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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Michael Moon (MM) conducts masterclass interview with Trae Clevenger (TC), VP, Analytics, a master practitioner of multichannel analytics at Targetbase, a customer engagement agency.

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Professional background

MM: We’re here with Trae Clevenger of Targetbase. Trae, would describe your current position and a few career highlights?

TC: Sure. I’m currently VP of Analytics at Targetbase, traditionally a direct-marketing agency.

At Targetbase I focus on innovation, helping to drive innovation across clients, particularly in the areas of analytics and digital marketing. As an integrated marketing agency, we focus on outcomes; most of our approaches and innovation impact technology and creative, or content.

My career spans multiple industries, many in Fortune 500 companies, in the areas of strategy, analytics and solution delivery, emphasizing statistical analysis and modeling, predictive analytics, segmentation, optimization, web analytics, relationship marketing, engagement analytics and behavioral targeting.

About Targetbase

MM: Would you describe Targetbase, its typical clients, and solutions?

TC: Sure. Founded in 1979 as a spinoff of MARC Research, today Targetbase has a long-standing market research and consumer research heritage.

Our analytics foundation remains at the core of everything we do down through the years.

Our business model—if you think of a hub-and-spoke—put analytics and insight at our core as the hub with strategic outputs and outcomes as spokes, producing technology, creative, and strategy that we believe delivers superior consumer insight

Our clients span the global and cross many industries, including Travel, Finance, Insurance, Pharma, Healthcare, Automotive, Utilities, Packaged Goods, and Retail.

The length of our client relationships represents one of the unique more things about Targetbase. Most of our clients view as a trusted, strategic partner (as opposed to a vendor or agency). Our clients value their relationship and tend to maintain it over the long term.

Concerning solutions, we pride ourselves on integration. We provide strategy, analytics, technology, and creative. However, the real differentiator is how all of those pieces come together to provide superior solutions for our clients.

For example, technology and analytics combine to produce more actionable business intelligence. Creative and technology combine to produce dynamic, customized content delivery. Analytics and Creative combine to produce communication planning and more targeted, impactful content.

We often use the phrase “Database. Digital. Direct.” So much of what we’re doing these days is increasingly in the digital space and the online world, using that term broadly, to incorporate not just web but mobile and every other touch point really that’s available out there. Very much a 360-degree view of the consumer is our philosophy.

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MM: There was another dimension that you introduced. You kind of suggested a little bit in terms of needing to understand the behavior of a logistics supply chain—or in this case, a transportation value chain. In classic economics, according to the work of Ronald Coase in his book, “Theory of the Firm,” he would refer to these as “transaction costs.” Transaction costs was his way—as a theorist and economist—to describe all of the handoffs. The communication, interactions and handoffs—as well as the delays associated with getting a business process completed.

So you were really calling attention to the fact that there were all these other hidden costs—almost like opportunity costs. A percentage of the truck that wasn’t fully loaded, and the amount of time it was sitting some place.

MB: Or the inability to ship something at a certain time, for lack of availability of capacity, and so forth.

Solving many of those problems, honestly, is easy for people once you give them access to the information.

MM: Right. Because it’s their data.

MB: Yes. It’s their data. The big headache here is integrating it from multiple systems. Representing it in a uniform way for people, getting it in the form they need, and in front of the eyes of the people that have to take action on it.

In that sense, solving the transportation and logistics problem is not just a matter of some computer-science oriented thing. It’s just as much — or more — of the basics of data display and information integration.

That said, those practices have until now been far too costly and far too complex for many companies to acquire. So, that’s what we’re going after and trying to make far more cost-effective.

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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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Emergence of diagonal applications

MM: Would it be fair to characterize that these vertical applications tend to be not necessarily transaction systems, but rather analytic systems?

MB:  I do not believe that is exclusively true. But I do think more of them tend to be like that.

MM: Predominantly true?

MB: For example, in industrial manufacturing… Industrial manufacturers sell—in many cases—equipment used by other people. So they have the service and maintenance applications associated with after-market service of the equipment. That’s not analytical.

