Case studies on cohorting

MM:  Would you just walk us through one or two case studies?

TC: Yes. We have some well-known clients for which we’ve done some great work, such as P&G, GlaxoSmithKline, Pacific Gas & Electric and American Honda, and in the area of consumer segmentation or cohorting-an area for which Targetbase is most well-known.

MM: Would you give us a quick primer on cohorting?

Certainly. If you look at our packaged goods clients like P&G and General Mills, they have a tremendous portfolio of brands. P&G came to us many years ago with the question of how they could leverage consumer insights-a better understanding of their consumers and what mix of brands to promote to each person on their very large database.

This resulted in turning the old model of brand marketing on its head: it was not about the brand; it’s about the consumer. They asked us to develop a mix of brands that a consumer would be interested in-that P&G has a right to win.

We often find among large portfolios of brands natural groupings or segments of consumers-what we refer to often times as a “cohort”—a group of consumers that tend to utilize a group of brands or a mix of brands in a particular way.

Originally for P&G and later for General Mills and others, we developed a methodology for identifying those unique segments or cohorts within their consumer base.

We identified them in such a way that P&G and General Mills could then know not only who they are from a behavioral and attitudinal standpoint, but then what products they were likely to be interested in…thus how o drive relevant messaging, offers, etc. to optimize their sales at the individual consumer level.

Joe Six-Pack and Hockey Moms

MM: Could you give expand your definition of a cohort? We just went through a election, where candidates and pundits used cohort-like terms of Joe Six-Pack and Hockey Moms.

Yes. Absolutely. There are certainly broad conventions. But one of the services that we offer our clients is a truly customized approach to segmentation and cohorting, specific to their brands and customers-unique differences.

There are certainly themes that crop up on a regular basis, but it is actually a customized approach — as opposed to, say, the traditional prism cluster type of approach. Where every neighborhood in the United States is dubbed a particular name, to fit within that prism cluster.

It is certainly consumer-centric, but it’s in the context of a particular brand or group of brands.

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Integrated information for policy-managed decisions

MM: It seems that as your solution evolves to include WiFi Max networks and 3G phones—such as the iPhone—these mobile Internet connected devices become points of control of an entire industry, almost like the channel changer for a TV; it’s becoming the control system for these very sophisticated applications.

MB: I think of the world of mobile devices as a great way to give freedom to people who otherwise have to be slaves to the careful tending of systems and so forth. In that sense, they’re very freeing.

If you take the kinds of monitoring and management application that people want as a business intelligence solution and simply display it to them on a mobile device, you’re not going to be doing them any favors. You’re just changing the location at which they have to do a piece of work, where they look at a screen, make a business decision and so forth. It might give them some location freedom, but there’s a lot more potential out there for the activity you have to do, from the mobile perspective—to be a higher level of monitoring. You automate the decision-making at the lower level.

Today, let’s say you’re looking at a sales margin inventory kind of report. You say, “Gee. Here’s a product that I have very low inventory of, and I happen to be selling a lot of it. Gee. It’s selling at high margins. I guess I should reorder that.”

Of course, the system should just reorder that for you.

Today, people struggle just to get all that information on one line. So they can see that the problem is actually there. The next generation of systems will be ones directed at business rules that will help people automate the solutions. It’s what we call “operational business intelligence,” where triggers and tripwires and things of that sort can notice characteristics of the data in the enterprise, and can take actions.

Then from their favorite mobile device, people can make sure that the decision-making that’s happening for them is not going off the rails for some unforeseen reason. Instead of having to switch every switch on the train, you just have to see that the trains are all moving in a reasonable way.

I think the future will lead to integrated information properly displayed for human decision-making, to support of that human decision-making.

MM: And eventually, I guess, we get into policy-managed processes that basically report back to you that, “Hey. I did this. Is that okay?”

MB: Once you have integrated information, the sky is the limit with what you can do with it. Integrating the information and presenting it in a reasonable model for people has been the bottleneck and remains the bottleneck today.

MM: Well, that sounds like a great place to conclude. Thanks very much.

