Marketers in virtually all kinds of companies have become intensely focused on improving the quality of the customer experience. Most marketers have long believed that personalizing offers and messages for individual customers is critical to delivering outstanding customer experiences. As a result, marketers are continuing to make personalization a major priority.
In the 2017 Digital Trends study by Econsultancy (published in association with Adobe), survey respondents identified targeting and personalization as one of their top three “digital-related” priorities for 2017, and 51% of company respondents said they will increase their spending on personalization this year.
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.
It’s also clear, however, that many customers aren’t particularly impressed by the personalization efforts they encounter. For example, in a 2014 survey by Adobe, 71% of consumers said they like receiving personalized offers, but 20% reported that offers are not done well, and another 20% said that personalization efforts are too intrusive.
For several years, the most common way to personalize a marketing message has been to include specific facts about the recipient – name, job title, company affiliation, etc. – in the message. I call this explicit or overt personalization. It’s as if we marketers believe that the effectiveness of personalization comes from telling the customer or prospect what we know about him or her. That may have been true in the past when personalization was still novel, but today, most types of overt personalization are not effective.
There are, of course, notable exceptions. For example, I find the product recommendations made by Amazon to be useful and valuable. They frequently alert me to the availability of recently-published books that I didn’t know about. Amazon’s website clearly states that recommendations for me are based on the items I have previously purchased or the items I have recently viewed, and Amazon even enables me to customize the factors that are used to create the recommendations. This is an example of explicit personalization that is still very effective.
In most cases, however, the best way to personalize a marketing message or offer is to make the personalization invisible to the recipient. What our customers and prospects really want are offers and messages that are relevant to their interests and needs. So we marketers need to stop telling our customers and prospects what we know about them and start using that knowledge to craft marketing content that provides them real value and utility.
Invisible (or at least “non-obvious”) personalization can also alleviate some of privacy concerns that are associated with personalized marketing. When CEB recently asked a panel of nearly 400 consumers how “online ads that use details about what I have done” make them feel, almost three-fourths (73%) of the responses were negative, and almost half (49%) used synonyms for “creepy.”
I can use a real-world example to illustrate why marketers must be sensitive to using personalized marketing messages or offers that are too explicit or obvious. (Note: What follows is based on a great article by Charles Duhigg that appeared in The New York Times Magazine. I strongly recommend that you read Mr. Duhigg’s article if you’re involved in developing personalized marketing programs.)
Several years ago, a statistician at Target (the department store chain) developed a data model that could predict which of Target’s customers is likely to be pregnant. Target’s marketers wanted this information because they believed if they could entice a pregnant customer to purchase pregnancy- and baby-related products at Target, she would also buy other products and form the habit of shopping at Target. Specifically, the marketers wanted to send personalized offers to women in their second trimester, which is when expectant mothers begin to purchase all sorts of pregnancy- and baby-related products.
Target had a baby-shower registry, and the statistician analyzed how purchasing patterns changed as a woman approached her due date, which women disclosed when they signed up for the registry. The statistician was able to identify about 25 products that, when analyzed together, gave a strong indication of pregnancy. More important, the model could estimate a woman’s due date with a fair degree of accuracy.
The statistician applied the data model to regular female shoppers in the customer database, and the result was a list of thousands of women who were likely to be pregnant. Target’s marketers were very happy to have this information, but they were also perplexed about how to use it. Specifically, they were concerned about how a customer would react if she received marketing offers that expressly mentioned the pregnancy or otherwise indicated that Target “knew” she was pregnant, even if she had never told Target about the pregnancy.
To address this problem, Target’s marketers sent customers who were likely to be pregnant personalized offers (usually in the form of an ad booklet) that included pregnancy- and baby-related products, but also included products that were completely unrelated to pregnancy and babies. This approach made it appear that the products had been chosen at random, and the customer didn’t feel like Target was invading her privacy.
This example makes three important points:
- First, data and predictive analytics can enable marketers to develop marketing messages and offers that are highly relevant for individual customers.
- Second, relevance alone is not enough to ensure that personalized marketing messages will be effective and well-received.
- And third, sometimes it is more effective to make marketing messages less obviously personalized, particularly when the subject matter touches a highly personal or otherwise sensitive topic.
Image courtesy of Lisa Lowan via Flickr CC.