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.

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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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Problem of transportation logistics

MM: Not just trucks, but what’s on the pallet and how many pallets get organized by what truck.

MB: That’s right. And how many stops it takes and so forth.

This brings me back to what we mean by a “Diagonal,” BI application.

To build an application that really helps address the problem of transportation logistics, or the truck shipping of goods, you have to embed a lot of industry understanding and knowledge of trucking into the application. So it requires information specific to the business problem of shipping goods by truck, but it’s not specific to any particular industry.

You don’t really care whether you’re shipping machinery or consumer packaged goods or clothing. These applications cut across industries, but not all industries. Obviously, financial services people aren’t shipping goods around by truck, and for the most part, shipping is just not a part of their primary value proposition. Similarly, higher education is not a truck-oriented industry. But any manufacturing company, whether in the food segment, the clothing segment, the toy segment, the industrial products segment, etc., all have a similar trucking problem to solve.

Another example is any company that makes or sells something that typically has sales margin and profitability issues. The companies really want to understand what products are selling at good profit margins. They want to be assured that the inventory they carry, relative to sales rate, is in balance.

Sales margins and profitability issues cut across industries that have goods to buy and sell—but obviously these aren’t applicable to government or higher education. It’s not like a database system because it doesn’t apply across all industries.

These diagonal types of applications are important because they add high value for their customers. They typically save companies thousands and thousands of dollars all the time, or even millions, for large companies. So they are applications that can command high price points, because they really deliver great savings and a very attractive return.

But also, they’re applications that—because they can be sold across many industries—have a pretty large base of prospective customers—larger than vertical-market applications that are targeting a very narrow perspective. They are very attractive from a business standpoint.

Diagonal applications also work very synergistically with SaaS deployments. That was one of the things that I emphasized in the talk I gave at SaaScon. The reason there are companies like Oco and obviously other new market entrants in this space is because of this synergy.

When you build a system for a particular business problem, transportation logistics, let’s say, then the structure of the database of information that’s needed to support it is not specific to that particular customer. It’s a database that’s designed to support transportation logistics.

As a result, you can get great economy of scale in the deployment of that system by creating a SaaS multi-tenant deployment of that database. All the customers sharing that infrastructure are trying to solve the same kind of transportation and logistics problem against a database of similar structure.

This works a lot better than the ASP models of a decade ago. Back then, custom data warehouses would be designed for each business. If you tried to aggregate those together, you’d get a whole bunch of totally different databases. In some sense, they were too customized. You’re not going to get common behavior by putting them together.

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Game changer

MM: SaaS represents another development—almost a second or third wave development of the Web. The idea then is that you don’t have to install software or train a whole IT service management staff for managing and provisioning a service. But you can simply go to a provider such as Oco to get a capability that might’ve cost 5 or 10 million dollars for a hundredth or thousandth of that.

MB: Yes. It really changes the game tremendously. There’s been a lot of argument over, “What really is SaaS?” People have various definitions of it—some broader and some narrower. My definition of it is pretty simple.

SaaS is a service you utilize instead of buying software. It’s defined by what you don’t have to do. You don’t have to buy, learn, modify, install, and maintain software.

MM: I think that the analysts have all kind of gotten together and shared some basic definitions of SaaS V1 or 1.0—which was a point solution that wasn’t really set up to interoperate. It might pass data, but it wasn’t really set up to interoperate with other SaaS applications or installed on-premise applications.

MB: I think people talk about the SaaS 1.0 versus the future of SaaS. It’s true that the first wave of SaaS introduced applications like Salesforce.com. Some people would even put applications like Webex into that category. I don’t. The alternative to using Webex is not buying a software package. The alternative to using Webex is getting on an airplane to go give a customer presentation.

MM: I think the Go To Meeting Citrix people would probably argue with that, but that’s okay.

MB: I mean the alternative to these online demo and meeting systems — Webex or the other services like it—is if you don’t want to use one of those, you can’t buy a package that solves the bridging problem between you and whomever you need to give a demo to. I suppose you could host such a thing on your own corporate website, but I don’t recall many people doing that in the days before Webex.

In any case, the point is that these applications didn’t involve integration. We have moved into an era you can call SaaS 2.0, if you want, where the applications are starting to involve the core activities or functions that businesses do, such as business intelligence or ERP and so forth.

So yes, there certainly is a qualitative shift, there. But some of the industry people who I have some disagreement with would say, “It’s not SaaS if you can’t download it yourself,” or, “It’s not SaaS if it doesn’t have self-installation and free trial.”

They basically are narrowing the definition in ways that I don’t believe are required. As far as I’m concerned, if an alternative to a solution requires that you have to buy software and install and maintain it, then it fits the category of SaaS.

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Webification of BI

MM: Then in history of business intelligence, the Web came along—and some things began to change. Could you quickly reprise us in terms of what changed how as a function of the Web, in the space of business intelligence?

MB: The Web changes everything. The Web changes some things directly and some things indirectly. One of the interesting forces in the database world and the data processing world is that the Web introduced a whole new realm of data to be handled.

