0 Marketo's Engagement Engine Simplifies Complex Marketing Automation Campaigns

I’ve long said that the best campaign design would be one circle: the system executes the best treatment for each customer, waits a day, and repeats. My point is that elaborate, branching flows are too complex for most marketers to build and maintain, and – because reality is infinitely messier than even the most sophisticated flow chart – will often give customers a sub-optimal treatment.


It’s probably just as well that no vendor has ever built a system based on my design. But the good folks at Marketo have taken a step in that direction with their latest enhancement, which they call an “engagement program”. It has more than one step but does get away from the idea of a rigid, branching campaign flow. Instead, it is organized in terms of “streams” that contain pools of content. Once a customer is added to a stream, the system will offer the next piece of content whenever a contact is due according to the campaign cadence. What’s next is set by the order of content within the stream: users just drag content into the container and put on top the ones they want sent first. The system will go through the content in this order during each execution (which Marketo calls a “cast”), and send each person the first item they have not already received. This avoids duplicate messages and lets the system deliver a defined series of messages without explicitly setting up a sequence. It also makes it almost effortless to insert a high priority message that goes to everyone or to swap out contents as new materials become available.

As you’ll immediately notice, sending the next thing isn’t quite the same as sending the best next thing.  In my ideal world, the content would be selected by calculating the value of each item for each individual and taking the highest. This would only take a small tweak in the current approach, which is one reason I like what Marketo has done. Marketo does in fact plan to apply predictive modeling to the system, although I think they're trying to find the most effective content sequence for all customers, rather than scoring content at the individual level.

There’s quite a bit more to the new Marketo feature than I’ve described so far. Content can actually be a multi-step program of its own, such as a sequence of messages to promote and manage a Webinar. Content can also have availability dates that are enforced automatically, so future messages can be added at any time and obsolete messages are automatically discontinued. One thing that’s missing is eligibility rules on content, to let users specify who is allowed to receive it. This is a key feature in traditional decision management systems, permitting customer-level customization within a fixed priority sequence. But Marketo users can achieve the same thing by embedding content within programs, which do have such rules, and adding the programs to the stream instead of the content itself. This is Marketo’s recommended approach because it also provides better data for reporting.

Users can further tailor treatments to customers by setting up multiple streams within one engagement program.  Each stream has “transition rules” that pull in qualified customers from other streams.  This is less rigid than having selection rules in one stream push customers to another stream.  It's not quite clear what happens if someone qualifies for more than one stream: Marketo's position is that would only happen if you make a mistake.  I think reality is not so tidy.  Marketo is considering letting marketers prioritize the transitions based on the position of the streams, just as they prioritize contents within a stream.

In any event, customers can only be in one stream at a time, so they won’t receive multiple messages from the same engagement program. Whether they receive messages from multiple programs is controlled by Marketo’s standard communication limit features, which can set a maximum number of messages per day and per week. Users can decide whether those limits apply to any particular program. The system also lets salespeople or other programs pause messages from an engagement program to an individual customer.

The new package includes a good set of reports that track content usage and results.  They also provide an “engagement score” that combines several success metrics into a single value. Other reports show how many people in the program have run out of content – a good way to ensure the company doesn’t lose touch with them. Surprisingly, there's no report on movement from one stream to the next. But Marketo says this can be set up using their revenue performance management module, which tracks movement of customers across other types of stages.

The engagement engine is included in all Marketo versions, although lower-level versions have some limits.  Adding a more powerful version to the Standard edition of Marketo starts at $295 per month.

Added Thought: Marketo engagement programs are a type of state-based system, an idea that has been tried from time to time in marketing systems and is currently the basis of Whatsnexx.   As the name implies, state-based systems assign customers to categories and then define treatment rules within each category.  Unlike the sequential flow of a traditional multi-step campaign, customers in a state-based system remain in the same category so long as they meet its membership conditions. State-based systems typically reclassify all members at the start of each cycle, which is different from Marketo's approach of relying on transition rules to pull customers from one stream to the next.  This means that someone could remain in a stream even though they no longer met its entry criteria.  This is something Marketo might want to reconsider.

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 thinkAnalytics Helps Marketers Optimize Customer Treatments

Summary: thinkAnalytics provides a robust decision engine to help make optimal recommendations across channels. Too bad more people don't use it.

As I mentioned in my post on PegaSystems’ acquisition of Chordiant, I’ve been planning for months to write about the thinkAnalytics recommendation system. The delay had nothing to do with any reservations about the product, which I find extremely impressive. It was more because I've been giving the topic low priority because the market for such systems seems to be moving slowly despite the clear benefits they provide.

The history of thinkAnalytics itself illustrates my point nicely. The company was founded in 1996 to offer K.wiz data mining software and had reached pretty much its current form by the early 2000’s. Indeed, the briefing slides the company showed me in mid-2009 were nearly identical its slides from 2007. The company also reported about twenty installations in both sessions. This isn’t to say that product itself has not evolved: it’s now up to version 8.0 and release notes on the company Web site show a steady stream of enhancements. But the fundamental approach has not changed.

This approach uses an “Intelligent Enterprise Server” to connect company touchpoints and data sources to thinkAnalytics’ data mining, recommendations and business rules engines. That is, thinkAnalytics sits outside of the individual touchpoint systems, allowing it to deliver consistent recommendations across all channels. These recommendations in turn are based information from on all data sources, not only those captured within a particular touchpoint system.

