The Promise and Peril of Personalization at Scale

Marketers in virtually all kinds of companies are now intensely focused on improving the quality of the customer experiences their companies provide. Most marketers believe that the ability to personalize marketing offers and messages for individual customers at every touch point is critical to delivering outstanding customer experiences.

Numerous research studies have shown that personalized marketing can be highly effective. For example, in a 2016 survey of more than 1,500 US and UK consumers by Accenture Interactive, almost two-thirds (65%) of respondents said they are more likely to make a purchase from a retailer that sends them relevant and personalized offers. But there are also many well-documented examples of personalized marketing efforts that have failed miserably because they were clumsily executed, or because they were seen as a "creepy" invasion of privacy.

The ability to "personalize at scale" is highly dependent on data and technology. Many companies are already using data and predictive analytics technologies to automatically generate personalized marketing messages without human involvement, and many marketers believe that this practice will only become more commonplace. In fact, it's difficult to envision how personalization at scale can be achieved without a heavy reliance on data and technology.

But this creates a conundrum for marketers that I contend isn't fully appreciated. Automated, data-driven personalization will make personalization at scale more achievable, but it also increases the danger of getting personalization wrong.

An article by Charles Duhigg in The New York Times Magazine provides a great illustration of both the promise and the peril of automated, data-driven personalization. In this article, Mr. Duhigg describes how personalized marketing caused an unwitting father to discover that his teenage daughter was pregnant.

When I decided to write about this topic, my intention was to briefly summarize the content of Mr. Duhigg's article, but I quickly determined that a brief summary simply would not do justice to the subject matter of the article. I strongly recommend that you take the time to read the entire article if you are involved in developing personalized marketing programs.

For me, there are three important takeaways from the article.

• First, data and predictive analytics can enable marketers to develop marketing messages and offers that are highly relevant for individual customers and prospects. 
• Second, relevance alone is not enough to ensure that a personalized marketing program will be successful. It takes sound human judgment to evaluate factors that data and analytics simply cannot capture. 
• And third, sometimes it is more effective to make marketing messages less personalized, particularly when the subject matter touches a highly personal or otherwise sensitive topic.

(Note:  Mr. Duhigg's article is fairly long. The beginning and ending portions of the article contain the material that pertains directly to personalized marketing. The middle part of the article focuses on the power of habit in human decision making. The entire article is well worth reading.)

Image courtesy of Josh Hallett via Flickr CC.