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<p><em>Are you considering integrating <a data-mce-href="https://pendo.io" href="https://pendo.io" target="_blank" rel="noopener">Pendo</a> into your organization, or are you in the midst of these challenges? Leveraging Pendo's powerful product analytics can transform the way you understand and engage with user data to make informed decisions. For product leaders and managers, mastering product analytics is imperative to achieve success in today's competitive market. Interested in exploring this together? Let's chat!</em></p><blockquote><p><em>We present a Product Analytics Maturity Model that can help lay out a roadmap of evolution for PM's with increasing sophistication in insights generated and presented.&nbsp;</em></p></blockquote><h3>Introduction</h3><p>This is the 9th post in our series on Product Analytics Integration Best Practices. We've covered a lot of content in the last several weeks, written for Product Managers to help them build up a sophisticated set of analytics reporting that helps them operate as data-informed as possible in their product leadership. Today's post focuses on how PM's should "product manage" their analytics efforts. We present a Product Analytics Maturity Model that can help lay out a roadmap of evolution for PM's with increasing sophistication in insights generated and presented.&nbsp;</p><p>This maturity model came about from a Modern Product engagement that involved Pendo integration and product analytics practice development with a rollout to a large client with many agile product teams. The ideas presented here can be applied to any analytics platform.</p><figure id="figure-2abyzxo0jm0v643ft" data-full-width=""><img id="2abyzxo0jm0v643ft" src="https://cdn.durable.co/blocks/1124zq9OikQKzXmnYeAYf5SPP8rWBkhW9CcRZCnmwooSvTXfphCwsbcw6XeFlgoo.png" alt="product analytics maturity model graphic" title="product analytics maturity model graphic" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption=" " data-positionx="undefined" data-positiony="undefined" data-fullwidth="true" data-mce-src="https://cdn.durable.co/blocks/1124zq9OikQKzXmnYeAYf5SPP8rWBkhW9CcRZCnmwooSvTXfphCwsbcw6XeFlgoo.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"><figcaption class="body-normal text-gray-500 mt-2"></figcaption></figure><h3>In the First 30 Days<br></h3><p>Once data begins streaming into an analytics platform like Pendo, a Product Manager should explore basic, easily actionable analytics efforts. Pendo is designed for Product teams, offering excellent features to start promptly. Within the first month of collecting analytics data, consider setting up the following:</p><p><strong>Simple User Segmentation:</strong> Your first step in the analytics maturity model is to establish simple user segmentation. Begin by categorizing users based on fundamental criteria such as demographic information, geographical location, or user type. This will provide a foundational understanding of who your users are and how they interact with your product. This will also be a chance early to identify potential additional user metadata that might be needed down the road for more sophisticated segmentation (which will almost certainly be an expectation). Examples of "simple" user segmentation might include new users versus users with accounts &gt; 3 months, or users on mobile versus users on desktop, etc.&nbsp;</p><p><strong>Simple User Environment-based Analytics:</strong> Next, analyze what your user environments look like, including which browsers, operating systems, and platforms from which your users access your product. Understanding the technical environment in which your product operates can help identify possible usability issues and optimize performance. In Pendo, some of the basic dashboard widgets can be used for this type of insight, such as the <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" target="_blank" rel="noopener">Browser Usage</a> or <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" target="_blank" rel="noopener">Operating System Usage</a> widgets.&nbsp;</p><p><strong>Basic Engagement Behaviors:</strong> In the first 30 days, a PM should also focus on analyzing basic user engagement behaviors in their product. PM's should be able to analyze page views and feature usage to get a snapshot of user activity, to answer questions such as, "How many users visit this page?" or, "How much is this feature used on average per week?", etc. This data helps you identify which features are most popular and how users are navigating through your product. In Pendo consider using the <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" target="_blank" rel="noopener">Feature Use by Visitor</a> dashboard widget, or the <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" target="_blank" rel="noopener">Feature Adoption</a> widget.</p><h3>In the First 90 Days<br></h3><p>As more data is captured and basic reporting is disseminated across the organization, the Product Manager probably will have a good sense of where to focus efforts and iterations with product analytics next. In general, here are some recommendations of where the PM should focus over the first 90 days of their product analytics journey.</p><p><strong>More Advanced Segmentation:</strong> You should be able to define specific segments of users that matter most to analyze for your product. This probably will get into advanced segmentation efforts that rely on specific user attributes or metadata that you can lean on when defining your segments. Perhaps you might have specific personas of users that leadership might want to understand more fully how they actually behave within your product (typical examples include at-risk or lapsed members, or power users). It is our recommendation that within the first 3 months of product analytics efforts, you will be able to segment your data &amp; reporting to the user groups that strategically matter most.</p><p><strong>Adoption / Conversion Analytics:</strong> As a PM, understanding whether new users' behaviors within your product experience are expected or not is critical to driving up active user engagement and retention. Similarly, when your team launches new features, it's imperative to know whether users are interacting fully with them or not. So getting up and running with core adoption or conversion based analytics is very important to understand whether your core value proposition for users is hitting home or not. So within the first 90 days of a product analytics journey, we strongly recommend that PM's focus in on those adoption &amp; conversion analytics. In Pendo, consider using the <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360031863292-Funnels" href="https://support.pendo.io/hc/en-us/articles/360031863292-Funnels" target="_blank" rel="noopener">Funnel Report/Widget</a> for conversion based analyses, or the <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" target="_blank" rel="noopener">Feature Adoption</a> widget.</p><p><strong>Retention Analysis:</strong> &nbsp;Once you get advanced user segmentation set up, one of the more critical insights you can report out on is specific user segments' retention over time. PM's who have a solid grasp on retention of various core user segments will be able to more surgically identify opportunities to improve the product experience to ensure these core segments stick around longer within their product. Pendo has a <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" target="_blank" rel="noopener">Retention</a> widget and a <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" target="_blank" rel="noopener">Retention by Usage</a> widget that should be considered.