The data types that drive the most relevance: EQ3 2018 out now

By Evan McGonagill

December 6, 2018

Marketers already know that one-size-fits-all is out. Segmentation and targeting techniques were developed in response to that realization, in order to cater to audiences with distinct characteristics so that tailored decisions can be made for different groups of customers. And that technique works, up to a point. However, its effectiveness is limited: no segmentation scheme is detailed enough to capture every meaningful variable that might influence which content is most relevant for a given consumer. 

In fact, even individual customers themselves should not be treated as static entities: what is relevant to someone on a Tuesday might no longer interest them on Thursday. The shift could be caused by a change in weather, day of the week, or even whether they made a purchase from the brand on Wednesday evening. All of those small differences might change a customer’s interests and behaviors enough to influence what they will respond to and engage with while interacting with a brand. 

So how can brands keep up with all of the minute-to-minute changes that influence their customers?

The answer is in the data. Making sure that brands are collecting as much customer data as they are able to, and ensuring that they have the technology to leverage it in their content decisions, goes a long way towards making a customer feel connected. And making sure that they are AI-capable is the only way to perform truly individualized personalization at scale.

For the latest Ecommerce Quarterly report—EQ3 2018: A Conversation About Context—we analyzed over 1.6 billion shopping sessions from the 3rd quarter of 2018, in order to determine what types of data can help brands interpret contextual signals from their customers, and how they can use those signals to serve the ideal content to each individual. What we found is that different types of data perform differently, but there is no excuse for using none at all—and the rewards are clear, with the use of contextual data bringing an average lift in engagement of 37%. For brands looking to capture their visitors’ attention with relevant, personalized content, these contextual signals are an essential piece of the puzzle.

Download the full report to learn more about which types of data brands are relying on the most heavily, which drive the highest lift, and what organizations can do to build their capacity to leverage the data they have.

Evan McGonagill is a content writer for Monetate, where she researches and produces whitepapers, blog posts, and other material about commerce and personalization. Evan has a background in libraries and archives, and she uses her interest in the structure and flow of information to think about how brands can harness data to build more personal connections with their customers. When she isn't in the library or learning about personalization, you can find her playing music in West Philadelphia.

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