Personalization may mean different things to marketers, but we may break it down to, one, reacting to what you specifically know about the target and, two, proactively personalizing messages and offers based on both explicit and implicit data.
The first one is more like “OK, the target prospect is clicking a whole a lot in the hiking gear section, so show him more related products right now.” This type of activity requires technical know-how regarding Web and mobile display techniques, and there are lots of big and small companies that specialize in that arena. Simply put, what good is all this talk about data and analytics, if one doesn’t know how to display personalized messages to the target customer? If you “know” that the customer is looking for hiking gear, by any means, usher him to the proper section. There are plenty of commercial versions of “product-to-product” matching algorithms available, too. We can dissect the data trail that the consumer left behind later.
All those transaction data trails become integral parts of the “Customer-360” (yet another buzzword of the day). Once that type of customer-centric view (a must for proper personalization) becomes a reality, however, marketers often realize “Oh jeez, we really do not know everything about everyone.” That is when the analytics must get into a higher gear, as we need to project what is known to us to the unknown territory, effectively filling in the gaps in the data. I’d say that is the single most important function of statistical modeling in the age of abundant, but never complete data — a state of omnipotence that we will never reach.
Then the next natural question is how we are going to fill in such gaps? In such situations, many marketers jump into an autopilot mode to use what we have been calling “segmentation” since the ’70s and ’80s (depending on how advanced one was back then). But is it still a desirable behavior in this day and age?
As “data-driven” personalization goes, no, using a segmentation technique is not a bad thing at all. It is heck of a lot more effective than using raw data for customized messaging. As a consumer, we all laugh at some ridiculous product suggestions, even by so-called reputable merchants, and that happens because they often enter raw SKU-level data into some commercial personalization engines.
If we get to have access to segments called “rich and comfortable retirees” or “young and upcoming professionals,” why not make the most of them? We can certainly use such information to personalize our offers and messages. It is just that we can do a lot better than that now.
The traditional segmentation technique has its limitations, as it tends to pin the target into one segment at a time. Surely, we all somewhat look like our neighbors, but are we so predictably uniform? Why should anyone be pigeonholed into one segment, and be labeled along with millions of others in that group? Even for rich and prestigious-sounding segments, it may be insulting to treat every member equally, as if they all enjoy the same type of luxury travel and put their money into the same investment vehicles. Simply put, in the real world, they do not.
Every individual possesses multiple dominant characteristics. For that reason alone, it is much more prudent to develop multiple personas and line them around the target consumer. The idea is the opposite of “group them first, and label them later”-type segmentation. It is more like “Build separate personas for all relevant behaviors, then find dominant characteristics for one person at a time.” With modeling techniques and modern computing power, we can certainly do that. There already are retailers who routinely use more than 100 personas for personalized campaigns and treatments.
The following chart compares traditional clustering/segmentation techniques to model-based personas:
This segment vs. persona question comes up every time I talk about analytics-based personalization. It is understandable, as segmentation is an age-old technique with long mileage. Marketers feel comfortable around the concept, as segments have been the common language among creative types, IT folks and geeky analytical kinds. But I must point out that the segments are primarily designed for “general” message groups, not for individual-level personalization with wider varieties.
Plus, as I described in the chart, personas are more updatable, as they are much more agile than a clunky segmentation tool. I’ve seen segmentation tools that boast of more than 70 to 90 segments. But the more specific they become, the harder it is to update all of those with any consistency.
Conversely, personas are built for one behavior/propensity at a time, so it is much easier to update and maintain them. If the model scores seem to be drifting away from the original validation, just update the problematic ones, not the whole menu.
In the end, the personalization game is about which message and product offer resonates with the customers better. Without even talking about technical details, we know that more agile and flexible tools would have advantages in that game. And as I mentioned many times in this series, matching the right product and offer to the right person is a job anyone can do without a degree in mathematics. Just bring your common sense and let your imagination fly. After all, that is how copywriters imagine their target; by looking at the segment descriptions. That part isn’t any different from looking at the descriptions of personas instead; you will just have more flexibility in that matchmaking business.