Adobe Product Analytics overview

Adobe Product Analytics enables product teams to self-serve data and insights about their product experience through guided analysis workflows, built on the same cross-channel data of Customer Journey Analytics.

Hi, this is Jen Lasser with Adobe Analytics Product Management. I’m excited to show you Adobe Product Analytics, a completely new way to interact with the rich omnichannel data that lives in customer journey analytics. Analysts love to dive deeply into their data using Analysis Workspace. But what about product and marketing teams that need to answer questions quickly about usage of their experiences? The data language and experience of Analysis Workspace can be overwhelming for some, leaving them unable to self-serve their critical business questions. Adobe Product Analytics introduces guided analyses that have been tailored to the jobs that must be done by product owners like myself. You can see some of them here. Friction and conversion trends for funnel analysis. Place and first use impact analysis. Usage trends. Active user growth and net user growth. And even more to come. Now, product teams commonly need to assess monthly active users to determine the growth of their user base. So let’s dive in here first. I instantly understand more about my users without having to take another action. This purpose-built experience is doing the heavy lifting for my most critical questions and delivering not only chart and table answers, but human readable insights as well. The active view in user growth helps me understand am I getting new users? Are they being retained? Or are they falling dormant? The guided experience lets me ask my next question with ease. Need to swap to weekly active users? No problem. If you want to filter to a more pivotal point in the journey, no problem as well. Adobe’s event-based model for data ingestion makes this easy to do. Let’s filter to those users who’ve started a video. Now this dormant users segment provides a great opportunity to reengage users of this product. Just like in customer journey analytics, I can click from my analysis and make any audience available for activation in Adobe real-time customer data platform and journey optimizer. The audience is available without delays and is consistent from analysis to activation. The net growth view provides a quick pulse on this analysis to help you understand if your user base is growing or declining. From this view, you can very quickly see that our user base grew quite a bit the week of May 21st. This presents a great opportunity to go look at what we did in the product experience that week and see if we can replicate it more. Now let’s take a look at some of the other analysis types that we offer. Funnel helps you understand where there’s friction in key customer journeys. And because customer journey analytics incorporates marketing data, product data, and data from the entire customer journey, your funnels can start earlier and end later. I can start with that dormant campaign delivery that we talked about earlier to reengage our users, click throughs from that campaign, and then two critical events in our product, media starts and media completes. I can look at conversion through this funnel compared to the first step and also calculated compared to the previous step. Additional settings include looking at this across a user’s entire lifetime or constraining the funnel to happen within the same session. I can also compare the journey across different segments of users. Let’s say all users versus my free subscribers. With additional views like conversion trends, I can also take a look at this funnel not only in aggregate, but also trended over time. Impact analysis helps us understand if new factors are impacting key success events that we’re measuring. For example, if we wanted to measure the impact of that dormant campaign that we sent out earlier to reengage our users, we can use the first use impact analysis. We’ll center our analysis on users that click through, and then we’ll see if that has an impact on media starts or engagement generally. We can very quickly tell that we had a 45% lift in media starts from users who click through from that campaign. The first use view is just one impact analysis. Release impact helps you understand performance before and after key release dates or campaign launch dates. Now, if you want to measure user engagement over time, the usage trends analysis has you covered. Watch how the context from my impact query came with us. Now, if we want to see how many users took these actions instead of a count of events, we can quickly change our metric to users and get a different answer. If we prefer to look at this as a bar chart instead of line chart, that’s also a very easy change. If you need to understand how these trends compare to historical time periods, that’s just a click away. Now, building better customer experiences is a team sport. Product teams need to work closely with marketing and analytics teams to deliver better customer experiences. Guided analysis enables seamless collaboration with those teams through sharing and open a workspace. Because guided analysis is built from the same data views that are used in analysis workspace, you can click open a workspace and everything is brought with you to analyze further and collaborate with your colleagues. All of the moments that matter with your customer available in one analytics tool. With Adobe Product Analytics and Customer Journey Analytics capabilities, teams will be able to analyze and activate based on the entire customer journey. Product, marketing, and analytics teams will be aligned like never before.

Using Adobe Product Analytics, teams can:

Adobe Product Analytics is a paid add-on to Customer Journey Analytics. If your organization would like to be provisioned to use this feature, contact your Adobe Account Team.

For additional information, please visit the documentation.