Creating a Persona Menu (for You)

Personas are like menu items, each representing key characteristics of target customers that marketers need to know to push their products.

persona
“93H,” Public Domain license. | Credit: Flickr by saul saulete

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.

  1. 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.

  1. 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.

  1. 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.

Similarly, there may be only slight differences between “Conservative Investor” and “Annuity Investor” personas. But the users of those personas may grab one or the other because of their targeting need at the moment. Or whatever inspired their marketing spirit. Think in terms of user-friendliness, not mathematical purity.

  1. 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.

  1. 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.
  2. 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.

Segments vs. Personas

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.

Tina ThrillseekerPersonalization 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:

Screen Shot 2016-06-08 at 11.16.08 AM

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.

Persona Marketing Tricks

How does a marketer go about creating the most effective set of personas? The first step is to create the 360-degree customer view out of available data. Personalization must be about the person, not about channel, product or even brand.

Personal.jpgHow does a marketer go about creating the most effective set of personas? The first step is to create the 360-degree customer view out of available data. Personalization must be about the person, not about channel, product or even brand.

For that, all event- and transaction-level data must be rearranged around the target individuals. Often, this data step turns out to be the first major hurdle for the marketers.

Then marketers, along with data scientists, should draw the list of required personas. After all, all analytical work must start with a clear definition of targets, and the targets must be set with clear business goals.

If you could ask for any personas for your marketing efforts, what would they be? Surely, the list would vary greatly depending on the lines of business that you are in. Obvious ones — such as “High-Value Customer,” “Frequent Shopper” or “Online Buyer” could be helpful for all types of retailers.

Going beyond that, marketers must expand their imaginations and think about the list from the customer’s point of view, while keeping a sight on the products and services that are to be offered to them. We must look at this as an ultimate “match-making” exercise between the buyers and the products, way more sophisticated than a rudimentary product-to-product level match (as in “If you purchased product A, you must also be interested in product B”).

The idea is to create personas imagining what you are going to do with them in marketing campaigns. “Frequent Flyer” maybe an obvious choice, but would you need a related but different one called “Frequent Business Traveler”? Would you extend the “Young Family” to “Avid Theme Park Visitors”? Why not both?

For B-to-B applications, we can think of many more along the lines of a “Consumable/Repeat Purchase” persona and “Big Ticket Items,” but the idea is to have both of them on the menu, as one may reveal both types of traits at the same time.

Similarly, if you are in a telecommunication business, what would be a good set of personas for broadband service? What type of personas can explain the “why” part of the equations? Simply for the sales of broadband, we can think of the following set as a starter:

  • Big Family
  • Home Office
  • High-Tech Professional
  • Avid Gamer
  • Avid Movie Downloader
  • Voice-over IP User
  • Frequent International Caller
  • Early Adopter
  • Etc., etc.

The key is matching the propensity of a customer and the product, and showing compelling reasons why they need to purchase a particular product. We all routinely consume all kinds of products and services, but each of us does it for different reasons. Personalizing the message based on known or inferred personal traits is the key to stand out in the age of over-communication.

Once we imagine the list, there are ways to build the personas. I can say that with conviction, as I’ve seen a persona called “NASCAR Fan” being used in an election season. So, don’t be shy and start being creative on your whiteboard today.