</p><p><strong>Utilization:</strong> Utilization is the measure of how frequently and effectively users employ key functionalities within your product. There is a nuanced difference between simple engagement analytics, which reports out on usage of specific features, to a set of analytics insights that reports out on how well or how effectively the product experience is driving engagement across a user base fully. So as a PM, once you get basic reporting of core feature engagement in place, your next maturation is beginning to set up reporting that assesses utilization of those core features.&nbsp;</p><h3>Within the First Year<br></h3><p>In the first year of your Product Analytics journey, expect a familiar pattern: the Product Manager or team will develop increasingly advanced analytics reports, which will be well-received by stakeholders and leadership. However, this will lead to more questions that require further analysis and possibly technical assistance from engineering. This "bringing along" of the organization is a natural progression that a Product Manager should anticipate and be prepared to drive. The Product Analytics Maturity Model we're discussing can help prepare the PM for this journey. Here are some recommendations the PM should consider when planning product analytics efforts in the first year.</p><p><strong>Cohort Tracking/Analysis:</strong> There will likely be scenarios in PM'ing your product in which you'll want to track cohorts of users over time and analyze various aspects of their behavior to determine whether changes to your product have any longer-lasting impacts that are either desired or not. Cohort analysis relies on the ability within your product analytics capabilities to not just segment users to specific groupings that matter for cohort analysis, but the ability to keep track of them over time against ongoing measures. This is another maturation of your user segmentation capabilities to consider ensuring you have the means to do this within your first year.&nbsp;</p><p><strong>Sentiment Analysis:</strong> Hopefully you have access to qualitative user data as a PM (typically collected in the form of user research via UX Design in partnership with Product Management, or other sources such as customer support feedback tickets, etc). one of the challenges with these types of primary, qualitative data is that it's hard to link that feedback to specific analytics data within your product. Our recommendation for Product Managers within their first year of their product analytics journey is to find ways to directly tie user sentiment or qualitative feedback, to the specific (actual) activities and behaviors these users exhibit within your product experience. This is harder than it seems! One of the nice advantages that Pendo provides is capabilities that allow Product teams to capture user feedback and sentiment within the product experience, in the form of <a data-mce-href="https://support.pendo.io/hc/en-us/articles/5092218184475-Overview-of-Pendo-Feedback" href="https://support.pendo.io/hc/en-us/articles/5092218184475-Overview-of-Pendo-Feedback" target="_blank" rel="noopener">Feedback</a> and <a data-mce-href="https://support.pendo.io/hc/en-us/categories/360001404191-Guides" href="https://support.pendo.io/hc/en-us/categories/360001404191-Guides" target="_blank" rel="noopener">Guides</a>. Because it's all part of the Pendo platform, the users who engage with these feedback mechanisms are also tracked via Pendo, so it's relatively straightforward to correlate qualitative feedback from users with their actual behaviors.&nbsp;</p><p><strong>ROI Evaluation: </strong>&nbsp;A challenge PMs might face in their product organizations is linking financial data with product data to report on ROI. This typically begins with collaboration between finance and product departments to define investments tied to product roadmaps or revenue from products or features. If these teams can align on mapping data sets for ROI reporting (a significant challenge), the task of building the report can start. Meanwhile, everyone hopes for automated ROI reporting one day. This challenge is common across organizations, so you're not alone if you feel this way. While not easy, we recommend Product Managers pursue this "holy grail" of reporting. Ideally, within the first year, the PM will have provided valuable insights into their products. Equally important is how the PM fosters discussions with these teams and leadership to align on collaborative efforts for true ROI-based reporting. Ultimately, if an organization can see reporting (ideally automated) that links product investments to financial or user engagement benefits, everyone benefits!</p><p><strong>Basic Predictive Analytics: </strong>Depending on the product you manage, and your organization's needs for forecasting or predicting future behaviors, etc, this may or may not be an interesting or relevant recommendation. Within the first year of capturing rich product analytics data, teams should consider whether they would have a need for establishing any basic predictive analytics based systems that can help inform various organizational departments. Some examples of predictive analytics that teams could consider:</p><ul><li>Predicting revenue impacts based on membership conversion flow optimizations, leveraging historical data that shows conversion #'s to membership revenue.</li><li>Predicting new feature impacts to user engagement based on historical data that shows engagement of similar features.&nbsp;<br>&nbsp;</li></ul><h3>Beyond the First Year<br></h3><p>As a Product Manager aiming to advance product analytics in the second year and beyond, we hope your foundational analytics insights and reporting have been developed and embraced by your organization. If not, we strongly advise prioritizing their establishment. For those fortunate enough to be further along in maturing their product analytics journey, here are some recommendations for the next steps.</p><p><strong>Efficacy Measurement: </strong>Achieving an advanced level of reporting involves integrating product analytics, financial data, and the efficiency of your product development teams to truly measure your organization's efficacy. If you've already set up automated ROI-based reporting within the first year of your product analytics journey, the next logical step is to incorporate data on product development efficiency. Efficiency should be defined within your organization, often considering metrics based on quality and delivery predictability—key indicators of highly efficient teams. By aligning this efficiency data with your product analytics and financials, you can develop remarkably sophisticated efficacy-based reporting.</p><p><strong>Advanced Predictive Analytics:</strong> To have infrastructure that supports predictive analytics reporting is a luxury that should not be taken for granted by anyone within your organization. If you do have these infrastructural capabilities, then our strong recommendation as a Product Manager is to continue to build on your predictive analytics capabilities with more and more advanced insights that can truly drive your organization forward.&nbsp;</p><blockquote><h3>Every product manager should approach their analytics with the same diligence as they do their products. In other words, we encourage all Product Managers to "product manage" their analytics capabilities.</h3></blockquote><h3>Conclusion<br></h3><p>Every product manager should approach their analytics with the same diligence as they do their products. In other words, we encourage all Product Managers to "product manage" their analytics capabilities. Start with the highest-need, low-hanging fruit insights—those obvious, easiest metrics to track and report on. Then, gradually add layers of sophistication as your organization becomes more data-savvy.</p><p>By following this Product Analytics Maturity Model, you can transform your product analytics into a powerful tool for your organization that ties many core functional groups together with leadership.&nbsp;</p><p><br></p>