Nickels and dimes

MM: That was one of the things that really came through in your talk, Mike. First of all, you were approaching these applications that you call “diagonal applications,” really almost as a value chain optimization suite. So you’re looking not just at one business, but rather at how to optimize an entire value chain—irrespective of your location in that value chain.

MB:  I guess that’s a way to look at it. There is a collection of these Diagonal BI applications. We’ve tried to package a number of them in modules that can be sold to particular industries.

MM: There was another thing that was remarkable in your presentation. That was the ability for using all of these kinds of hidden charges in the trucking area. There were some terms that you used, but they referred to—basically—”hidden” markups.

MB: Yes. That actually brings me full circle to the point I started to make at the beginning of the interview here. That was about “tools versus solutions.”

When I said Oco is a provider of business intelligence solutions, well—every business intelligence provider will tell you they’re providing solutions. The question is “solutions for whom?”

If you’re a data analyst, then a data analyst tool is a solution to your problem. At Oco we’re trying to provide a solution to business users for a transportation cost minimization problem—as our example here. That application goes to the eyes of the business user—not to the eyes of a data analyst. It’s intended for use directly by the people that are in the trenches who need that information. That’s why I stress that it’s a solution.

MM: The nickels and dimes, to use a metaphor. Right?

MB: Yes. Well, because it really adds up. That’s the problem. This is part of the reason why summarized data cubes and so forth have given way to, customers saying, “I need to be able to drill down to the actual data.”

In a summarized data cube, you would just roll up all the accessorial charges noted above. If instead you can actually see what’s happening at the individual bills-of-lading of the trucks, you can spot many of the problems and identify the carriers charging more than others, and so forth—even though the line-haul charge which is the advertised cost of the shipping, is the same.

MM: I refer to these as “carbon monoxide expense items.” Carbon monoxide constitutes an odorless gas that you can’t see, touch or smell. But you know you have it because you have a headache. And if you’re in a cave, you know the canary dies.

MB: Yes. These are, in some sense, ways for people to slide charges in on you.

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SOA Value Chains?

PvT: Who are the prime contributors to the development and support of an operational marketing and service innovation platform? And how did you start researching the technical ecosystem—what you and I now call engagement marketspace?

We started in 1995 with digital asset management and content management because no matter what else came along, you must have a media and content under management.

In 2000, we started investigating another class of vendors in the marketing automation, MRM, and marketing operations management space. Some of the vendors have make great progress.

With rare exception, they all still need to better understand DAM and, more the point, metadata management—a database and DBA for logging and tracking enterprise metadata as instantiated in all enterprise databases, including ERP and CRM, as a strategic asset.

Since 2004, we have tracked vendors that come from the CRM, business intelligence, and process analytics space.

For the last three or so years, we have tried to understand firms in marketing service provider and data enrichment vendors—lots to cover!

Of course there are whole sets of vendors in dynamic messaging and email management content space, and in the customer experience management space too/

As I stated before, there’s many different technology vectors in the marketing and innovation value chain, that ultimately support the idea of an innovation-services platform.

This calls attention to, however, the critical need for leadership within marketing to have a services integration framework and an underlying Service Oriented Architecture (SOA) enabling this integration framework. IBM does some great work there with its component business models—what I call CIO blueprints.

Services integration

However, the senior marketing executive, not the CIO, must commission and own the services integration framework—it basically specifies in one wall-mounted poster all of the services – marketing and innovation-related services – of the business eco-system from which the firm will build, buy, or rent technology or engagement services over the next five years.

Now, the CIO blueprint represent an living, evolving visual depiction of one thing: how firm intends provision services needed attracting, serving, and keeping profitable customers for life.

The CIO blueprint also makes explicit how the firm intends to marshal the resources of a global business eco-system: ‘Here’s what we bring to the customer experience. Here’s what our partners bring, and here’s how it all integrate to an end-to-end process of customer-making.

PvT: I guess that repositions marketing automation a bit player in a larger play?

MM: Well, I don’t think that the rubric of marketing automation delivers useful distinction anymore. I don’t like the term “marketing automation” because many of the research firms and vendors have abused the term, rendering it useless.

Rather, I would like to speak about marketing in terms of process maturities, and levels of process maturity for a marketing operation.

