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How to Use Customer Data for Personalization

By Frances Russell | April 03, 2020 | 0 Comments


Any story about personalization is really a story about data. In addition to demographics, our customers have attitudes, preferences, browsing behaviors in addition to buying behaviors. Not only are we sourcing this data ourselves, but customers are giving it to us. On Amazon, Facebook, LinkedIn, etc., they’re telling us what they want, need and think without us ever even asking. And they’re expecting us to use it.


When looking at technologies and systems to enable personalization, marketers start with customer data management. Looking at internal systems as well as new technologies, they desire both quantity and quality: more data points, more “clean” data, and, of course, access. This is one of the many promises of customer data platforms (CDPs) and it helps us postpone what we really need to do. Stick with me.


What’s the ideal scenario we’re chasing? Faced with a few high-level business objectives (see below), imagine having access to all the data points in the world on all of your customers – everything you need to solve their problems and no need to ask them questions. Now, start crossing off the data points you don’t need. Now, stop imagining. It’s going to take forever.


  • Selling a Product: I have a problem and I’m searching Google, entering your store or browsing your website for products and/or services that will help me solve it.
  • Customer Experience: I’m having issues using your product or service to solve the problem I bought it to fix.
  • Cross-Selling: I have a problem that I’d hoped to solve through your product or service, but I think I may need something else.


Many marketers have realized that in order to achieve these objectives, what they really need to do is not get more data; they need to get the right data. What is “the right data?” The right data is both correct (i.e. customer contact information that changes addresses when they change, de-duplicated records that recognize Ethan Budgar and Ethan H Budgar are the same, etc.). and informative (i.e. data that will enable us to better solve their problems, or provide tailored help to customers, not bother them, creep them out, or worse, terminate customer relationships).


Rather than starting with the universe of all possible data points, start instead with the problem a customer has (see same bullet points above). Imagine interacting with the customer one-on-one. What questions would you ask that customer? That’s the data you need to get before investing in any technology to “bring it all together.”


At the risk of sounding excessively simple, beware there are probably multiple combinations of data points you could use to solve a customer’s problem (i.e. city, address, zip code, etc. with marital status, # of people in household, name, etc.). There are additional considerations to take into account such as cost, reach and whether or not the combination of data points aligns customers with your category or your company and differentiators. Not to mention ever-evolving regulations, security considerations and data privacy concerns.


To better understand how to prioritize data points to personalize content, read our case study from Clorox: Data Dimensions Prioritization Process (Gartner subs. required) or connect with our analyst team. For marketers looking at adopting technologies to enable personalization (or considering how to better prioritize related investments in light of COVID), read Part 2 of this blog series: The False Promise of a 360-Degree Customer View.

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