0 IBM Interact Adds Interactions to Enterprise Marketing Management

My continuing tour of real time interaction managers landed with the good folks at IBM two weeks ago, where I caught up with what’s now IBM Interact. The product was originally launched more than a decade ago by Unica as Affinium Interact.*

The concept of Interact has stayed quite consistent over the years, although the underlying technology has been overhauled several times. The general trend of the changes has been closer integration with other components of the IBM/Unica marketing suite. For example, the original Interact had its own flow chart interface, but the system now uses the same segmentation interface as IBM Campaign. The two modules can also share segment definitions, offers, and interaction history. There’s also some integration with other IBM marketing products, notably the Product Recommendation component inherited from IBM’s CoreMetrics acquisition.

Interact's concept is the same as other interaction managers: touchpoints send it data about a current interaction; the system uses rules, models and data to select one or more offers; and the offers are sent back to the touchpoint for delivery. The differences among these systems are matters of nuance: Interact stores its own permanent customer profiles, while some other systems must re-load data from external systems during each interaction.  Interact assigns fixed scores to offers within each segment definitions, while other systems use scoring formulas shared across segments (although Interact can do that too).  Interact can create self-training predictive models, not all competitors have this option.


A couple of other features seem more or less unique. Interact determines whether customers are eligible for an offer using either qualification rules or Campaign-generated “white lists” and “black lists”; other systems use rules alone. Interact can also assign offers at global, segment, or individual levels, while other systems don’t provide all those choices.

It’s unlikely that any of these differences make Interact significantly more powerful or easier to use than competitors. In practice, the system’s major appeal will be its close integration with Campaign and other IBM products. It is now part of the IBM’s Enterprise Marketing Management (EMM) group, which includes both Unica and Coremetrics, both acquired in 2010. This group supports IBM’s larger strategy of selling systems that use huge quantities of data to run all aspects of large organizations. The company has identified marketing organizations as a major potential market within this strategy and is spending aggressively to both develop that market and take advantage of it.

You might think that Interact plays a central role in IBM’s marketing ecosystem: after all, real-time interactions are the epitome of data-driven marketing. But just a tiny fraction of IBM’s 2,500 EMM customers use Interact (actual figures are confidential) and most deployments seem to be focused on specific -- dare I say tactical? -- applications in one or two channels. The company’s EMM focus seems to be more on analytics and outbound marketing: for example, its most recent EMM acquisitions were Tealeaf Technology (Web experience analysis)  and DemandTec (merchandising analysis) . But it does report increasing interest in Interact among its clients, and high hopes for future growth.



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*A year’s free AARP membership to everyone who remembers the Affinium brand and can sing the jingle.**

** Okay, just kidding.  There never was an Affinium jingle, so far as I know.

0 Marketing Lessons from Chernobyl

I’ll be speaking about optimization this Wednesday at the Online Marketing Summit conference in Santa Clara, CA. Since I’m very comfortable with the actual topic, most of my prep time has been spent looking for pictures for my slides.

One discovery was the image above, which shows is how I think most people imagine optimization: a team of dead-serious revenue engineers carefully tweaking dials and watching gauges until they find the perfect balance among alternative marketing investments. That the real world isn’t quite so rigorous is a sad truth I’ll cover during the conference.

But this picture isn’t just any power plant. It’s the control room at the Chernobyl nuclear reactor which disastrously exploded in 1986. Look closely, and what do you notice?

Yes, those hats. Apparently the Chernobyl plant was being run by pastry chefs. That explains so much.

My theory is this: the Soviets had a little-known tradition that translates roughly as “switch jobs with your friends day”. The year of the accident, a team of bakers decided to change places with their buddies in the Chernobyl control room. The nuclear engineers spent the day calculating the volume of pie tins and optimizing heat convection in the baking ovens. Meanwhile, the pastry chefs were decorating fuel rods with icing and asking, “What if we replace the reactor coolant with meringue?”

This did not end well.

Well, maybe that didn’t happen. But my imaginary pastry chefs sound a lot like stereotypical marketers: experts in a subjective field where decisions are based on taste, feel, and appearance, and progress comes through intuitive experimentation. Those methods work well in the kitchen, but can’t be safely transferred to a nuclear reactor. Nor do they work for marketing optimization.

Like reactor management, marketing optimization programs need to be based on deep knowledge of the underlying process. They rely on precise tracking mechanisms that support long-term monitoring of detailed results. They need to be run by marketing equivalent of nuclear engineers, not pastry chefs.