Putting it all Together: A Product Analytics Maturity Model

<p><em>Are you considering integrating <a data-mce-href="https://pendo.io" href="https://pendo.io" target="_blank" rel="noopener">Pendo</a> into your organization, or are you in the midst of these challenges? Leveraging Pendo's powerful product analytics can transform the way you understand and engage with user data to make informed decisions. For product leaders and managers, mastering product analytics is imperative to achieve success in today's competitive market. Interested in exploring this together? Let's chat!</em></p><blockquote><h3>Product Analytics data is only as good as the reporting that presents the meaning behind it to the right audiences.&nbsp;</h3></blockquote><h3>Introduction<br></h3><p>Product Analytics data is only as good as the reporting that presents the meaning behind it to the right audiences. Product Managers need to carefully design their reporting to get the most out of the data, in order to make the right data-informed decisions for their products.&nbsp;</p><p>This is the eighth post in our series on Product Analytics Integration Best Practices using Pendo. Today, we'll explore Pendo's reporting capabilities, sharing what we've learned from a two-year engagement with a large client. Pendo provides a pretty good "toolbox" of capabilities that allow PM's to dive into reporting quickly. We'll focus on the strengths and limitations of Pendo's dashboard widgets, Data Explorer reports, and more in this post.&nbsp;</p><h3>Pendo Dashboards and Widgets</h3><p>Pendo's <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032203651-Dashboards" href="https://support.pendo.io/hc/en-us/articles/360032203651-Dashboards" target="_blank" rel="noopener">dashboards</a> and <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" target="_blank" rel="noopener">widgets</a> are the starting point for any Product Manager looking to visualize data quickly. Every user has a default dashboard automatically created that is tied to their account, and Pendo has built up a sizable library of analytics visualization widgets. Once your data is flowing into Pendo, you can begin exploring these various widgets to visualize your data. The interface to add widgets is fairly straightforward and many of the core analytics visualizations that product teams care about are there.&nbsp;</p><p>However, some widgets have limitations, especially when it comes to segmentation and time grain capabilities. For example, while you can segment data to some extent, the time-granularity might not always meet your needs (daily, weekly, monthly, etc). This is something to be mindful of as you set up your dashboards; not all of the widgets you would like to add/visualize might match exactly what you're hoping to be able to report on, in the exact way you'd like. Sometimes this is ok, but sometimes alternative approaches must be explored (see later sections of this post).&nbsp;</p><p>Another potential issue to be aware of relates to Dashboard-level filtering. Pendo dashboards allow users to set filters or segments at the top of the dashboard. One would expect that all widgets that make up that dashboard would then be segmented to this selection, but not all widgets can be segmented in this way. When building dashboards where users might leverage this global filtering/segmentation capability, make sure to explicitly call out any widgets that do not adhere to this in your dashboard.&nbsp;</p><p>Despite these limitations, we found several widgets that were very valuable during our client engagement, especially in the beginning phases of introducing product analytics to various teams who had not previously been exposed to these types of data:</p><ul><li><strong>Weekly Visitor/Accounts:</strong> A nice snapshot of weekly visitors and/or accounts.</li><li><strong>Unique Visitors/Hour Heatmap:</strong> Highlights peak usage times, essential for understanding user behavior throughout the day.</li><li><strong>Stickiness:</strong> Measures how often users return, a key metric for assessing product value.<br></li><li><strong>Feature Adoption:</strong> Tracks how your product's core features are being used, helping you gauge their success.</li></ul><blockquote><p>One of the great things about Pendo is their continual investments to improve their reporting capabilities.</p></blockquote><p>One of the great things about Pendo is their continual investments to improve their reporting capabilities. A couple of pretty exciting recent enhancements that we would call out:&nbsp;</p><ol><li>Pendo recently added their <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360031863292-Funnels" href="https://support.pendo.io/hc/en-us/articles/360031863292-Funnels" target="_blank" rel="noopener">funnel reporting capability</a> as a dashboard widget. Previously, the funnel report was a separate standing report, which made it harder to share out to business stakeholders. This was a great addition to the dashboard / widget capabilities on Pendo's part, because funnel based reporting is typically a core aspect of a Product Manager's analysis of their product's performance.</li><li>Pendo is readying the full general release of <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032203651-Dashboards#h_01HQXY5F9YV9TWQ83W5VTR7VDE" href="https://support.pendo.io/hc/en-us/articles/360032203651-Dashboards#h_01HQXY5F9YV9TWQ83W5VTR7VDE" target="_blank" rel="noopener">Dashboard Templates</a>, which gives Product Managers a head start in building dashboards that are fairly standard or typical for product analytics reporting. They are starting with a Product Overview template and a New Feature Launch template. This is pretty exciting, as we look back on our engagement, much of our focused activities were to build out representative dashboards for the various product teams for which we were rolling out Pendo!