Yes, they’re very interested in analyzing why these machines are all failing or what the quality issues are, but they are also scheduling the repair cycles.

MM: Yes. The operational systems, then.

MB: Yes. There are some operational ones. But I would tend to agree with you that a lot of them do have the tendency to be analytic.

That brings me to the issue of what I call Diagonal BI. The term was coined by our CEO Bill Copacino. So, diagonal—if it’s not horizontal and it’s not vertical—what is it? Diagonal is the word we chose to define things that live across some industries, but not across all industries.

These are applications or analytical solutions that are definitely not specific to a particular industry, but they represent common functionality that’s needed across many industries.

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Core concepts

MM: Let’s shift now into the conversation and hopefully extended discussion of digital supply chains and how they parallel to a high degree physical supply chains. Would you just give us a quick reprise of the core concepts or the core ideas of a supply chain? Then start to correlate that to digital and physical versions?

First, supply chains start with the idea that there are multiple business entities or operations that are part of an end-to-end process of transforming raw materials or IP into some sort of tangible good or service at the end, that a consumer ends up buying.

MB: Part of the complexity of managing a supply chain is that the number of these parties is not small.

If you are a company that buys things from two other companies, you do not have a big and complex supply-chain problem.

But many companies buy many thousand items from a large number of suppliers and in turn sell to hundreds or thousands of customers—these companies have very complex planning and execution issues and can benefit from new analytic tools.

Beyond analytics, collaboration with your supply chain partners, sharing of information and allowing views by your suppliers into the inventory levels of their products in your facilities, or views by your customers into the status of your shipments to them, can dramatically reduce your joint costs, improve product availability and increase customer service.

Web-based access to these analytics and collaboration applications by supply chain partners is a big advantage of SaaS solutions.

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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.

PvT: And from your point-of-view, how will marketing’s contribution to the organization evolve?

MM: Marketing is really about what I’ll now call engagement with customers and stakeholders that affect the purchase, consideration, trial, and ultimately loyalty and advocacy of customers.

Marketing remains core, fundamental to the value and purpose of a company. However, marketing must evolve beyond messaging—you know the old saw, lipstick on pigs.

Unfortunately, most senior marketing executives lack fundamental skill sets to innovate new services, especially digitally provisioned services.

Most senior marketing executives lack – are utterly bereft of what I call IT service management chops. And yet, the marketing executives that will have the big wins over this next 5 or 10 years will essentially be senior IT execs and CIOs that understand the concept of customer-making, the primacy of brands as a way of engaging customers in the value proposition, and more specifically, the provisioning of online interactive services as a core innovation to the customer-making process.

That’s why most chief marketing officers of major companies today will simply be out of the game in 3 to 5 years. They will have to retire out or do other sorts of boutique consulting because fundamentally they are suited up for hockey when everyone else is doing ballet.

Not good news, huh?

PvT: No, not at all. Not at all, and I’m sure most marketers would not want to hear that, so.

MM: Well, as I mentioned it before, William Gibson, has this great aphorism: The future arrives unevenly distributed, i.e., some people get it, some people don’t, those that don’t end up feeling a lot of pain and hurt as a function of being laggard on innovation-adoption curve and, more specifically, the future that arrived yesterday. We need to play a little catch.

Customer-making mindset, plus systems

PvT: Okay. So what do you consider as the core elements of a tightly integrated marketing model? And that’s sort of a loaded question…

MM: It sure is. Well, not to belabor the points that I’ve already made. First, you need to have a customer-making mindset; you must integrate the systems and compensation of pre-sales and post-sales to customer-making process benchmarks.

Second, you need to have the analytic discipline and rigor to be able to identify your ideal customers and predict lifetime or long-term value. You must understand your customer.

Third, you need to develop the operational capability of listening: mood of the market, voice of the customer, and patterns of engagement.

Fourth, you to put into place agile methodologies for the development of content and services used promotional reach and engagement.