This doesn’t mean that data geeks should take over marketing. Chances are, things weren’t going very well in the Chernobyl bakery that day, either. The city needed both bakers and scientists. But having them wasn’t enough: they needed each in the right place. Marketing departments are the same.

0 Rapid Insight Provides Low-Cost Options for Desktop Data Transformation and Predictive Modeling

Summary: Rapid Insight offers low-cost desktop tools for data transformation and automated regression modeling. They're a good choice for companies that need something simple yet powerful.

Predictive modeling is widely used by consumer marketers to select names for mailing lists and to decide which products to offer existing customers. These models are typically built by statisticians with tools like SAS and SPSS. In other cases, marketers can build them for themselves with automated tools like KXEN that are tightly integrated with the marketing automation system.

But most marketers still don’t have a marketing automation system or even an integrated marketing database. Nor do they have the skills to use a product like SAS. This group needs stand-alone tools to do two key things: assemble data from multiple sources, and build and execute the models themselves. (Okay, three things.)

Rapid Insight offers exactly those two (or three) capabilities in a reasonably priced package.

Veera is the data assembly tool. It lets users connect to most standard data sources and then define a processing flow to filter, merge, aggregate, transform and otherwise manhandle data into a form that makes it useful. Veera also provides some basic analytics including descriptive statistics (mean, median, value frequencies, etc.), cross tabs and graphing. The flow is set up as a sequence of icons with a drag-and-drop interface, which means users don’t have to learn a scripting language. Rather than go into more details, I'll just point you to the vendor's on-demand demo.


The predictive modeling tool, prosaically named Analytics, builds logistic and least squares regression models. (Logistic models predict yes/no outcomes such as whether someone will respond to a promotion; least squares models predict continuous numeric outcomes such as lifetime value.) The Analytics interface is more sequential than Veera: users get a set of tabs that lead them through the steps of loading data, selecting variables, building the model itself, assessing the results, and scoring an audience. At each step along the way, users can make their own decisions or allow the system to choose for them.

I’ve seen quite a few automated modeling systems over the years, and was impressed at how well Analytics provides users with information to understand what's happening and take control when desired. This should let the system satisfy knowledgeable statisticians looking for a productivity enhancer, as well as novices who want to rely on the system's choices. Analytics also has a good online demo.


Veera and Analytics both run in client/server or desktop configurations. They load data into system memory (RAM), which means very large projects could be problematic. The vendor says a half-million rows with a couple hundred variables is a reasonable universe to model.

The two products are sold separately. This makes sense: many companies could use a generic data assembly tool like Veera for purposes other than modeling. For example, marketers might use it to construct a multi-source marketing database for promotions or analytics.

Pricing is $3,000 for the first Veera user and $5,000 for Analytics, with discounts for additional licenses. This is quite reasonable compared with other automated modeling systems, although other products often provide more than just regression models. Annual maintenance for each product is $1,750 per license. Rapid Insights has been selling its products since 2005 and has more than 150 clients with over 200 licenses.

0 DataMentors Offers Low-Cost Marketing Database

Summary: DataMentors has launched a low-cost marketing database product with limited functionality. It's an interesting test of what marketers really want.

Marketing database software and service vendor DataMentors last week tossed its hat into the ever-more-crowded ring of marketing automation for small(ish) businesses. The new product, DataPoint, is a limited version of the company’s flagship PinPoint system. Like PinPoint, it combines DataMentor’s DataFuse data cleansing and matching software with a private-label version of SmartFocus campaign management and analysis. The difference is that PinPoint scales to tens of millions of customer records, while DataPoint is limited to 100,000 customers, one million prospects, fifty data fields and quarterly file updates. Pricing is $2,000 per month, probably less than half what most marketing automation vendors charge for a 100,000 name installation.

Wait. Back up. Did I just write quarterly updates? Fifty fields? Warm up the eight track and fluff out my mullet, modern marketing automation products don’t have those types of limits. Nor does DataMentor’s own PinPoint. What's going on here?

Even though DataPoint includes the quite sophisticated campaign management features of SmartFocus, it’s really less a marketing system than a tool for data analysis. Marketers without any access to their customer data will be happy to load their files into DataPoint and do all the cool slicing and dicing that the SmartFocus engine makes easy. They might produce some outbound campaigns as well, but lack of fresh data means these are going to be pretty generic.

