Creating a Persona Menu (for You)
I have been writing about the importance of using modeling techniques for personalization for some time now (refer to “Personalization Is About the Person” and “Segments vs. Personas”). If I may summarize the whole idea down to a 15-second pitch:
- We need modeling because we will never know everything about everybody, and;
- Selfishly for marketers, it is much simpler to assign personas to product groups and related contents than to have to deal with an obscene amount of customer data and a long list of content details at the same time.
Simply, personas are like menu items, each representing key characteristics of target customers that marketers need to know to push their products.
One may say, “Hey, I just put in SKU-level data into some personalization engine!” To which, I must ask, "Do you also put in unrefined oil into your beloved automobile?" I didn’t think so. Not that ruining some personalization engine will break anyone’s heart. But it may annoy the heck out of your customers by treating them as extensions of their immediate purchases, not as living, breathing human beings.
I’ve actually met someone from a software company at a conference who claimed to be able to create hundreds of thousands of combinations of SKU-level transaction data and content data. If you have a few hundred thousand SKUs and tens of thousands of pictures and creative items, well, the number of combinations will be quite large. Not exactly the number of stars in the universe, but quite unmanageable, enough for marketers to just “let go” and leave it all to the machine on a default setting. So, even if someone automated the process of combining such data (with some built-in rules, I’m sure), how would any marketer – and recipients of messages – make sense out of it all?
That type of shotgun approach is the mother of all of those annoying "personalizations," like offers of the very same items that you just purchased. For such rudimentary methods, it might actually be a great achievement to offer a yoga mat to someone who just bought a yoga mat. Hey, they are in the same category after all, categorically speaking, right?
The key to humanization of marketing messages is to make them about the customers, not about marketers, products or channels. And that kind of high-level personalization requires, well, a real human touch. That means, each block of information must be bite-sized so that human beings – i.e., marketers – can process and consume it easily.
When I first came to America (a long time ago), it wasn’t so easy to go through menu items in a typical diner. Too many items! How can I pick just “one” of those items that matches my appetite and mood of the day? Now imagine a menu that goes on for hundreds of thousands of lines. And you have to act fast on it, too.
Personas, or architypes as some may call them, are the bridges between obscene amounts of data points and yet another large set of pictures and content. The idea is to have a manageable number of personas to make it easier for us to match the right content to the right target.
I bet most content libraries are not crazy big, but large enough. But on that side, it is what it is. You will not cut out some valuable digital assets just because the inventory got big. So, we have to make the personal data – especially behavioral and transactional data – more compact to facilitate easy assignment, as in “Show this picture of a glass of red wine next to a juicy steak” to a persona called “Wine Enthusiast” or “Fine Dining.” The assignment itself would be as simple as saving a room for persona designation in the content library (if you don’t even have a content library, we need to talk).
Then, how would you come up with the right list of personas for “you”? Having done this a few times for many companies in various industries on a national level, I have some tips to share.
- Be Product-Centric: Anyone who has been reading my articles about personalization will be surprised by this one, as I have been screaming “customer-centric marketing” all along. But, in the end, we are doing all of this to sell more of our products to customers. Think about the products you want to push, then think about the types of characteristics that you would love to know about customers to push those products in a relevant way.
Trying to sell cutting-edge products? Then you may need personas such as “Early adopter.” Selling value-based items? You may want “Bargain-seekers.” Pushing travel items? Try “Frequent business traveler” or “Family vacation” personas. Dealing with high net-worth people? Well, go beyond simple income-select and try “Globetrotter,” “Luxury car,” “Heavy stock investor,” etc., depending on what you are selling. By the way, these luxury personas may or may not be related to one another, as human beings are much more complex than their income levels.
- Be Creative: Models can be built if you have data for “some” people who have actually behaved in a certain way to be used as targets. That limitation aside, you can be as creative you want to be.
For example, if you are in the telecommunications industry, expand the typical triple-play offering, and dig deeper into “why” people would need broadband service. Is it because someone is an “Avid gamer,” “Heavy VOIP user,” “Frequent international caller,” part of a “Big family,” “Home office worker” and/or “On-demand movie watcher”? If you can differentiate these traits, you don’t have to push broadband Internet services with brute force. You can now show reasons why they need over 100 megabits per second service.