Now some companies people start with the social media and social networks; they start with a voice with which some customer might connect and begin a dialog.

Social media enables a firm to initiate emotional connection with its customers, and get hints about what’s really going on, and then using those intuitions and soft perceptions drive a broad-spectrum analytic practice and develop true rigor about who is your customer.

So, you know, it can mean a Yin and Yang kind of thing where they feed on each other. It should result in a positive feedback loop: listening begats better content and services that in turn produces “earned media” in the form of praise and recommendations in the Web 2.0 mediaspace, that you inform above the line mass market creative strategies, and so on.

So to unpack your loaded question, the fundament challenge confronting the marketing executive today entails building operational capabilities within the context of an operational marketing platform—a business process-management platform for marketing-related activities.

Unlike marketing automation tools for “doing the marketing process”, the operational marketing platform must also support the rapid, agile development and provisioning new interactive services—essential software applications, service mash-ups, and widgets.

With good listening tools and process, combined with collaboration and scheduling systems, the operational marketing platform becomes an innovation-services platform

That idea nicely summarizes how innovation and marketing have converged in terms of a core competency, vis-a-vie this platform.

Data cubes got it started

MM: Again, we were in the middle of reprising the development of business intelligence. You’d talked about the early days of data warehouses and then how ERP started to move through a lot of corporations, normalizing a lot of that data, giving rise to the need for a master data management as a way of harmonizing data among systems.

Then I think you were about to launch into the emergence of business intelligence tools or technologies such as Business Objects or Cognos or Microstrategy or things like that.

MB: These tools, and the companies around these tools, emerged over time. There was a big flurry of tools companies that came into existence around this idea called OLAP or On-Line Analytical Processing. Its central idea was something called “Data Cubes” which allow you to analyze and manipulate data. They give you many different ways of looking at data and organizing it along different dimensions that you need to look at it. You could look at items by vendor, by price or by profitability or also by geographic region, organizational roles or hierarchy, etc. The “cube” notion comes by analogy to being able to turn a cube around in your hands to look at it from different perspectives.

These tools have been implemented in a variety of ways. In the early days, people had to summarize the data to a considerable degree in order to get these tools to perform very well. As computing power and storage has become less expensive, people have discovered that you really no longer need to summarize the data. In fact these tools become a lot more useful if you can actually drill all the way down to the lowest level of detail.

You can drill down all the way to the details, and observe issues associated with the data at finer granularity.  Then you are using the tool to figure out what’s causing the problem and how to solve it. This results in a much more flexible, robust, and efficient solution with much faster response times.

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PvT: You just mentioned analytics. How important is it to integrate marketing and customer data across the organization versus at the local level?

MM: Well, analytics is generally a can of worms that once you open it you never find a can large enough to get all the worms back in.

Analytics has become central and critical to success in the always-on, 24-by-7 integrated, online-offline brand theater.

When we start talking about analytics, we discover that 80-90 percent of the data that a marketer needs does not reside, or exist at all, in their CRM systems.

So many marketing organizations spent the last six to ten years getting organized around what I’ll call tactical CRM – your sales force automation platform. I am astounded how many firms still struggle with CRM as operational capability.

Many firms have separate or loosely connected operational CRM used by the customer service or call center. I am also amazed with the number of these system that contain little more than a transaction record about previous purchases and logged complaints.

A number of firms that have not yet integrated tactical CRM from sales operations with the operational CRM of their call centers and customer interaction centers.

I just chalk that up to the penalties of execution—everyone’s heads down hitting their numbers with little extra time or incentives to innovate something better.

Integration of multiple CRM systems represents a major undertaking for most firms, and it requires developing huge data model by which to specify – in very concrete table-to-table or data-element-to-data-element level—specifically how to transform data into high-level business information that supports specific business decisions.

Most companies that I’ve run across have incomplete or just simply wrong data maintenance procedures in place. So, as a function of that they end up with glorified mailing lists with very little useful analytic data beyond who bought what and why.

