Niche and Prosper
By Mark Frauenfelder, Thu Dec 09 08:45:00 GMT 2004

Mobile marketing guru Tomi T. Ahonen on sub-segmentation, the limitations of demographics and the power of self-organizing maps.


When it comes to knowing the needs and desires of mobile customers, Tomi T. Ahonen is like a walking search engine. In the mid-1990s, Ahonen started Nokia's global 3G business consulting department and oversaw the company's 3G research center. On publication of his first book, Services for UMTS: Creating Killer Applications in 3G, in 2002, Ahonen set up his own consultancy, retaining Nokia as a key customer, along with Ericsson and many mobile operators.

With the release of his third book, 3G Marketing: New Strategic Partnerships, in July, Ahonen shed light on 3G service creation, distribution, management, branding, sales and promotion. In this latest book, he also delves deeply into customer intelligence and segmentation. We spoke with Ahonen to find out more about his views on the subject.

TheFeature: What are mobile companies doing to improve customer segmentation?

Ahonen: Mobile operators are trying to move from a traditional segmentation model that has ten or fewer segments. If you were an automobile manufacturer, that would be like offering one van, which is green; one sedan, which is blue; one convertible, which is red; and a pick-up truck, which is white a total of ten vehicles. You don't get the option of a diesel or gasoline engine, four doors or two doors, or automatic or manual transmission. Ten models total. You go to the lot, and that's it they say, "Oh, you're married you get the station wagon." That's the level we're at in the mobile industry. Now operators are growing [their model] to 30 to 100 segments.

TheFeature: Has that helped?

Ahonen: Yes, but they are not taking advantage of the real information they have. Very typically, they bring in demographics, which makes sense if you don't have much information about your customers. But mobile operators actually collect a fantastic amount of real data on real customers.

Let's look at two 30-year-old women, who are both secretaries at a major corporation. They live in the suburbs, they're neighbors, they're married, they have three children, they're college grads and their husbands work. They have similar mobile phone use. But suddenly one of them starts behaving like an 18-year-old on the mobile phone she starts sending text messages and using chat and flirting services. The reason is that she has separated from her husband and has started dating again. Demographically, they are identical, but they are completely different in terms of behavior.

TheFeature: So what's the solution?

Ahonen: In the mobile business, we have perfect information on what a person does with their device every minute. And because we get immediate indications from our systems to suggest when that behavior is changing, we can also study how customer segments evolve. This means we can develop a deep customer understanding, not just a generic demographic profile, but actual behavior. Now we can see, for example, when a customer has moved from voice-only to text message-based communication.

All operators collect this information automatically they have to for billing purposes. Mobile customers generate the world's greatest amount of information. The problem for the mobile operator is that there is too much data. Theoretically, we could hire a staff of sociologists, psychologists and statisticians to go through everything and try to build some profiles, but you'd only get the tip of the iceberg.

In the late 1990s, operators tried to solve this with data mining. Some types of profiles can be developed that way, but it's usually one-dimensional. We can find out things like which customers provide the biggest amount of money for us, or which ones are most likely to remain loyal, but the data being collected from a user has about 60 different parameters. If we want to develop a three- or four-dimensional analysis profile of our customer database and run it across millions of subscribers, we can't. There's no computer in the world that can handle this. Supercomputers theoretically could, but there's not the money or software to go after it.

TheFeature: Is there a practical way to analyze all this data?

Ahonen: There is a type of mathematics called neural networking. And there's something called the "self-organizing map" (SOM). SOMs will find patterns in enormous amounts of data. The SOM itself is stupid: it has no idea why this part of the map is green, and that corner is yellow, and that part in the middle is red. You need analysts to ask: "Why did the SOM say that this part is green and this part is yellow? Ah! These people are now very heavy users between each other, and these people have only a few different contacts, but there's a massive amount of traffic between them. These people here receive traffic, but don't really originate much." Recognizing the differences between what makes one part of the map a different color to another is where we need the human input. We come up with an absolutely fantastic understanding of real-life usage patterns.

TheFeature: What are some examples of the segments that have been found with the assistance of SOMs?

Ahonen: One of the early things that came out was the concept of the "Alpha User" a person who teaches everyone else how to use communication technology. The Alpha User is that person who taught you how to send a text message or a picture message. Someone either sat with you and taught you how, or else you are an Alpha User. The Alpha User concept is about two years old.

Going further with SOMs leads to some real insight. Recently, they've shattered the myth about second subscriptions. We had assumed that a person who gets a second or third phone is doing it to reduce their overall costs. One network might offer good weekend prices, another offers good weekday prices, one gives good prices on text messages and data access. But when analyzing customer usage patterns using SOMs leads to very revealing information: the person who is first to get a second mobile phone isn't a 35-year-old businessperson who wants to keep costs down. No, it's a young person who has his or her first job. Every person in high school or college has a mobile phone already, but when the first paycheck comes, they want the coolest phone ever. Their total phone bill more than doubles. This is because their spending is no longer restricted.

This kind of data has only now become available. When we start to analyze this we notice a pattern that doesn't fit the conventional wisdom. All at once, we get a really deep understanding of what subscribers are doing, what we offer them and how we can then move them further along.