Behind every great digital experience is robust data. Each time a consumer launches an app or puts an item in a shopping cart, a piece of data is created. Brands that are able to gather all of this and make sense of it can engage their audiences in ways that are intuitive and highly personalized. As consumers become increasingly selective about how they spend their time across digital channels, data is the secret sauce to draw in new customers and retain existing ones.
The tools we provide in Adobe Analytics bring together Adobe’s creative heritage with a robust data platform, to not only help brands analyze data and uncover insights at the click of button, but to also become better storytellers in driving action across their organizations. With Analysis Workspace, users have a visual editing platform to deliver, discover, and visualize insights and curate them to share with the organization.
Today we are announcing new capabilities in Analysis Workspace that furthers our mission to democratize analytics.
Flow Exploration and Fallout Analysis
The first step to delivering great consumer experiences is having an understanding of how people are navigating your digital channels and where they are hitting stumbling blocks. A new Flow Exploration capability within Analysis Workspace let’s brands visualize customer movement through digital experiences, showing the steps taken from entry point through to conversion or churn. Segments of users can be created based on usage patterns. A cohort of customers who are bundling related items, for instance, can be engaged with content on other suggested pairings.
The new Fallout Analysis in Analysis Workspace allows brands to easily drag, drop, and rearrange steps along the user experience to better understand at what point users are disengaging, in addition to insight around where they go after the fallout. With this data available, brands can implement the necessary adjustments to not only retain users but also improve the experience and drive more loyalty. Analysts can also create audience segments from users who stay in the funnel, or those who fall out, at any stage for remarketing and personalization.
A home improvement retailer, for example, might see that, on their mobile app, users who have to leave the shopping cart to view more items abandon purchases at higher rates and don’t return. This insight can be leveraged to re-design the cart experience as a floating interface that remains on the app screen.
Getting A Faster Start
Organizations are increasingly pushing every team member to become more data-driven in their decision-making. Given that, tools must appeal to the most novice user while still serving the needs of the most advanced analysts. We all know how difficult it is to stare at a blank page, which is why we are introducing new starter projects in Analysis Workspace. These will provide a more natural ramp-up for those trying Analysis Workspace for the first time. With out-of-the-box answers to common business questions across the web and mobile apps, as well as specific templates for vertical industries including Retail and Media & Entertainment, no one should have to be a data scientist to leverage insights to improve their work.
Seeing The Forest for the Trees
In this release, we are rolling out a series of new visualizations, as well as enhancements within Analysis Workspace that will provide users more tools to curate and present insights in ways that resonate most with stakeholders.
Time-series analysis: Within line graphs, users can now forecast expected future outcomes or automatically detect data anomalies and run contribution analysis in a single click to determine the cause of unexpected performance. A financial services company could tap into this to determine the cause of a spike in credit card applications during a typically slower month, for example.
Histograms: In a classic bar chart, each bar shows an individual value. A histogram is different in that it presents distribution. This is useful for brands as they’ll be able to pinpoint the most valuable and least valuable segments of customers, as well as the distribution of a certain behavior. A clothing retailer, for instance, can identify that most shoppers spend between $25 and $50 and turn that into a key segment for holiday promotions.
Date comparisons: At the push of a button, users can now compare data year over year, month over month, or day over day. All of this is presented in a single graphic to visualize the change, while serving up data points around percent changes as well as raw values. This can better inform everything from inventory to campaign planning.