Personalization Framework

In the age of constant bombardment with marketing messages, staying relevant to prospects and customers is not just good practice in the manual; it is a matter of survival.

First, let’s divide the personalization efforts into outbound and inbound activities:

  • Outbound Personalization: This includes mail, email and any other media where each individual is targeted on a 1-on-1 basis. We have been calling that 1-to-1 marketing for some time now, and it stems all the way back to direct marketing days. Basically, this is a type of activity where the marketer must know to whom they are talking, and there is no excuse not using all available personalization techniques — including model-based personas coming from the 360-degree customer view. There is no excuse to employ anything less for both targeting (i.e., deciding whom to address) and personalization (i.e., knowing what to say and what to offer). We can do far better than using 1-dimensional segmentation techniques in this day of abundant data and computing power (refer to “Segments vs. Personas”).
  • Inbound Personalization: This is more complicated than the outbound one, as we can’t always identify who just landed on the website or entered the store. Even diligent marketers who follow best practices in Web or store activity management may not be able to identify all shoppers; in many cases not even half of them, and not in time, either. Therefore, we must look at inbound personalization in two separate ways, mostly due to the data availability factor:
  1. Target Identity Known: If a visitor signs in when browsing the website, then the marketer can bring in all historical transaction data, response data, as well as geo-demographic data. The same is true for mobile apps, as most are continuously linked to the user’s identity. Similarly, during an inbound call or an online chat, the caller’s ID may be revealed, though not guaranteed. In such cases, it is a matter of connecting the dots and bringing in relevant data points and modeled persona scores (or segment/clusters) as fast as possible. This is a game of speed calling for near real-time response, so having pre-developing personas based on readily cleaned up data is very important. Then, it becomes a matter of matching personas (or segments) to proper scripts, offers and products.

  2. Target Identity Unknown: This gets tricky, as marketers will not be able to rely on historical transaction data, modeled personas or other personally identifiable information (PII)-type data. Luckily, many companies — along with their smart developers — have been working on such an “in-the-moment” type of personalization for some time now. In fact in practice, such reaction-oriented personalization should be the first to check off, if we look at this effort from the sequential framework. Simply, if you know what the shoppers are looking for, just usher them to right aisle. If the intention is not explicitly revealed, then we have to get into agile and real-time version of predictive modeling, with all available data up to that point (such as location, device, source, day-part, immediate browsing history, etc.).

Obviously, there is no need for pre-built personas (or segments) or real-time modeling if the intention is clearly revealed (as in “I want a pair of men’s dress shoes, Size 10, black or dark brown”). The trouble is that such reactionary personalization is not the end of it, as marketers don’t always get to know what their customers are looking for.

In the interest of “personalizing all messages through all channels all of the time,” we must look at other not-so-explicit data and make the most out of them using statistical modeling techniques. Even when “some” explicit data are available, personalized messages become far more colorful — and relevant — when used in conjunction with personas and segments. Because shoppers behave in contexts, not in isolation.

Personalization is complicated, because it is all about human interaction. Even for seemingly one-way communication, such as email or mailing campaigns, marketers must consider the relevancy factor at all times. And such relevancy comes from the right context; which, in turn, comes from the combination of past and present views of the target individual.

I introduced a new framework, as available data drastically change based on the stage of this personalization game. We should never look at it from the perspective of technical difficulty or complexity, as that type of mentality would lead us to the rudimentary methods (i.e., habitually resorting back to the default setting of a commercial personalization engine). And when you do that, you may end up personally annoying your customers, not really personalizing by staying relevant at all times.

Lightening Up a Bit

I don’t mean to scare every marketer, but there will be different outcomes for marketers who fully embrace personalization and those who don’t.

A sense of urgency — leading to a firm commitment for serious personalization efforts — is exactly what is required today.

Data and technology are at our disposal already, so commitment to connect dots between piles of data and recipients of messages will get marketers to the goal line. Besides, why do marketers worry so much about technical difficulties? That is the job for the techies and data geeks, isn’t it?

Author: Stephen H. Yu

Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at

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