Adobe Product Analytics accelerates experience-led growth
Many of us on software product teams remember the “olden days” (not all that long ago) when we would celebrate a launch as success. If you delivered, that was victory. While the act of shipping software may still warrant a pizza party and commemorative T-shirts, experience-led growth today is requiring product teams to measure success not in terms of code shipped, but rather in terms of user engagement, retention, and conversion — and not just within the product. Curious, forward-thinking product teams are asking to put their product data in the context of the broader, cross-channel customer journey.
Today we launched Adobe Product Analytics, a groundbreaking new solution that empowers product organizations to look beyond their data silo to understand the complete customer journey at incredible speed. After countless hours of prototyping, development, gathering user feedback, and iterating, we are so excited to bring this extraordinary solution to market and help our customers solve their product data needs.
When we started this journey, our goal was to help product teams tap into the power of Adobe Experience Cloud, but in a way that better met their needs and exceeded their expectations. Through extensive research and time spent learning from product teams, we identified critical user needs unmet in the market today to build the Adobe Product Analytics foundation.
Self-serve insights
Users increasingly have high expectations for the digital products they use. They expect brands to understand who they are, know what they want, and provide a personalized experience when they need it. To meet these expectations, product teams need to quickly gain insights into their users — including previous cross-channel engagement — and act fast to improve the product experience. Product teams cannot afford to spend time reconciling differences between disconnected tools or waiting on an analyst's backlog for an answer.
We designed Adobe Product Analytics with the goal of providing an insight in under a minute. With industry-first guided analyses, product teams can quickly self-serve answers to questions in seconds, saving time and leading to more data-driven product decisions. Additionally, Product Analytics provides answers in natural language so even the most data-novice users can find the insights they need.
Identify friction in the experience
Identifying areas of friction in the user experience is vital. Product teams are eager to track critical user journeys and find ways to improve their conversion rates. Unfortunately, disconnected data and surface-level usage metrics often hide pain points along the user journey.
By using funnel-guided analysis, product teams are able to isolate areas of friction in the product and measure conversion over time. Powered by cross-channel data views, funnels can start earlier and end later for a complete view of the user experience.
Find patterns in user engagement over time
In order to provide the best experience possible, teams need to have a clear understanding of how product usage is trending over time. Whether they’re tracking logins or other key success measures, monitoring the engagement of their users is a necessity for product teams.
Trends-guided analysis helps teams to quickly find patterns in user engagement over time. Since Adobe Product Analytics is built on the power of Adobe Experience Platform, brands can evaluate engagement beyond just their digital products. By using all the same workflows, data, and customer profiles as their marketing and customer-experience (CX) counterparts, product teams can monitor how a marketing campaign performed and follow-up to see if those customers converted.
Measure impact of releases
It’s incredibly difficult for product teams to understand if their work is making a difference. Whether they launch a new feature or improve an existing one, they need a quick way to see if that release is improving the product experience across key success measures.
Impact-guided analysis is the perfect way to measure pre- and post-launch behaviors. Teams can see how a release changes user behavior — and this is not limited to just the product. For the first time, product teams can monitor if an email clickthrough leads to more cart checkouts in the mobile app or if an improved help section decreases calls to the call center.
Track user growth and retention
Understanding active usage, growth, and retention of a user base over time requires complex user segments and calculations. Product teams need to measure their monthly active usage (MAU) on an ongoing basis. They need to break down the makeup of their user base to understand who is actively using the product and who may need a nudge to re-engage.
By working with user growth–guided analysis, product teams can track changes in user activity overall or focus on the use of a specific feature. Within seconds, they can identify new, repeat, return, and dormant users over different periods of time. Keeping an eye on the user base ensures they are aware of trends in activity and can take action.
Connect to deeper analysis
Data maturity varies greatly across teams and roles, yet most tools are one-size-fits-all. This often leaves novice users feeling overwhelmed and advanced data analysts left wanting more. While a product team needs to self-serve vitally important insights into their products, they may reach a wall in their capabilities and need to collaborate with their analyst for deeper insights.
With Adobe Product Analytics, organizations now have the right tool for everyone. Guided analyses empower teams to start their analysis quickly, but if they want to go deeper, they can easily open their analysis in Analysis Workspace when needed. This ensures product and their analyst counterparts can collaborate seamlessly within one tool.
Deploy and analyze experiments
Experimentation is critical for the success of a digital product, yet it often seems out of reach for most product teams. This fundamentally important process is often noted as a luxury for product teams due to the time lost and challenges with cross-team coordination and navigating multiple tools to deploy and analyze experiments.
The native connectivity of the Experience Cloud helps teams to deploy experiments with Adobe Journey Optimizer and analyze experiments with the experimentation panel in Analysis Workspace. The ability to deploy and analyze these experiments using the same data, metrics, and customer profiles allows teams to test ideas faster and better optimize the product experience.
Create audiences for activation and engagement
Insights and data visualizations are helpful, but the real value comes from acting on those insights. Once a product team uncovers an insight into the customer experience, they need to quickly partner with colleagues from across marketing and CX to take action.
By tapping into the full power of Experience Cloud, product teams can go beyond insights. Through native integrations with Adobe Journey Optimizer and Adobe Real-Time Customer Data Platform, product owners are able to act immediately by publishing real-time audiences for activation. Once a campaign has launched, they can close the loop and measure its impact in Adobe Product Analytics for ongoing optimization.
What’s next?
If you’re a member of a product team who cares about any of the topics I’ve described here—from removing friction in your app to running and analyzing experiments to understanding your users in the context of the holistic customer journey — I hope you’re excited about what we are releasing. This is the next evolution for leading teams who readily accept responsibility for more than just what happens in their piece of the product.
As one of those product teams I mentioned at the beginning who has evolved to care more about impact than about delivery, we will be watching closely to make sure we’re hitting the mark with these and other capabilities coming soon to Adobe Product Analytics.
And don’t worry — we’ll be celebrating the release with T-shirts, too.
Learn more about Adobe Product Analytics and how it empowers product teams to self-serve data and insights to drive product decisions.