Which Data Matters Most for Personalization?

Which Data Matters Most for Personalization?

January 4, 2019 4 minute read
Eric Fullerton, Cyril Coste and Daryn Mason will discuss the keys to personalization success in a webinar on customer experiences.
Which Data Matters Most for Personalization?

Three age old questions:.

  • What is the meaning of life?
  • What came first the chicken, or the egg?
  • Which data matters most?

While I can’t help with the first two questions, I’ll be teaming up with my new friends from across the pond, Cyril Coste and Daryn Mason, to offer insight into the only question from above we are remotely qualified to answer (hint: it’s question three) in a Jan. 24 webinar: Customer Insights for CX Personalization: Which Data Matters Most? Coste and Mason have already started the conversation, which you can see in this Q&A blog post, and I want to chime into help set the stage for our upcoming webinar with some thoughts of my own.

Let’s start this blog with a couple of assumptions. First, let’s assume that you agree that personalization is a critical component of a customer experience strategy. Next, let’s also assume that you agree that data is a critical component to doing personalization well. Now that we’re on the same page, I can help answer part one of the “which data matters most?” question.

In my eyes, the most important type of data for today’s organizations to collect and analyze is first-party data. I’m by no means dismissing the usefulness of third-party data or other data types, but I believe they are most effective as a way to augment existing first-party data. I’m not one to make claims without sources, so it’s worth referencing a marketer data survey from E-consultancy and Signal which came to the conclusion that first-party data garners the highest return on investment of any data type.

The survey also found:

  • 64 percent pointed to first-party data as driving the greatest increase to their customer lifetime value
  • 68 percent selected first-party data as the easiest data investment to justify financially

Once you’ve agreed to invest and focus on collecting and actioning first-party data, you’ve only solved one piece of the puzzle. There’s lots of information to collect on customers, so how does one do it best in service of their customer experience goals? How can you identify which data is most actionable and what isn’t as useful? I like to think of data collection as a phased approach that builds off itself, and that there are three phases of data collection that can provide context – baseline, activity, and behavioral data. I’ll delve into at a much deeper level on the webinar, but here’s some examples of each data type. 

  • Baseline: collected at first touch from anyone
    Examples: geolocation, device type, new or returning, marketing campaign
  • Activity: collected from actions taken by users
    Examples: viewed X page, clicked on X content, downloaded X asset, purchased X product.
  • Behavioral: make inferences from actions of most active users
    Examples: favorite product, preferred content, funnel stage, high value region

An interesting point about all of the data referenced above … it can all be collected anonymously. While “knowing” who your users are via identifiable information may be necessary for the most complex of personalizations, there’s so much data to collect and action on that knowing customers by PII isn’t a requirement to improve the customer experience. With that brief overview of the three important types of first-party data to collect, let’s actually review some customers who have collected the right data to gain true insight into their customers to deliver powerful personalization.

Wendy’s is one of my favorite examples to highlight because it’s a little fun and very tangible. The goal for Wendy’s with Acquia Lift, our data collection and personalization tool, is to build persona groups for their different users based on their interests – folks who are burger lovers, salad lovers, and chicken lovers. By understanding the activities they took, whether it be clicking on certain offers or promotions, or actually completing an order, Wendy’s was able to group users into those behavioral segments based on their activity, rather than assuming they fit a certain segment based on demographic or other non-behavior based information. At our Engage conference, Mike Mancuso (head of digital analytics at Wendy’s) spoke to this using the example of his wife, a sure fire fit for the “salad lovers” persona based on every identifiable demographic factor … except that she isn’t, and fits firmly on team burger based on her love of the Baconator.  

That’s just a sneak preview of what I’ll be discussing with Coste and Mason on the Jan. 24. I encourage you to join us for Customer Insights for CX Personalization: Which Data Matters Most? by clicking here.

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