</li></ol><h3>Pendo's Data Explorer Report<br></h3><p>If you're looking for flexibility and versatility in reporting, Pendo's <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360033574351-Data-Explorer" href="https://support.pendo.io/hc/en-us/articles/360033574351-Data-Explorer" target="_blank" rel="noopener">Data Explorer</a> is where you'll spend most of your time. This report type gives Pendo users the most flexibility in identifying what data to group and visualize, and it gives a pretty robust filtering/segmentation capability tied to the metadata and event properties being captured. Once we had established solid out-of-box reporting using Pendo's basic Dashboard and Widget capabilities with our client's various teams, any new, more sophisticated reporting we desired typically ended up being a Data Explorer report.&nbsp;</p><p>One valuable ability within Data Explorer reporting is the ability to define/select event groupings and compare them against other groupings. We found ourselves using Data Explorer quite often to define specific segments of users and creating comparison groups around core feature engagement activities that we could analyze. For example, how might relatively new users use the platform navigation differently compared to long-term, power users? Or, how might users in a certain region use a set of core content-engagement features differently than users in a different region? Data Explorer gave us the ability to easily run comparisons of core metrics against different segments of users.</p><p>While Data Explorer was overall a robust, valuable tool that we used quite a bit, there are limitations that we encountered that should be mentioned here. While it's great to be able to easily set up comparison groups, Data Explorer only allows you to compare two event groups. So, in the case where you need to compare more than two groups, you'll need to set up multiple Data Explorer reports, or find an alternative means to analyze these various groupings of data.&nbsp;</p><p>An additional limitation of the Data Explorer report capability ties to metadata filtering. There is a limit to using this filtering capability to data sets that have less than 10,000 events. This can be challenging for events that occur frequently, especially across a large, heavily-used platform. When you hit this limit, it can restrict your ability to get detailed insights to the proper filtered metadata. In our previous engagement, we had to develop workarounds to address this limitation. As the next section covers, sometimes the limitations in Pendo's reporting requires Product teams to build reporting outside of Pendo's UI.</p><h3><br>Leveraging Data Exports and Excel for Advanced Reporting<br></h3><p>As mentioned in previous sections of this blog post, sometimes Pendo's reporting capabilities will not meet the needs of your Product team, whether it's based on certain limitations or simply that you have much more sophisticated reporting needs. In this case, the great news is that it's relatively easy to extract your product analytics data from Pendo in various ways that allow you to build external reporting that is exactly what you might need. The simplest way to do this that we'll cover today has to do with exporting certain report types from Pendo and pulling those data sets into Excel.</p><p>Pendo's <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032202791-Visitor-reports" href="https://support.pendo.io/hc/en-us/articles/360032202791-Visitor-reports" target="_blank" rel="noopener">Visitor</a> and/or <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032202731-Account-reports" href="https://support.pendo.io/hc/en-us/articles/360032202731-Account-reports" target="_blank" rel="noopener">Account</a> reports can be defined in the Pendo UI and then exported to csv. Similarly, Pendo's Data Explorer reports can also be exported to csv. So as a Product Manager building reporting suites out for yourself, your team, and/or your stakeholders and leadership, it's best to build as much of your product analytics reporting out directly in Pendo as is possible, but then export any specific data that can be leveraged for customized Excel-based reporting.</p><p>For example, tying back to some of the limitations in Data Explorer report visualization we encountered, there was a need for us to export specific visitor reports and raw event data to perform deeper time-on-site analyses of certain user segments. Leadership had asked us to provide average time-on-site for a certain segment of users, along with a year-over-year comparison. The time-granularity and YoY comparison was not something we could accomplish in Pendo directly. By segmenting this data, exporting it, and examining it at different time grains in Excel, we could uncover trends and insights that weren't immediately visible within Pendo.</p><p>Using Excel, you can build custom reports tailored to your specific needs. Whether it's detailed cohort analysis or advanced data visualizations, having the flexibility to manipulate and analyze data as you see fit is invaluable.</p><p>This is the first external reporting workflow we would recommend to Product Managers who have this need. Pendo has many ways to export data beyond report types that can be leveraged by Product teams (<a data-mce-href="https://engageapi.pendo.io/" href="https://engageapi.pendo.io/" target="_blank" rel="noopener">Enage API</a>, Pendo's <a data-mce-href="https://support.pendo.io/hc/en-us/articles/18214274061595-Overview-of-Pendo-Data-Sync" href="https://support.pendo.io/hc/en-us/articles/18214274061595-Overview-of-Pendo-Data-Sync" target="_blank" rel="noopener">Data Sync</a> capability) to build out data pipelines and more automated reporting. Once a need is established for these bespoke types of reporting through manual creation, we then recommend teams investigate if/how they might automate these reports.</p><h3>Conclusion<br></h3><p>Pendo offers excellent options for product managers to visualize their product data using dashboards and widgets. These tools provide a robust starting point for gaining insights quickly and efficiently. However, for more sophisticated or customized visualizations, mastering Pendo's Data Explorer is essential. And when those tools still fall short, exporting data to Excel gives you the freedom to perform advanced analyses tailored to your unique requirements.</p><p>In our two-year engagement with a large client, we've seen firsthand how Pendo's reporting capabilities can transform Product Management. By understanding the strengths and limitations of these tools, you can make more informed decisions and drive better outcomes for your products.</p>