If you are dealing with mostly female customers (who are, by the way, responsible for the bulk of economic activities on a national level), one can imagine categories that start with various health and beauty items, going all of the way to yoga and fitness personas. In between those, add any persona that is an ideal target for the products you are trying to sell, be it “Fashion enthusiast,” “Children’s interests,” “Gardening enthusiast,” “Organic food,” “Weight watchers,” Gourmet Cooking,” “Family entertainment,” etc., etc. The keys is to describe the buyer, not the product.
- Start Small, but be bolder as the list grows: In the beginning, you may have to prove that personalization using model-based personas really works. Yes, building a persona is as simple as building a propensity model (in essence, they are exactly those), but that doesn’t mean that you start the effort with 50 persons. Pick the product that you really want to push, or characteristics that you need to know in order to resonate with your core customers, and build a few personas as a starter (say five to 10). You may find some data limitations along the way, but as you go through the list, your team (or analytics partners) will definitely gain momentum.
Then you can be bold. I’ve seen retailers who routinely maintain over 100 personas for just one major product category. And I'll bet that list didn’t grow that big overnight, either.
Also, when you are in an expansion mode, just add items when in doubt. Think about the users of those personas, not mathematical differences among models. Do you know the difference between Kung Pao Chicken and Diced Chicken with Hot Peppers? Just peanuts on top. But restaurants have them both because customers expect to see them.
- Do Not Go Out of Control: When I was leading a product development team in a prominent data compiling company in the U.S., our team developed about 140 personas covering the entire country for various behavioral categories, including investment, travel, sports (both active participation and being a fan of), telecomm, donation, politics, etc. One of our competitors tried to copy that idea, and failed miserably. Why? It had built too many models.
For instance, if you are building personas for the cruise industry in general, you may need just “Luxury cruise” and “Family cruise” for starters. Those are good enough for initial prospecting. Then, if you must get deeper into cross-selling for coveted “onboard spending,” then you may get into “Adventure-seeker,” “Family entertainment,” “Gourmet,” “Wine enthusiast,” “Shopping expedition,” “Luxury entertainment,” “Silver years,” “Young parents,” etc., for customization of offers.
My old copycats with too many models had developed separate models for “each” cruise fleet and brand. How were they going to use all of that? One brand at a time, with one company as a user group? Why not build a custom model as needed, then? Surely that would be more effective if the model is to target a specific brand or fleet. Anyway, my competitors ended up building a few thousand models, for any known brand out there in every industry, seriously limiting the chance those personas would be used by marketers.
As I mentioned in the beginning, this is about matching offers (or content) to the right people at the right time. If you go out of control, it will be very difficult to do that kind of match-making. If your persona list is just big for the sake of being big, well, how is that any different from using the raw data? You’ve got to know when to stop, too. The key is “not too small, and not too big,” for humans and machines alike.
- Update Periodically: Like any menu, persona lists go out of date. Some items may not have been used actively. Some may become obsolete as business models and core product lines go through changes. And models do go stale, as well. You may not have to review this all of the time, and there will be staple menu items, like spaghetti with meatballs in a restaurant. But it will be prudent to go through the menu once in awhile. If not because of the product, then because of people’s attitudes about it changing.
- Evangelize: It would be a shame if the data and analytics people did all of this work and marketers didn’t use it fully. These personas are in essence mathematical summaries of “lots of” data in compact forms. They can be used in targeting (for selecting the right target for specific product offers), and for personalization of offers and messages based on dominant characteristic of the target (e.g., show different pictures to “Adventure-seeker” and “Family entertainment” personas, even if they are about to board the same ship). Continuously educate your fellow marketers that using personas is as easy as using any other type of data, except that they are compressed model scores with no missing values.
The personalization game is complex. It may look easy if you just buy an off-the-shelf personalization engine, set up some rules with unrefined data and let it run. While it's better than sending uniform message to everyone, that kind of rudimentary approach is far less than ideal, not to mention the annoyance factor.
To maximize the power of all available data and the personalization engine itself, we must compress the data in forms of personas. Resultant messaging will be far more relevant to your target audience as, for one, a persona is a built-in mechanism for the personal touch. If you set the menu up as a bridge between data and people, that is.
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 email@example.com.