The whole world of e-commerce introduced a need to understand e-commerce marketing, and to understand click-streams and how people were using the Internet and so forth. That created a number of new opportunities for people to try to process and understand the wealth of data, and to understand the customer behavior.

The companies that successfully handled Internet advertising have become the masters of this—Google and so forth. That’s the way that the Internet raised the stakes on this kind of marketing.

There’s also the absolutely direct benefit that the Web introduced—a new way to get information to people—in a way that is really much more appealing.

You’re able to get rid of many of the hassles and costs associated with software installation, if you can just give people a website to visit to get the information they’re looking for. People really like this model. It has all of the graphical capabilities that they’ve become accustomed to with their Office and installed desktop software.

That is an immediate thing that people latch on to: “Can’t I just have this on a web page, please?” Of course there is no reason that they can’t. There are a lot of companies like Oco making that happen now.

The Web also changes the way that the service, the calculations, and the data preparation can all be handled. Now, and throughout the history of data warehousing—going back to the mid-’90s, there was an awful lot of outsourced data warehousing. Lots of companies outsourced their data warehousing to big companies like Acxiom that specialized in data warehouse hosting, particularly for target marketing and related applications.

The Internet basically makes this idea a lot more attractive to companies—and in particular, attractive to companies with smaller budgets. It’s not just the big companies that can consider leveraging database and business intelligence technology, but in fact, everybody now can.

People are reluctant in some cases, because they fear, “Oh, gee, my precious data is going outside of my firewall.” But once people are satisfied that their data’s going to be handled securely, there are tremendous advantages.

One data-warehousing consultant I know said it pretty well, “All companies outsource the way their money is handled. That’s certainly precious to them. Why not data?”

MM: I think it’s because there’s a career track associated with it.

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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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Mind-mapping best practices

MM: Does the methodology derive from any particular established profiling methodology?

MB: No, it doesn’t. It was developed internally with the input of some very experienced people who have a track record in handling complex business processes.

We do a lot of things that would probably be familiar to many database practitioners. We conduct a database dimension analysis. There are some unique aspects to our approach. The way we structure it makes it very efficient, as well as very effective, at capturing what’s needed for the business people, as well as identifying the sources of the information.

MM: Is there a corresponding data diagram or an entity-relationship diagram or some other kind of high-level visual abstraction of the transformation of business data into intelligence?

MB: We actually do have a set of proprietary diagrams we use. We use a mind-mapping tool in a very powerful way, that maps the transactional data needed to solve the defined problem and all the business dimensions that would be useful in analyzing, what we call slicing and dicing, that data.

And of course we bring a point of view on best practices, key metrics, the ideal reporting and analytic frameworks that are the best way to gain insight into a business area. We developed these with very notable experts in each functional or industry area where we work.

So we bring a very thoughtful and complete starting point to the table on day one, and we work with our customers to modify these solution templates to meet specific perspectives or needs that they have. We avoid turning it into a full-up custom solution, though. So we get the best of both worlds—a world-class solution as a starting point and the tailoring of that solution to specific customer preferences and needs.

We don’t publish our diagrams obviously, because they contain a lot of our intellectual property, but customers that go through the profiling process obviously get to see and benefit from that analysis.

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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…

Critical success factor: Data model architect

MM: That almost reminds me of a conversation I had with a data warehouse architect. She was building a data warehouse for an executive information system for Bank of America. She would talk about sitting down with a fairly senior marketing executive and saying, “What are the business decisions that you make in the course of a day?” And then, “What information do you need in order to make a fully-informed decision?” And, “Where do you go for that information?”

Of course, there are green bar reports here and a conversation here and a fax here. In the course of doing that, she’d talk about identifying the most important—the number 1 or number 2 most important—business decisions that an executive would make. Then doing a map of logical but physical data sources, so as to be able to identify what the data items were that needed to be collated into information that then supported an action or an insight.

That kind of describes what you’re talking about in terms of this top-down optimization strategy or top-down problem-solving sort of thing.

MB: My expectation is that a large percentage of the projects that have been successful have had practitioners working on them in the model that you just described. Here at Oco, we’ve really taken that notion and turned it into an art form. We sit down with a business for one or sometimes two days and go through a systematic approach to define the key problems they need to solve. We call this approach a profiling session.

We design the solution and figure out the data resources that are going to be required and so forth. We have a quite robust methodology we go through. It’s a precise recipe.

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Digital sail boats: Hole in the water in to which one pours money

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MM: It sounds like a recipe for a very expensive digital sailboat.

MB: That’s what a lot of these projects are. That’s what has caused much of the difficulty and the high failure rate. There have been many successful data warehousing projects, but certainly a recipe to success is having some specific focus and purpose.

Many more benefits can accrue, but a lot of organizations simply run out of patience with the project before it has really gotten to the point where it’s delivering results.

At Oco, we do something quite different. I call it the top-down approach. We basically pick a business problem that is causing pain to the organization, and we identify a way of presenting the information to the business users in a way that we collectively believe will help them solve the problem.

We create this solution by bringing our best practices and knowledge of specific functions and industries to bear. Then we work top-down from this solution design to what specific data and related information sources need to be integrated to solve that problem.

So our integration work isn’t open ended. We know when we are done integrating.

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