One version of the customer truth

Often first major initiative in data integration entails creation of customer master.

While simple in name, the development of a customer master represents a Herculean accomplishing: one-version-of-the-customer-truth.

As this starts by developing a data model of what constitutes a customer relationship—and I stress the relational aspects of the customer and way beyond basic name and address—we often discover that multiple individuals with multiple roles and responsibilities within a single customer object.

In this data-centric view of the world, a household or business entity constitutes the cornerstone of a customer relationship—to which you can associate a number of individual buyers and influencers by context.

Right there, many CRM implementations fall down: they make no meaningful distinction between an account, an contact, and customer object—the business entity or household—that represents the economic context for many buyers, transactions, interactions, and influencers.

So, let’s say we have a customer master—one version of the customer truth expressed in clean, uniform data!

This invokes 90/90 rule which state after you have completed 90 percent of the work (i.e., building your customer master), then you another 90 percent more to complete—the second 90 percent!

That almost always requires the purchase of external enriched data overlays to your customer master.

This will take to you companies such as Acxiom, D&B, Experian, Epsilon, InfoUSA, Merkel, etc.

Enriched data overlays of households might include credit histories and scores, the model and year of cars in the household, names of other members of the household, marital status, plus things like educational levels, current job position, annual income, total credit available and the equivalent of a business profile.

By the way, one of the most interesting developments as it relates to the customer data master, relates to the emergence of an XML standard from business reporting called XBRL (XML Business Reporting Language) that mandates that all public firms must publish their annual reports, 10-Ks, and 10-Qs in explicit 2000-element XML schema. While just a side show for now, XBRL will transform database marketing into true one-to-one engagement. Gosh, we take another hour unpacking that idea. But here’s the seed of a big idea: Every system of record in the next 5 years will adopt XBRL for all its publishing and reporting functions, creating a level of hyper transparency within business operations that will boggle the mind.

Segmenting for profit

So let’s get back to customer masters and enriched data overlays. Now you have the ability to really start to slice, and dice, segmenting customers and markets.

However, you can’t slice and dice your customer database using the relational database or the tools of a CRM system. You can start there. But, soon enough you will need more speed and better visualization.

At this point you need to bring in specialized, analytic databases—wicked fast columnar databases—for plowing through 5 or 50 million customer records with a response time of several seconds; as opposed to using a relational database that might take hours or all night to complete one complex query.

So specialized analytic databases with train-of-thought visualization tools use the enriched overlay data to begin understanding things like price sensitivity, unmet needs, and other sorts of buying criteria within dozens or hundreds of micro-markets—what analysts call consumption cohorts.

This fast-cycle analysis enables a practitioner to think in terms of predicting long-term value of individual or small clusters of customers.

With time and practice, a good analyst can profile the ideal or most profitable customer sets, specifically identify them by name, engagement criteria, and media consumption preferences..

Now, everything we have discussed to this point deals with database analytics. Four more analytic disciplines now come into play: Web analytics, messaging or email analytics, social media analytics and content analytics (or semantic analysis of one’s inventory of content and advertising)

Web analytics, site performance, and customer experience management will continue to evolve into an integrated suite—all good but fairly narrow sets of data.

Closing the loop with messaging

Messaging or email analytics really start to validate with quick call-and-response or probe-and-validate procedures of newsletters and emails specifically targeted to those segments that your predictive modeling identified.

In practical terms, this means that you need to have something far more than just the mail list manager or a newsletter system. You need to have really powerful analytics process driving each newsletter.

A creative and analytics team starts by building newsletters with Lego-blocks of content and data that correspond to a specific set of segmentation and targeting criteria.

So as I send out 15,494 emails to those individuals that I know are interested in Mexican cruises with Salsa dancing lessons, I want also want to see the response level to other recreational ideas, venues, and offers.

This will require that each email embeds personal URLs, sometimes called ‘Purls’, so that each click through takes the recipient to an individualized landing page—built just in time, just for them—that validates the messaging effectiveness or lift and associates that event’s data to a preexisting user database record.