Pendo's Reporting Capabilities for Product Managers

<p><em>Are you considering integrating <a data-mce-href="http://www.pendo.io" href="http://www.pendo.io" target="_blank" rel="noopener">Pendo</a> into your organization, or are you in the midst of these challenges? Leveraging Pendo's powerful product analytics can transform the way you understand and engage with user data to make informed decisions. For product leaders and managers, mastering product analytics is imperative to achieve success in today's competitive market. Interested in exploring this together? Let's chat!</em></p><blockquote><h3>Integrating a robust product analytics tool like Pendo into a single platform that houses multiple content-driven products presents unique challenges.</h3></blockquote><h3>Introduction</h3><p>Navigating the intricacies of product analytics within complex platforms can be daunting, especially for those matrix-organizations that manage multiple products differentiated by content within a single platform architecture. For example, Edtech companies that manage digital platforms across all K-12 subjects and their respective educational content. Think of how many "products" (aka, educational content like 9th grade math) this entails, each of which is led by Product Managers. Add on top of that the Platform-focused Product Managers who care about how the platform meets the needs across all of these users (teachers and students). This is no easy task to build and maintain, and when it comes to integrating Product Analytics, there is much to think through intentionally and proactively.&nbsp;</p><p>In this 7th installment of our series on Product Analytics Best Practices, we’ll explore how to effectively leverage Pendo within these content-heavy platform environments. Our insights are based on two years of hands-on experience with a large client, ensuring you get the most out of Pendo’s capabilities.</p><h3>Challenges with Content-Focused Product Platforms</h3><p>Integrating a robust product analytics tool like Pendo into a single platform that houses multiple content-driven products presents unique challenges. While a common platform architecture simplifies engineering, it complicates product analytics, especially when users switch between different products mid-session (imagine teachers switching between the courses they teach, each of which is a different content-product).</p><p>Here are three areas to consider:</p><p><strong>1. Event-based product context is key:</strong> Accurate event-based context is critical for distinguishing between various content products (ie: 9th grade math versus 6th grade english). Tracking the user’s context in real-time ensures that analyses reflect the correct product engagement.</p><p><strong>2. Users' engagement must have proper product context across page transitions and UI interactions:</strong> Common URL structures and UI element identifiers in single platform / multi-product architectures requires Product Managers to work with Engineers closely to ensure the right content-product context can be provided per page transition or UI interaction.&nbsp;<br></p><p><strong>3. Custom events and properties can be life-savers:</strong> Sometimes Pendo's out of box capabilities make it much harder to associate the right content-product context with the events the user is generating in their session; this is when the power of Track Events and custom properties can really help.</p><h3><br>Event-Based Product Context is Key<br></h3><p>Pendo’s visitor and account attributes are "last-in, first-out", meaning the most recent values passed in are persisted until the next session updates. This works for most visitor and account attributes that don't really change that often (think: the user's email address or the company's state location). But, for some attributes, what we really need is an event-based historical record of these attributes, so that we can accurately track users' states during their sessions. This is especially critical in content-product platforms, where users could be switching between courses mid-session.&nbsp;</p><p>Luckily, Pendo has provided an awesome capability called <a data-mce-href="https://support.pendo.io/hc/en-us/articles/13125305910299-Historical-metadata" href="https://support.pendo.io/hc/en-us/articles/13125305910299-Historical-metadata" target="_blank" rel="noopener">"Historical Metadata"</a> that they rolled out in the last 18 months that tracks attributes at the event level, allowing for more precise context management. Using Historical Metadata to capture event-specific context, such as the content product a user is engaged with, significantly improves data accuracy. This ensures that each event reflects the correct product context.</p><p>Product Managers working within these content-product platforms will need to think critically about what key Visitor and Account attributes are going to be critical per-event versus "last-in, first-out", to ensure proper segmentation at the product level can be applied to the events within users' sessions. These attributes should be designated as "Historical Metadata" in Pendo.&nbsp;</p><h3>Users' Engagement Across Pages and UI Interactions</h3><p>A single platform/multi-product architecture offers significant benefits from an architectural build out and maintenance perspective, but makes it much harder to implement product analytics. For example, a single platform might have common pages that can be applied to any of the content-specific products (ie: the 9th grade math homepage might be the same underlying platform page as the 6th grade english homepage). Similarly, the platform might actually use the same UI components across its various content-products (ie: a main navigation is the same no matter what product the user is in).&nbsp;</p><p>What this means for Product Managers as they think through how to best integrate Pendo into their platform boils down to making sure that the proper level of content-product context can be provided, in/around the use of these common architectural structures.</p><p><strong>Common URL structures:</strong> If the platform uses similar URL structures across its content-products, the PM should think about how to best associate the product's context with the page transitions of users during their sessions.&nbsp;</p><p><strong>Common UI components:</strong> If the platform leverages shared UI components, PM's should think about how they might identify or distinguish the content-product the user is within when they interact with these elements.</p><h3><br>Consider: Your Product Hierarchy / Taxonomy</h3><p>Product Managers should consider how Pendo's great metadata features might help them differentiate content-product events from each other during users' sessions. The first thing the PMs should align on with their teams and broader PM organization is how the "content-products" should be hierarchically organized, or what the product "taxonomy" should look like. In the Edtech space, for example, an oversimplified product hierarchy could look like: subject area (ie: math) -&gt; product (ie: 9th grade algebra) -&gt; user-type (ie: students vs teachers). </p><p>If alignment can be reached about this product hierarchy, then the right Pendo metadata capabilities can be leveraged to provide that context at the event level. Consider Pendo's <a data-mce-href="https://agent.pendo.io/advanced/location/" href="https://agent.pendo.io/advanced/location/" target="_blank" rel="noopener">LocationAPI</a> to augment page URL context, augmented or <a data-mce-href="https://support.pendo.io/hc/en-us/articles/21346265192603-Custom-HTML-attributes" href="https://support.pendo.io/hc/en-us/articles/21346265192603-Custom-HTML-attributes" target="_blank" rel="noopener">custom HTML identifiers</a> with product-specific context that Pendo can hook into for Feature Tags, or, as we'll discuss next, <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032294151-Track-Events" href="https://support.pendo.io/hc/en-us/articles/360032294151-Track-Events" target="_blank" rel="noopener">Track Events</a> with custom properties - all of these Pendo capabilities should be considered and leveraged fully in this complicated platform/product scenario.</p><h3>More Need for Custom Events (and Properties)</h3><p>Product Managers who work within these complex content-focused product platforms should put additional emphasis on <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032294151-Track-Events" href="https://support.pendo.io/hc/en-us/articles/360032294151-Track-Events" target="_blank" rel="noopener">Pendo's Track Events</a> and custom properties as a powerful way to capture their product hierarchy or taxonomy metadata at crucial parts of their product UX. These custom events and properties are often more effective than using Pendo’s out-of-the-box tagging features.&nbsp;</p><p>The downside, of course, is that the Product Manager will need to partner with their Engineering team to implement these Track Events. So PM's should be very strategic and selective in the Track Events they prioritize to be built out, to correspond with the most critical events of their products.</p><blockquote><h3>With the right product or content specific context captured by Pendo as metadata, teams can build granular and insightful reports tailored to various audiences.</h3></blockquote><h3>The Payoff of the Additional Efforts to Capture Product/Content-Specific Context?&nbsp;<br></h3><p>With the right product or content specific context captured by Pendo as metadata, teams can build granular and insightful reports tailored to various audiences. The types of reporting that can be built out include:</p><p><strong>Platform-Level Reporting</strong><br>Platform Product Managers can create aggregate reports to analyze UX flows and optimize overall platform performance. This high-level view can assist in the overall performance of the platform across all customers / users who engage within the platform UX across the various content-specific products.</p><p><strong>Content-Specific Product Reporting</strong><br>Content Product Managers can segment reports to focus on their specific product hierarchies/taxonomies. These PM's can analyze how their specific users interact with the content products of which they manage, providing them the ability to make data-informed decisions specific to their content products.</p><p><strong>Aggregate Insights for Leadership</strong><br>Leadership benefits from aggregated reporting across platform and product lines, which can help them manage certain programs that are targeted toward strategic customer segments.&nbsp;</p><h3>Conclusion</h3><p>For Product Managers operating within complex single-platform, multi-product environments, intentionally designed Pendo integration is crucial to ensure the right levels of granularity across the platform and product hierarchy are captured and able to be segmented for reporting. Understanding and implementing the right context around content hierarchy or taxonomy is vital for generating meaningful insights.</p><p>By leveraging Pendo’s metadata capabilities, including visitor and account attributes as Historical Metadata, Track Events with custom properties, or even LocationAPI and/or even custom HTML identifiers, Product Managers can ensure accurate, context-rich analytics for all audiences.&nbsp;</p><p>Take the next step in mastering Pendo within content-focused platforms. Implement these strategies and elevate your product analytics to drive success across your organization.</p>

