Explore and act on your audience data with real-time analyses
With customer data management and real-time audience segmentation, marketers can efficiently analyze data and discover optimization opportunities, and quickly act upon it through personalization campaigns.
For marketers across industries, lack of user data is no longer a major challenge. Consumers are more willing than ever to hand over data in return for more relevant internet experiences and marketers are easily able to collect it.
But as brands collect more information, a digital paradox of plenty emerges. Marketers have a wealth of knowledge about consumer behavior but struggle to act on this plethora of audience data to create personalized experiences across all consumer touchpoints.
With an average of 17 point solutions in most marketing stacks, data often exists in disparate sources ranging from CRM to marketing automation and analytics tools, greatly limiting the ability to craft cohesive experiences. Last month, we announced omnichannel audience sharing to help marketers create rich data sets that account for all consumer interactions across devices. As a result, Dynamic Yield customers can serve users who visited their mobile app but did not complete a purchase with personalized offers on desktop and mobile web.
However, even when data is unified, it takes time to put to use. Amazingly, less than .5% of digital data created globally is ever even analyzed.
As we enter the “experience age”, time has largely replaced data as the limiting factor for marketers implementing personalization. The simple lack of hours in a day to study trends in data directly leads to a decrease in the number of personalized experiences created. To boost agility, marketers require an interface that optimizes for easy analysis of audience data in the shortest possible time.
To help our customers quickly act, we’ve introduced our Audience Explorer.
With Audience Explorer, Dynamic Yield customers can now easily view user segments without having to fully construct audiences. As the ability to analyze segment data in real-time has proven invaluable to marketers, the audience exploration tool lives as a standalone section under the “Audience” tab in the Dynamic Yield left side navigation bar.
Go inside the DNA of your users as Audience Explorer provides a wealth of information on a user segment based on any parameter you would like to explore. See instant snapshots of behavior based on simple filters such as device or geography or complex custom actions and understand how each segment contributes to conversion or revenue goals.
Furthermore, Audience Explorer bolsters Dynamic Yield’s power as the source of truth for all customer interactions, incorporating data based on user properties, behavior and onboarded off-site data from sources such as a CRM.
From Exploration to Action
A key feature of Audience Explorer is the ability to find valuable segments, particularly those that present an opportunity to drive more conversions.
For example, examining traffic solely from mobile web will often show that mobile web users represent over half of a website’s traffic but account for under 20% of purchases and have a significantly lower average order value. This conversion gap comes from decreased engagement during mobile sessions and too many marketers still serving mobile experiences that leave visitors prone to cart abandonment. To combat this, create a segment for mobile cart abandoners and send a personalized message such as a discount at checkout as a peel notification on the bottom of the page.
Once you’ve identified an interesting group of users to deliver a personalized experience to, Audience Explorer lets you instantly save the audience for future use. This audience’s behavior will now be continually tracked and is permanently available to target in the audience manager tab.
Audience Explorer allows marketers to seamlessly analyze smartphone users in Paris, newsletter subscribers or users who clicked on product page recommendations without having to spend time building standalone audiences for each. The result is less time sorting data more time acting on it.