This closes the loop in terms of my analytic profile, engagement criteria, and consumption of the media.

Now, most of the time that kind of closed-loop feedback information remains locked up in the newsletter or messaging system, and very rarely, if at all, comes back into the customer master or the creative teams driving other media creation processes.

So, as Website, database, and messaging analytics come together, guess what happens: Gee, given all these really fresh insights that our multi-channel analytics has developed, how then we inform the strategic communications teams in our agencies and our tactical content teams pushing content into the various websites—brand touchpoints that passively activate engagement as visitors land.

I have met hundreds of executives who struggle with breach: how do we get the advertising, web, direct response, and field marketing teams on the same page, using a common set of analytic insights to create effective engagement? How do make creative briefs more interactive and driven by same-day analytic insights.

Part of the underlying problem, we have discovered, lies in the very structure of what most creative and marketing professionals call content—the process of creating content and the operational capabilities of managing multimodal content.

But, I skipped to the end of my argument about the evolving integration of five analytic disciplines: Web, database, messaging, social media, and content analytics.

Getting social with analytics

So let’s pick up with social media analytics.

How do you use technology to quantify three really important dimensions of the Web 2.0 mediaspace (blogs, tweets, forums, and social networks).

How can you track, in near real-time, the mood of the market, the voice of the customer, and their individual patterns of engagement?

Social media analytics takes you further upstream into the buying process—much further up in the buying process where people are still developing awareness and consideration.

For that you need to have a really effective voice-of-the-customer program coupled with social media monitoring.

A good voice-of-the-customer program entails long-form interviews with 50 to 300 customers a month, transcribing exactly what they said about the process on discovering, considering, buying, using, and disposing (where applicable) a featured product or service—what we call the ‘customer journey’.

Of course we now see powerful new systems coming to market that automatically transcribe call-center interactions with customers—requests for information or service—all social content to feed a voice-of-the-customer content analytics process.

With semantic tagging of voice-of-the-customer content and mapping that against segmentation and engagement profile, something quite amazing emerges: each step of the customer journey.

So as a customer transits from no awareness to awareness, consideration, trial, purchase, commitment, repurchase, loyalty, and advocacy—as they transit customer engagement lifecycle—you will have actual dialog of real interviews with people at each of those stages, and, more powerfully, how they transit each stage of the customer engagement life cycle.

With just a few hundred of long-form interviews, a team will use a text mining engine map keywords and phrases of the voice-of-the-customer content and develop a taxonomy of desire: awareness, consideration, trial, preference, as well as things like dissatisfaction and satisfaction, wow, or disgust.

And as you develop this library, this taxonomy of engagement supports all kinds of goodness, including which AdWords to buy, how to optimize content for search engine discovery, and the structure of engagement.

This taxonomy of engagement also supports what practitioners call the basis of conversation—the details of how your customers talk about themselves, their lives, and what makes a contribution, including your products and services.

This all syncs up with social media analytics, usually the work of social agencies or monitoring services with specialized spidering tools that crawl through the 50 to 100 million blogs and forums and hundreds of millions of social network profiles and billions of tweets.

Social media monitoring then mines them these sources for keywords and phrases that correlate to your markets and competition, generating a dashboard with statistics on awareness, consideration, trial, etcetera, by your various customer segments, and more specifically what your customers are saying about your brand, what it means to be in a relationship at various stages of the brand lifecycle.

The voice-of-the-customer basically mines interviews about how customers talk about being in a relationship with you, and then the social media monitoring tells you how to validate which brand stories connect brands and consumers.

Agile methods for content creation

This all brings to the last analytic discipline in my rant: content and how marketers will have to reengineer their processes of creating content and and manage multimodal content.

First, it starts with the principles of digital asset management: systematic reuse, do it once, get it right up front, tag and classify everything for speed discovery and retrieval, optimize media components for database publishing and content transformation processes, build and use templates, etc.

Second, adopt the principles of agile software development. A bit much to go further here…