Barriers to Personalization

Recently, I stumbled onto survey results from marketers regarding “data-related headaches,” published by a reputable source. What surprised me the most was not the list of the pain points, but the way marketers expressed the severity of pains. They collectively answered that “moving data among different silos” and “gaining a

Recently, I stumbled onto survey results from marketers regarding “data-related headaches,” published by a reputable source. What surprised me the most was not the list of the pain points, but the way marketers expressed the severity of pains. They collectively answered that “moving data among different silos” and “gaining a single customer view” gave them the most severe headaches, while “personalization” brought not-so-severe pain. That gave me an “oh, really?” moment. Then they put “contextualization” (of data, I assume) and “turning data into insights, and insights into actionable segments” right in the middle.

From a data and analytics specialist’s point of view, it seems like marketers have no idea where the pains originated. Simply, proper personalization is not possible without the 360-degree view of a customer and insights derived from the data. So, in my opinion, the severity list seems completely backward. And it is just unbelievable that marketers “think” that they are performing some type of personalization without much of a headache.

During the past few months, I have been emphasizing the importance of personalization in modern marketing (refer to “Personalization Is About the Person”), and data and analytical steps to achieve such goals (refer to “Road to Personalization”). I’ve said similar lines many times before, but let me repeat: Proper personalization is not possible without understanding the target individuals as people. If marketers are thinking that buying some fancy software and putting transaction- and event-level data through it are the end of their jobs, they cannot be more wrong. Such activity often leads to “personally annoying people,” not impressing customers with relevant messages. If they were to automate such a rudimentary practice? Well, they are going to end up annoying their customers and prospects on a regular basis.

If you as a marketer are having a hard time stomaching what I am saying here, please then take a look at your inbox, which is filled with irrelevant messages — as it is for a consumer. Aren’t they filled with the kinds that you would purge mercilessly, as in “highlight all, then delete”? How many messages are really relevant and timely to you? Maybe one out of 300 to 400? Even the ones that are based on some tidbits of information that you left behind purposefully or accidently become really annoying after the third time you see the same darn message stemming from them. Ok, I get that some marketers think that they know me, but could they please not overdo it by turning on some expensive personalization engine on an autopilot mode from day one?

As I emphasized in my previous columns, personalization is about the person. Putting event-level or transaction data into a personalization engine is like putting unrefined oil into a high-performance engine. Not a recommended course of action, for sure. And don’t blame the engineer when things break down, even though the salesperson who sold you that engine probably claimed that it would make all of your marketing dreams come true.

Regardless, I think we can safely agree that personalization must start with the data. Unfortunately, not all data are created equal or are of the same quality (refer to “Chicken or the Egg? Data or Analytics?”). In fact, most data are utterly inadequate for high-level personalization that does not annoy people. So yes, the fact that marketers think that creating a single customer view out of all types of data from different silos is indeed important and difficult is a good sign. A critical change always starts with the recognition of a problem. It is just that marketers should never think that personalization engines could magically help them skip that painful step of data hygiene and consolidation.

If the data management were the first hurdle on the way to decent personalization, then the second challenge that marketers often face would be the analytical part of the journey. Deriving insights out of data and turning such insights into actionable segments require advanced-level analytical skills. Here again, automated machines do not perform the human part of the equation. Some marketers may have procured some automated modeling engine (again, with much fanfare as a magical tool). But who will set the goals of models and define the target for each model (refer to “Data Deep Dive: The Art of Targeting”)? Who will connect the dots between resultant personas and segments to actual offers and messages that customers and prospects get to see?

Even for cases where marketers must respond to a customer’s need immediately (e.g., for buyers who are specifically looking for a specific product right now), the rules of engagement (i.e., customer journey mapping) must be set up based on clear business objectives, as well as mathematical equations. Humans, not so surprisingly, can smell the sign of not-humanness from miles away, through even digital channels.

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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