Pendo within Content-Focused Product Platforms

<p><em>Are you considering integrating <a data-mce-href="https://www.pendo.io" href="https://www.pendo.io" target="_blank" rel="noopener">Pendo</a> into your organization, or are you in the midst of these challenges? Leveraging Pendo's powerful product analytics can transform the way you understand and engage with user data to make informed decisions. For product leaders and managers, mastering product analytics is imperative to achieve success in today's competitive market. Interested in exploring this together? Let's chat!</em></p><h3>Introduction</h3><p>Today’s most successful Product Managers excel at addressing challenging strategic questions regarding their product's true performance. They deliver insightful answers confidently by effectively utilizing product analytics reporting that has been intentionally designed. This blog post explores how Product Managers can get to the "real" answers they must be able to answer by maximizing their use of Pendo for strategic purposes, by employing a top-down design approach.</p><blockquote><h3>Today’s most successful Product Managers excel at addressing challenging strategic questions regarding their product's true performance.</h3></blockquote><h3>What Do We Mean by "Real" Answers?</h3><p>Before we jump in, let's clarify what we mean by "real" answers. In the context of product analytics, real answers are quantified views into various aspects of your product that specifically address the most important product questions that need to be answered, whether it's for leadership, for the Product Manager themself, or stakeholders. "Real" answers provide a clear understanding of what users actually do in the product, rather than assumptions based on gut-feel or market expertise. Getting to "real" answers is crucial for making data-driven decisions that align with user behavior and needs.</p><p>So how do you get to the "real" answers with Pendo? Here's a simple top-down framework to try out:</p><p>1. First, start with your product questions<br>2. Next, wireframe your strategic reporting<br>3. Then build your metrics specification<br>4. Next, build your reports<br>5. Finally, answer your product questions&nbsp;<br></p><h3>First, Start with Your Product Questions</h3><p>The first step in getting to real answers is to identify the strategic questions of which you need answers. This involves collaboration between Product Management and leadership to come up with a list of key questions that can be answered using product analytics data.</p><p>For example, you might want to know:</p><p>- What does adoption look like across various segments of users?<br>- What does the conversion look like for our membership sign-up and purchase flow?<br>- How are users engaging with our new features?<br></p><p>These are examples of essential questions that can guide your product development and marketing strategies. Having a clear list of questions ensures that your analytics efforts are focused and purposeful.&nbsp;</p><h3>Next, Wireframe Your Reporting</h3><p>Once you have your questions, the next step is to imagine what an ideal dashboard or set of reports would look like. This involves wireframing your reports to visualize the data story you want to tell.</p><p>Consider the following:</p><p>- Who will be the primary audience or users of your reporting?&nbsp;<br>- What would you start your story with? High-level aggregate counts or rolled-up KPIs?<br>- What kind of visualizations would be most effective? Simple snapshot counts or daily trends?<br></p><p>By wireframing your reports, you can outline the structure and order of the analytics you will feature. This helps you think through the types of visualizations you need to create valuable and insightful reports.</p><h3>Build Your Metrics Specification</h3><p>With your wireframes in hand, it's time to build a specification of the data points or metrics that need to be captured via Pendo in your product.</p><p>For each analytic, consider:</p><p>- What event data do you need? Clicks on specific UI elements? Visits to certain pages?<br>- How will you filter or segment your reports? By customer segments, active vs. disengaged users?<br></p><p>Defining these metrics and metadata ensures that you capture all the necessary data to answer your strategic questions. This step is crucial for setting up robust and comprehensive analytics.</p><h3>Build Your Reports<br></h3><p>Once your metrics data is flowing, you can start building the analytics components of your ideal reports. Use Pendo's dashboard features and out-of-box reporting components, or leverage <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360033574351-Data-Explorer" href="https://support.pendo.io/hc/en-us/articles/360033574351-Data-Explorer" data-mce-selected="inline-boundary" target="_blank" rel="noopener">Pendo's Data Explorer report</a> for more custom views.</p><p>Pendo is continuously enhancing its features, so always stay updated with the latest improvements. Building your reports is where the magic happens, turning raw data into actionable insights.</p><h3>Answer Your Product Questions</h3><p>With your reports in place, you can now answer the strategic questions you set out to explore. This is where communication and transparency come into play. Share your findings with your team and leadership, discuss the insights, and determine the next steps.</p><p>Effective communication of data and insights ensures that everyone is on the same page and can make informed decisions based on real answers.</p><blockquote><h3>By following these steps, you can ensure that your product decisions are always backed by solid, data-driven insights.</h3></blockquote><h3>Conclusion</h3><p>This top-down approach to designing your product analytics reporting keeps you focused on your product strategy. By starting with key questions, wireframing your reports, defining metrics, and building insightful dashboards, you can uncover real answers that drive your product forward. By following these steps, you can ensure that your product decisions are always backed by solid, data-driven insights. This not only helps in better understanding user behavior but also aligns your product strategy with actual user needs and expectations.</p><p>Try this approach and see how it transforms your product analytics approach. If you have any questions or need advice, reach out, and let's have a conversation about how to get the most out of Pendo and product analytics in general.</p><p><br></p>

