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.

–

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.

–

PvT: Okay. Talk a little bit about digital asset management and whether or not that’s a feasible way for global organizations to manage their corporate brand identities, photos, and videos—their brand assets?

MM: Sure. Well, just for a little bit of a history on that. My firm invented the term “media asset management” in 1994 in our work with Aldus and MediaStation.

Later in 1996 or so, we expanded the term when we wrote the white paper for Apple Computer as part of their Masters of Media Program—a brilliant industry-wide marketing framework that included Adobe, Agfa, Kodak, Quark, and Xerox conceived and executed by Jeff Martin, then the Director of Marketing for their Advertising, Design, New Media, and Publishing division.

Apple commissioned an executive white paper to make the business case for their line of Apple servers. IBM picked up from there and commissioned another white paper and international roadshow—also to make the case for the IBM Content Manager.

In 1998, my partners and I wrote the first full market report on DAM and continued with the reports until 2002.

In 2001, we began our long-standing partnership with Henry Stewart Events and their DAM Symposium.

In 2003, as the Editor in Chief, I started the Journal of Digital Asset Management—with which I continue today.

Strategic Capability

I say this all as preamble, do I consider digital asset management strategic capability? The short answer is, emphatically, yes. You can’t manage a global brand and a pan-regional marketing operations without some form of DAM. In fact, we have published a series of executive white papers on the subject.

Now DAM has a lot of misinterpretations, or misunderstandings in terms of what it constitutes.

DAM, first and foremost, constitutes business strategy for accelerating operational processes within media, entertainment, and publishing, and marketing content processes within global brands. So it’s reducing cycle time, reducing cost, and having a process that’s far more agile or flexible in adapting to change.

I contrast digital asset management with content management. I used to say somewhat tongue in cheek that content management is really ‘crap management’.

Content management deals with more or less self-descriptive files—documents or Web pages for which you do not need a lot metadata to describe its contents, meanings, semantics associations with other content and, more specifically, who owns the content or images—from where did the editorial or copywritten material come, when does it expire, all that.

Digital asset management, in contrast, deal with non-descriptive files, hence the emphasis on metadata and the systematic reuse and transformation of preexisting digital media files. This entails the creation and management of metadata associated with findability, reuse standards, and permissions or digital rights management.

Now a reusable digital file may represent an image, photograph, or publishing template. Digital assets may include text or product claims used in marketing communications, or video clips, MP3 podcasts, and type fonts, or Flash animation. Or elements that contribute to immersive virtual world experiences 3D and 2D models or primitives.

A digital asset might also include software code assets—scripts and programming—and things like IT service management policies and business rules or software libraries and software objects. Or learning objects or reusable pieces curricula that flow into books, instructional DVDs, or online courseware.

So, digital asset management is really about reuse and creating metadata that give you competitive advantage: Cost reduction, time to market, higher quality, greater process agility, and the ability to maintain transparency or governance across an entire marketing supply chain.

As a business strategy, digital asset management starts with a DAM repository—where you put all those bits—and begins to really payoff with an operational group—a DAM service group—that maintains the integrity of metadata, digital asset files, and user productivity.

This brings us to the current state of the art in DAM:  Managing a supply chain for continuous improvement and reduction of cost, cycle time, defects, and opacity of key business processes.

So, I do not consider digital asset management an option, nor a luxury. Just like you have an email system, you must have a DAM. It’s just not an option.

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.

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…

Cross-integration of many system

MM: In fact, Mike—as we were doing the quick recap of the history of data warehouses… I think one of the things that had developed or emerged out of ERP and the data warehouses as they interact… A lot of the data that people need aren’t inside the organization. They’re outside the organization in suppliers and in trade partners.

MB: Well, that’s certainly the case. There’s information that’s down in the supply chain. But in fact, in the organizations we visit the systems that are facing the supply chains at customer sites are actually comprised of a variety of systems like warehouse management systems (WMS), transportation management systems (TMS), freight payment systems, customer relationship management systems (CRM), budgeting and financial planning systems, and so forth.

These systems provide functionality not found in ERP systems and therefore sit next to them—so companies always have many disparate, systems where important data resides and needs to be integrated for analysis and other purposes. There still needs to be some cross-system integration of this information. ERP systems in some sense have not quite lived up to their billing of consolidating all of this information. It remains important to be able to reach across different systems as well as across multiple ERP systems to be able to provide the visibility that companies need.

You mentioned the organizational barriers. Those are really quite significant as well. I’ll mention a couple of significant problems there. These are good examples for contrasting the way we approach these issues at Oco versus the way others in the industry have historically gone after these problems.

Historically, people will set out to put the data from their whole organization into the data warehouse. They’re trying to get data — all the data — in one place and also must cleanse that data—an enormous task. It’s an open-ended business intelligence activity that will enable the company to utilize the data warehouse…someday.

In other words, they’re building the data warehouse without already knowing exactly what they want to get out of it. They want to get whatever can come out of it.

–

–