It’s tempting to relate this old-school approach to the origins of DataMentors itself: it was co-founded by industry veteran Bob Orf , the “O” is OKRA Marketing, a pioneer marketing database vendor founded in 1987 when small records and infrequent updates were the rule. But DataMentors has kept up with the times: Orf says that most PinPoint systems are updated daily or weekly, and the company even offers real-time, Web service access to its data quality system. And of course DataPoint users can upgrade to a more powerful version if they’re willing to pay.

That being the case, it’s probably more useful to think of DataPoint as part of the market for on-demand business intelligence – competing in some ways with companies like Birst, PivotLink and Oco. Although those systems are more flexible than DataPoint, they share its low deployment cost and focus on analytics rather than marketing execution.

One key advantage DataPoint has over those systems is integration with DataFuse, a highly sophisticated matching engine that was DataMentor’s original product and remains the cornerstone of its business. Another difference is that DataMentors has recently licensed consumer and business databases for its clients to use as prospect lists or to enhance their own files. DataFuse users can access these for an extra $1,000 per month – another highly competitive rate. DataPoint clients will also benefit from the deep expertise of DataMentors staff, particularly in the banking industry.

A configuration like DataPoint is not something I would have expected in today’s market, where continuous updates, flexible data models and near-real-time customer interactions are standard operating procedure. But I have tremendous respect for the DataMentors team and trust them to know their market. It will certainly be interesting to see how well DataPoint works out for them.

0 VisualIQ Measures Marketing Impacts Across All Channels

Summary: VisualIQ combines customer-level transactions and contact history with traditional aggregate data to produce better marketing performance measurement. It hasn't solved the problem of identifying the same customer across channels, but it's trying.

I was going to start this post by writing that last-click attribution has recently come under fire, but the first Google hit on the topic brings up a study from 2007. So maybe the criticism isn’t particularly new. But the fact remains that, now more than ever, marketers are trying to measure the impact of all contacts on customer behavior.

Broadly speaking, the problem is attacked in two ways. One, most common among consumer goods manufacturers and others who do not sell directly to their customers, uses aggregated data in marketing mix models to find correlations between marketing efforts and total sales. The other, favored by banks, retailers, communications providers and others who do sell directly to known buyers, assesses the impact of each contact with specific individuals. Last-click attribution is a particular challenge for online marketers because they fall between these two situations: they can often identify their buyers but not trace their full contact history.

VisualIQ, founded in 2005 as Connexion.a, proposes to straddle these worlds by combining aggregate-level models with customer-specific contact history. They haven’t found a magic bullet: like everyone else, VisualIQ tracks online customers through cookies, with all the limits that implies. But VisualIQ strives to make the best use of what’s available by unifying data from as many online campaigns as possible, linking cookies with online transactions, and then linking online transactions to offline identities.

This approach offers some general advantages and two specific capabilities. The general advantages come from assembling all advertising and customer transaction information in one database. This allows VisualIQ to analyze campaign results, do whatever identity matching is possible, and to isolate the impact of source, contact frequency, demographics, location and other variables. VisualIQ, a hosted service, has invested heavily in technology to analyze massive data sets along such dimensions.

The first specific capability is relating pre-purchase contacts to actual purchases for individual customers, thus moving beyond last-click attribution. Although this is subject to the limits of cookie-based tracking, VisualIQ does what it can to build a unified identity by sharing the same cookie IDs across as many online channels as possible. The second capability is building mix models with data from actual customer contacts instead of market-level estimates or surveys. VisualIQ says it has found this yields more accurate results than traditional information.

This is all good stuff and VisualIQ has packaged it nicely in a tiered set of offerings. These range from campaign-level reporting to customer-based insights to predictive modeling and simulation, with prices for the simplest system starting as low as $5,000 to $10,000 per month. The company has had considerable success, counting major banks, retailers, and communications firms as clients. Note that these are all industries that sell to their customers directly.

But VisualIQ’s specific offerings are just part of the story. What’s really important is setting explicit goals of linking identities across channels and measuring cross-channel marketing impacts. These are arguably the core challenges in marketing measurement today. This focus has led VisualIQ to look for alternatives to cookies and to use existing methods to combine online and offline information for the same person.

The company is also seeking to make it easier to apply its results. Today, it basically generates reports that suggest better media allocations and advertising contents. But it is working to automatically feed those findings as rules into execution systems such as ad servers and ad exchanges. This brings marketers closer to the ultimate goal of self-optimizing programs. Other vendors are also pursuing self-optimization, but VisualIQ promises the advantage of decisions based on data from all channels rather than a single channel or, heaven forbid, just the last click.