How Product Managers can get to Real Answers with Product Analytics

<p><em>Are you considering integrating <a data-mce-href="pendo.io" href="pendo.io" target="_blank" rel="noopener">Pendo</a> into your organization, or are you in the midst of these challenges? Leveraging Pendo's powerful product analytics can transform the way you understand and engage with user data to make informed decisions. For product leaders and managers, mastering product analytics is imperative to achieve success in today's competitive market. Interested in exploring this together? Let's chat!</em></p><blockquote><h3>In today's fast-paced digital world, it's imperative that product managers are a part of any product analytics design/implementation efforts to ensure that the specific data tracked in Pendo is actually going to help them make data-informed decisions.</h3></blockquote><h3>Introduction&nbsp;</h3><p>Welcome to the next chapter in our blog series, where we transition from the technical nitty-gritty of integrating Pendo to the crucial planning and oversight considerations for Product Managers and the product analytics reporting that will meet their needs. In today's fast-paced digital world, it's imperative that product managers are a part of any product analytics design/implementation efforts to ensure that the specific data tracked in Pendo is actually going to help them make data-informed decisions. This post focuses on metadata design, a key aspect that can make or break the effectiveness of your Pendo integration.&nbsp;</p><h3>Why Metadata Matters</h3><p>Pendo has several important metadata capture and tracking capabilities of which Product Managers should be aware. In this post, we use "metadata" as an broad term that refers to Pendo's Visitor and Account attributes, its page / session context augmentation to URLs from its LocationAPI call, and event and custom properties. As you can see, there are many ways to pass "metadata" to Pendo that should be considered.&nbsp;</p><p>But why does this matter, especially to Product Managers? These elements enable robust segmentation and filtering, making life easier for those creating reports, planning targeted guides, and more. Getting metadata right from the start is crucial because retrofitting it later can be a time-consuming process, often resulting in weeks of setbacks in reporting insights.</p><h3>Visitor and Account Metadata Design</h3><p>Product Managers should have a comprehensive understanding of their customers. This knowledge is vital for defining what attributes about specific users or their companies will be needed for detailed reporting. Pendo's Visitor and Account attributes are populated at initialization and/or when specific update calls are made throughout a user session.</p><p><strong>Questions to Consider</strong><br>1. What customer segments using my product matter most? How might these segments be represented in code that could be used to feed Pendo?<br>2. How will I want to filter or segment my reporting on my product? How will leadership want to see the reporting?<br>3. Are there already defined user attributes that we track for our product that can be ported into Pendo?</p><p>Once you have these details sorted, collaborate with your engineering team to detail what specific user or account attributes are tracked in the code. These attributes can then be used to populate Pendo attributes during the pendo.initialize() call, ensuring a seamless data flow.</p><h3>Product Experience Metadata Design</h3><p>Product Managers should know their product inside and out. They should understand which paths or funnels are crucial to the success of their users and product goals. They should know which features are most important to optimize engagement. Based on this intimate understanding of the product, PMs can translate what matters most into Pendo-specific metadata (and of course metrics that matter most to track). When it comes to feature engagement, pathing analyses, or funnel optimizations, Pendo's metadata capture capabilities with Location API, Event Properties, and Custom Properties via Track Events should be evaluated.</p><p><strong>LocationAPI as a Metadata Tool</strong><br><a data-mce-href="https://agent.pendo.io/advanced/location/" href="https://agent.pendo.io/advanced/location/">LocationAPI</a> is a way for engineers to provide additional context or metadata as part of the URL that Pendo captures when users visit various pages of the product experience. This method enables sophisticated page tagging, segmentation, and filtering capabilities that can be leveraged for pathing analyses, funnel optimizations, or other user session navigation throughout the pages of the experience.&nbsp;</p><p>There are best practices we learned over two years in working with LocationAPI that can help optimize the usability of the data that is captured. For example, sending metadata as query parameter extensions rather than additional pathing elements of the URL makes page tagging and filtering much easier. (There's a lot here, more than we can cover in a blog post... Reach out for more details about LocationAPI best practices if you're in the midst of implementing it!)&nbsp;</p><p><strong>Click Event Properties and Custom Properties</strong><br>Pendo's out of box <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360031950492-Tagging-and-viewing-Features" href="https://support.pendo.io/hc/en-us/articles/360031950492-Tagging-and-viewing-Features">Feature Tagging</a> capabilities are great, most of the time, for simple aggregated click engagement tracking. But occasionally, feature tagging doesn't offer enough depth of context. Product Managers need to identify those cases and consider one of two approaches:</p><p>1. Use Click Event Properties<br>2. Use Track Events and Custom Properties</p><p><br><a data-mce-href="https://support.pendo.io/hc/en-us/articles/7710433678619-Event-properties" href="https://support.pendo.io/hc/en-us/articles/7710433678619-Event-properties">Click Event Properties</a> is a subscription-wide setting in Pendo that gives admins the ability to specify certain property values (ie: metadata) at the time a specific feature is engaged with. These specific Feature Tags, when they are triggered &amp; tracked by Pendo, will then contain additional metadata context about the state things were in when the user engaged with that feature. This is a quick/easy way to capture additional context that extends Feature Tags.&nbsp;</p><p>Pendo also offers a custom event capability called <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032294151-Track-Events" href="https://support.pendo.io/hc/en-us/articles/360032294151-Track-Events">Track Events</a>. These are custom events triggered programmatically. In some cases, more robust &amp; detailed tracking is needed for proper analysis by Product Managers. In such cases, PMs should work with engineering to define and implement Track Events. The added benefit of Track Events is that they extend Pendo's metadata capture capabilities by allowing teams to pass custom properties as part of the event call in code. This allows teams to define their own metadata and programmatically pass it with these events. For example, tracking granular Search dropdown and results capabilities using Pendo's feature tagging can be limited. However, with custom Track Events that trigger around a user's engagement with the Search UI, you can gain deeper insights into user behavior, such as search terms and clicked results. The custom properties passed into these Track Events are powerful metadata that can be used for deeper analysis into the Search UX via Pendo reporting.&nbsp;</p><h3>Conclusion</h3><p>Product Managers are essential in translating their knowledge about product strategy, users, and the product experience into meaningful metadata that can be captured in Pendo. Their involvement from the outset ensures that the metadata design is strategically aligned and implemented before it's too late. The worst-case scenario is when user segmentation or filtering isn't possible due to the lack of proper metadata capture, leading to delays that could span weeks.&nbsp;</p><p>By thinking critically about the metadata needed for their products and leveraging Pendo's robust tracking capabilities, PMs can significantly enhance the effectiveness of their product analytics tracking and reporting tool.&nbsp;</p><p>Are you ready to elevate your Pendo integration? Start thinking about your metadata strategy today. Your future self will thank you.</p>

