow delivery of data and features forces businesses to make incremental changes without undertaking any kind of larger project. Doing so might be helpful at first, but can cause issues later. With a slow delivery of data, many organizations using a classic data ecosystem find that they are unable to keep up with the pace of business today. Classic data ecosystems are often built to meet reporting needs, not analytical needs and the analytics piece is a one-off project. The deployment and incorporation of analytical models into production in a classic setup requires a considerable amount of time and customization. Building a modern data ecosystem In this more modern data ecosystem, there is a more layered approach. Now it is much easier to gather, ingest, and integrate the data, and bridge gaps between systems, along with including new concepts like data lakes that can include data layers at the bronze, silver, and gold levels. You don’t have to invest fully in all of the layers, you can invest where you need to. You still have BI & analytics capabilities, but you have more of an application integration framework that serves additional needs. And then there is the trust of the data. This setup allows for more flexibility and customization for all parts of the organization. Read more: How to build your data analytics capabilities The value of incremental change Your investment doesn’t have to be an all-or-nothing proposition. You can incrementally build out components and capabilities and can make data available for exploration without deep upfront analysis that often slows everything down. Additionally, you can control the degree of your investment in a significant way. Without having to push data through all the layers to make it useful and the use of flexible architecture, you don’t have to make a significant investment and change to make it worth it. You can also leverage external tools in the interim. Service and subscription-based features allow for fast initiation, and exploratory efforts can be stood up and torn down easily and quickly. New technologies and design/development paradigms enable faster adoption overall. And now, more user groups are able to access data and analytics, create more use cases, and make business decisions on the insights. Ultimately, it is time to shift your thinking on ROI and leverage modern data technology and tools and focus on the return on insights, intelligence, and innovation. For more information on data, analytics, and assigning ROI, Fusion’s Vice President of Data, Saj Patel, recently spoke at the CDO Summit. His presentation details how to accelerate ROI and gain buy-in across your organization. Watch the recording here and connect with us if you have any specific questions Learn more about Strategic Data Management here.
Using data to drive digital transformation
Jul 6, 2021
While every organization’s journey to digital transformation looks different, one thing remains the same — the importance of data. Tackling your data systems and processes is vital to fully transform. However, the reality is that most organizations are overwhelmed with data about their customers. But these troves of information are completely useless unless companies know that the data they have is accurate and how to analyze it to make the right business decisions. In today’s world, organizations have been forced to pivot and have realized the value data can bring to drive insight and empower their decision-making. However, many organizations have also recognized their data immaturity. So how do you move forward? The role of data in digital transformation Data can be your organization’s biggest asset, but only if it is used correctly. And things have changed. A lot of organizations have completed the first steps in their digital transformation, but now they are stuck — they aren’t getting the results they expected. Why? They haven’t truly leveraged their data. According to Forrester, “Firms make fewer than 50% of their decisions on quantitative information as opposed to gut feelings, experiences, or opinions.” The same survey also showed that while 85% of those respondents wanted to improve their use of data insights, 91% found it challenging to do so. So, now that you’ve got the data, how can you make it more valuable? Data strategy is key to your digital transformation With so many systems and devices connected, the right information and reporting is critical. But first, you have to make sure you have the right technology in place. Utilizing big data Although you might feel inundated with the amount of data you have coming in, using big data analytics can bring significant value to your digital transformation. Through big data analytics, you can get to a granular level and create an unprecedented customer experience. With information about what customers buy, when they buy it, how often they buy it, etc., you can meet their future needs. It enables both digitization and automation to improve efficiency and business processes. Optimizing your legacy systems Legacy systems are critical to your everyday business, but can be slow to change. Why fix what’s not necessarily broken? But just because systems are functioning correctly doesn’t mean they’re functioning at the level you need them to — a level that is conducive to achieving your data and digital transformation goals. This doesn’t have to mean an entire overhaul. You’ve likely invested a lot into your legacy systems. One key to a good data strategy is understanding how to leverage your legacy systems to make them a part of (instead of a roadblock to) your digital transformation. With the enormous scale of data so closely tied to applications, coding and deployment can often make this stage of your digital transformation feel overwhelming. Sometimes DevOps tooling and processes are incompatible with these systems. Therefore, they are unable to benefit from Agile techniques, continuous integration, and delivery tooling. But it doesn’t have to feel impossible — you just need the right plan and the right technology. Focusing on your data quality Even with the right plan and technology, you have to have the right data. Bad data can have huge consequences for an organization and can lead to business decisions made on inaccurate analytics. Ultimately, good data needs to meet five criteria: accuracy, relevancy, completeness, timeliness, and consistency. With these criteria in place, you will be in the right position to use your data to achieve your digital transformation goals. Implementing a data strategy with digital transformation in mind So how do you implement your data strategy? You should start by tackling your data engineering and data analytics. The more you can trust your data, the more possibilities you have. By solving your data quality problem, you can achieve trust in your data analytics. And then, the more data you have on your customers, the more effective you can make your customer experience. But, this all requires a comprehensive data strategy that allows your quality data to be compiled and analyzed so you can use it to create actionable insights. The biggest tools to help here — AI and machine learning. The benefits of a data-driven digital transformation The benefits of investing in your data are clear, including increased speed to market, faster incremental returns, extended capabilities, and easier access and integration of data. Discover more about the different ways you can invest in your data and improve and accelerate ROI for your organization. Ultimately, your goal is to elevate how you deliver value to your customers. Digital transformation is the key to understanding your customers better and providing a personalized customer experience for them. Leveraging your data can make all the difference between you and your competitors. And we’re here to help. Learn more about how some of our clients have benefited from investing in their data and digital transformation.
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.
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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