What you’ll get from this post: An overview of why one-to-few personalization is more likely to be profitable than one-to-one as well as five core requirements and questions to ask to assess the potential value of segments.
Estimated reading time: 4 minutes; approximately 750 words.
A recent industry survey found 78 percent of respondents agree or strongly agree it’s important to “try to differentiate through customer experience,” yet we know from our own data that fewer than 49 percent of companies have actually implemented some kind of personalization solution. There’s clearly a desire for personalization but currently, there’s little action toward making it a reality. One reason for this comes from the daunting nature of personalization as it is often described.
In thinking about personalization, it helps to frame the discussion within the context of the “trichotomy of problems.” Personalization can be used to describe a one-to-most experience—the result of simple, broad segmentation—which is easy but not particularly effective. More commonly, personalization is user to describe a one-to-one experience, which is very difficult to achieve—if not impossible—and presents a questionable return on the required investment. Somewhere in the middle is a one-to-few approach. This type of personalization is difficult but attainable and the added value over simple segmentation is clear.
One-to-few personalization is to be appropriately tailored, providing tangible benefits to the consumer, but wide enough to actually be implemented profitably by the organization. Focusing on a few highly tailored groups allows personalization to be both effective and profitable.
Determining the total number and makeup of the groups is a challenge that can be solved mathematically through methods like cluster analysis. While this advanced method requires a knowledgeable analyst, any manager to assess the potential value of group can use a more qualitative approach built around five core requirements, summarized with the acronym ADAMS.
When it comes to websites, questions of whether it’s possible to reach a certain group rarely arise. When it comes to personalization, however, it’s important to ensure you can actually get your unique message in front of each group that you are targeting when and where the message will be most effective.
Ask this question: Is it possible to reach each group efficiently?
On the surface, it may seem obvious that the various groups should be different from one another. However, it’s important to determine not just whether groups have different characteristics, but whether they would actually respond differently to different campaigns. If the responses are not expected to be different, the effort of personalization isn’t worthwhile.
Ask this question: Would all groups actually respond differently if exposed to different campaigns?
When targeting different groups with personalized campaigns, you must ensure that there is actually a relevant product available that is capable of fitting their needs. If you’re a clothing retailer, for example, and you target two different groups but offer the same pair of jeans, then your personalization strategy may be too granular.
Ask this question: Do you have a product to fit the needs of each group that you identified?
If you’re going to use the resources required to execute a personalization strategy, you must be able to determine whether these efforts are beneficial. Proving a positive ROI, after all, is the best—and perhaps the only—way to sustain your program and justify its place in the budget. The only real way to measure this impact is through experimentation; specifically a series of A/B tests that split a segment of traffic between personalized and generic treatments. This method provides a clear, independent measure of your campaign’s impact.
Ask this question: Can the impact of personalizing to each group be measured?
If one group only contains a small subset of users, it may not be worth it to personalize to them—even if there are large differences between the groups. Projecting whether the lift in revenue over a certain period will cover the resources necessary to create the additional content and then operationalize the experience is a critical cost-benefit analysis that should be performed before any personalization campaign is launched.
Ask this question: Are the groups large or valuable enough to warrant the expense of personalization?
Personalization has been slow to gain meaningful adoption. Thinking about it in terms of a one-to-one solution portrays it as an insurmountable hurdle and has likely contributed to the slow growth. Focusing on potential for profit first, however, over theoretical ideals, allows marketers to embrace a more practical model of personalization. When approached carefully—through evaluation using the ADAMS model first, and statistical methods like cluster analysis after—one-to-few personalization can provide a route more relevant, profitable experiences online.
Want to learn more about making personalization profitable? Get access to this recent webinar recording.
Reid Bryant is a data scientist at Brooks Bell. He uses advanced analytics and applied statistics to create data models, refine methodology, and generate deep insights from test results. Reid holds a master of science in analytics degree from the Institute for Advanced Analytics at North Carolina State University.
Brooks Bell helps top brands profit from A/B testing, through end-to-end testing, personalization, and optimization services. We work with clients to effectively leverage data, creating a better understanding of customer segments and leading to more relevant digital customer experiences while maximizing ROI for optimization programs. Find out more about our services.