Again, the senior executive doesn’t really care about technology or marketing automation, per se, he or she is most concerned with operational capabilities and building or enhancing capabilities which will related directly to a process maturity model for marketing operation.

However, this all underscores a very strategic point: business rules and metadata enable orchestration of the technologies and processes of how firms attract, serve, and keep customers for life. Very, very few technology vendors deliver solutions for orchestrating the customer engagement life cycle. Typically, the missed or underplay the role of three SOA capabilities: digital asset management, metadata management, and marketing claims management.

Marketing claims management

This last one, marketing claims management, entails a end-to-end workflow for developing and publishing approved copywritten material—product or service claims—to a specialize XML database publishing system. I use the term broadly to include anything written, formatted, and published in printed collateral, business communications, web sites, interactive detailing or presentation systems, catalogs, microsites, newsletters, etc.

In my view of the world, marketing claims management represents a subsystem of DAM and metadata management—that in turn represent subsystems of master data management.

And all of which requires a IT governance scheme—systems, processes, and accountabilities for researching, acquiring or developing, deploying, provisioning, managing, and retiring the technologies used to attract, serve, and keep customers for life!

Key point: tomorrow’s CMOs are mid-level IT executives today getting their masters in Business Administration or Media Psychology.

Decision making versus predictive analysis

MM: Mike—traditionally, again, I come from a background of database marketing, specifically, dealing with really large data stores. The fact is that most relational databases and most business intelligence tools are not really good about drilling down on a what-if heuristic. Then, modeling—if I have these demographic or psychographic factors in my database, how many people does that represent?

The notion of being able to drill down into very specific sets, and almost do a little simulation in terms of the ability to access that group by e-mail, direct mail or whatever? That typically gave rise to specialized analytic databases, to specifically a deal with that kind of ability to drill down.

Could you just give us a quick reprise of both of those database strategies—and then get into the compare-and-contrast of them?

MB: I think the simplest way to understand it is that the relational databases were originally designed around transactional processing—the kinds of things that an ERP system needs to do. They are row-oriented. You’ll have a customer table and a customer buys something. So you put an order into the order table and there’s one row for each order, or probably each line in each order.

The whole approach is organized around the notion that there are entries—which are rows. They’re created in response to transactions. Transaction processing is the primary activity.

Then people started trying to use this to do data warehousing, where the workload is much more analytical, answering questions like ”find all the customers with these characteristics,” and so forth. And this required organizing the data in certain ways to support analysis and decision making versus transaction processing.

At first, people took the databases that were organized around transaction processing, and started trying to use them in different ways—to index them differently and so forth.

Then, many databases that centered around decision support entered the market. Teradata was really the first one. But there have been many since then that have entered with decision-support workloads in mind. There continues to be all kinds of interesting innovations in the database market.

The relational database market is around 30 years old. It should be mature by now, but every year there seems to be new innovations in the relational database space. I’m always astounded that there continues to be new entrants. There’s a whole slugfest among new entrants for who will have the crown of the TPC— the Transaction Processing Council benchmarks. The TPC publishes benchmarks, and they label them Benchmark A, Benchmark B. They’re down to Benchmark H now. This is a decision-support benchmark. It’s really quite a good benchmark, because it measures not only how good the database is, and how fast the database can answer the question—but how expensive it is at doing that based on a cost and a cost-performance type of metric. It’s interesting that these small vendors are continually displacing each other on the top of the heap for that benchmark and it illustrates the continuing innovation that is occurring.

The columnar or column-oriented databases are databases that are organized in order to be able to handle these decision-support workloads optimally. There are quite a few of them on the market now.

But Michael, you asked the question about “what if,” analysis, as well. The columnar databases and the decision-support optimized databases are very good at answering questions like, “find people that have these interesting characteristics.” One needs to feed a very complex SQL query to find them, and these databases can very rapidly extract and produce that result set.

But there’s a big difference between this type of query and predictive behavior addressing questions like, “If this was true, what would happen then?” These kinds of things get you into the world of data mining and predictive modeling. These types of things are—to my knowledge— not yet embedded in the databases.

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