Pendo, Product Managers, and Metadata Design

<p><em>Are you considering integrating <a data-mce-href="http://www.pendo.io" href="http://www.pendo.io">Pendo</a> into your organization, or are you in the midst of these challenges? Leveraging Pendo's powerful product analytics can transform the way you understand and engage with user data to make informed decisions. For product leaders and managers, mastering product analytics is imperative to achieve success in today's competitive market. Interested in exploring this together? Let's chat!</em></p><blockquote><h3>Integrating analytics tools like Pendo within SPAs can present unique challenges that need to be taken into consideration.</h3></blockquote><h3>Intro</h3><p>Many product-focused companies today build single-page web applications (SPAs), because they offer a seamless user experience by dynamically loading content without refreshing the entire page. Integrating analytics tools like Pendo within SPAs can present unique challenges that need to be taken into consideration. This guide aims to help you understand these challenges and provide practical solutions to ensure accurate data capture and reliable insights.</p><h3>Understanding the Basics of Single vs Multi-Page Applications<br></h3><p>When dealing with web applications, it's crucial to distinguish between single-page and multi-page frameworks. In a multi-page web application, each page change results in a full reload within the browser. This reload action typically triggers the `<a data-mce-href="https://agent.pendo.io/public/Agent#initialize" href="https://agent.pendo.io/public/Agent#initialize">pendo.initialize(options)</a>` call, capturing the current values of any visitor and account attributes, and passing those to Pendo seamlessly.</p><p>Conversely, in a single-page web application, page transitions are handled dynamically, eliminating the need for a full page reload. This dynamic nature poses a potential pitfall for Pendo integration. If any changes occur to visitor or account attribute values due to state changes, Pendo might not capture the most recent values, leading to data integrity issues. This can result in event data being associated with outdated visitor or account attributes, compromising the accuracy of your reports and insights.</p><h3>The Importance of State Changes in Your App<br></h3><p>Understanding state changes within your app is pivotal for effective Pendo integration. Users interact with various elements, transition between pages, and perform actions that might alter their user profile, account, or other aspects tied to visitor and account attributes (metadata). In a single-page application framework, it's essential to identify all possible points where state changes can be triggered.</p><p>For instance, if a user navigates to their profile page and updates their email address, this constitutes a state change affecting a visitor attribute. Recognizing these state change points allows you to coordinate with your engineering team to ensure that Pendo captures these attribute changes accurately.</p><h3>pendo.updateOptions(options)<br></h3><p>Once you've mapped out all potential state change points across your single-page application, the next step is to collaborate with your engineering partners. It's crucial to integrate the `<a data-mce-href="https://agent.pendo.io/public/Identity#updateOptions" href="https://agent.pendo.io/public/Identity#updateOptions">pendo.updateOptions(options)</a>` call at these state change points. By doing so, you ensure that the latest attribute values are passed to Pendo during the user's session, preventing any data integrity issues.</p><p>Your engineering team can help identify the best places to implement `<a data-mce-href="https://agent.pendo.io/public/Identity#updateOptions" href="https://agent.pendo.io/public/Identity#updateOptions">pendo.updateOptions(options)</a>`, ensuring that attribute updates are captured in real-time. This collaboration is key to maintaining accurate and trustworthy data, which is essential for making informed product decisions.</p><blockquote><h3>Our insights come from real-world experience working with a client on their single-page web application.</h3></blockquote><h3>Real-world Lessons Learned<br></h3><p>Our insights come from real-world experience working with a client on their single-page web application. Early on, we faced challenges with Pendo reporting accuracy due to the dynamic nature of this client's SPA framework and changing states from users' sessions. By closely monitoring state changes and implementing `<a data-mce-href="https://agent.pendo.io/public/Identity#updateOptions" href="https://agent.pendo.io/public/Identity#updateOptions">pendo.updateOptions(options)</a>`, we ensured that their data events and attributes were associated correctly.</p><p>This led to accurate and reliable reports, providing valuable insights into user behaviors. The success of this integration highlights the importance of understanding state changes within SPA web apps and ensuring Pendo is always updated properly throughout users' sessions.</p><h3>Conclusion<br></h3><p>Integrating Pendo within a single-page web application framework requires careful consideration of state changes and collaboration with your engineering team. By paying close attention to attribute updates and ensuring accurate data capture, you can achieve reliable insights and make informed product decisions.</p><p><br></p><p><br></p><p><br></p>

Blog

Putting it all Together: A Product Analytics Maturity Model

Pendo's Reporting Capabilities for Product Managers

Pendo within Content-Focused Product Platforms

How Product Managers can get to Real Answers with Product...

Pendo, Product Managers, and Metadata Design

Pendo and Single-Page Applications