The advantages of consistent treatment and access to all company are self-evident. Of course, they do require identifying individuals across channels, so that, say, behavior during Web visits is linked to behavior at a call center. thinkAnalytics doesn’t directly solve this problem, but can make use of whatever linkages the company has built elsewhere. Its most common applications, churn reduction for telecommuncations companies and content recommendations for video-on-demand services, are in situations where customers explicitly identify themselves, so this is not an issue.

The technical hub of thinkAnalytics is the enterprise server, which needs to handle traffic among touchpoints, data sources, and the analytical components. The main issues with such servers are flexibility and scalability. thinkAnalytics addresses these by deploying a component-based architecture that lets it connect with virtually any external systems and can easily be distributed across platforms and servers to scale as necessary. The company says existing installations have scaled to thousands of decisions per second. Its client list is weighted towards very large firms – Vodafone, Virgin Media, Sky, orange, Lloyds TSB, and Alcatel-Lucent among them – who require this sort of volume.

But while the server may be the technical hub of the system, its heart is the analytic components: data mining, recommendations and rules engines. Data mining includes a wide variety of predictive modeling and data visualization capabilities, some fully automated, which feed into the recommendations themselves. The system can also import external predictive models from vendors such as SAS and SPSS. The system includes several specialized capabilities related to video content selection, including automated text analysis to create metadata and classify new content; capture of user preference ratings; handling of social recommendations; maintenance of personal profiles; and user-initiated search. The component-based architecture makes it relatively easy for thinkAnalytics to add specialized features in general, so the system could be adopted to other applications fairly easily.

The rules engine complements the recommendation rankings by letting managers apply constraints such as limiting the number of recommendations within any particular category. However, the system doesn’t provide sophisticated optimization tools, so it’s still up to marketers to manually discover the most effective rule sets.

Although the multi-channel capability of thinkAnalytics is highly impressive, the vendor says that most clients start using it in a single channel and add others a year or two later. This suggests that clients are primarily interested in the quality of the recommendations, and just secondarily in the cross-channel treatment coordination. thinkAnalytics reports that its telecommuncations clients have seen churn rates of 20% drop to 12%, while video-on-demand clients have increased sales between 30% and 55%.

Pricing for thinkAnalytics real-time components depends on the nature of the application. Factors can include the channels and applications, number of data mining users, and customer volume. A minimum installation for the recommendation engine starts around $250,000. The system is licensed for on-premise operation by the client.

The four components of thinkAnalytics (predictive modeling, recommendations, rules and a server to connect with the outside world) make it the very model of what is sometimes called a “decision engine”. As I noted in the Chordiant post mentioned earlier, most companies use the decisioning capabilities built into their touchpoint systems rather than buying a stand-alone product. But it’s still worth keeping the model in mind when assessing whether your touchpoint systems’ capabilities are truly adequate.

0 Pegasystems Buys Chordiant to Help Coordinate Customer Treatment Decisions

Summary: Pegasystems purchased Chordiant last week, adding a sophisticated cross-channel decision engine to its stable. It's been hard for independent decision engines to survive, even though it seems an independent product should make it easier for marketers to unify their customer treatments.

Business process technology vendor Pegasystems announced on Monday that it was purchasing Chordiant, which offers a central decision engine for customer interactions. Although the news is interesting in its own right, it also triggered a twinge of personal regret because I’ve been meaning to write about Chordiant for nearly a year. At that time, they had just added some slick simulation capabilities that estimated outcomes if a different set of rules had been applied to historical interactions.

This type of simulation allows business managers, rather than technicians, to directly assess the impact of alternative business rules. It's an important sign of maturity, showing that the vendor has shifted resources from primary system functions (making things work) to supporting functions (making things work better).

If you’re not familiar with the Chordiant decision engine, its primary function is to apply business rules that guide real-time customer treatments. It has been deployed primarily in call centers, although it is designed to work across multiple touchpoints. To accomplish this, the system must accept inputs from each touchpoint about a current interaction, apply rules to select an offer, and feed the selection back to the touchpoint. Tracking results also requires a second loop for the touchpoint to report whether the offer was actually delivered and whether it was accepted.

The business rules can use both data provided by the touchpoint and data from other systems such as transaction and marketing databases. The rules frequently include predictive models that can either be built within Chordiant or imported from other systems such as SAS or SPSS. Chordiant also supports self-adjusting models that monitor outcomes and modify future recommendations based on the results of different offers.

The appeal of a stand-alone decision engine like Chordiant is that companies can coordinate treatments without using a single vendor for all their touchpoint systems. This makes perfect sense, since in practice most firms do use different products for different touchpoints. In particular, Web interactions are often managed outside of the CRM system.

Yet it’s still been difficult for stand-alone decision engines to survive. Most firms use whatever interaction management features are built into the separate touchpoint engines and coordinate the rules administratively (if at all). Or they rely on interaction management features provided by their marketing automation system.

A few independent decision engine vendors remain, notably thinkAnalytics (another product I’ve been meaning to write about for months) and eGlue (which I wrote about here [update: a week after this post was written, eGlue was apparently purchased by interaction management vendor NICE Systems, although I've yet to see a formal announcement]). But it’s ultimately not surprising that Chordiant should end up as part of Pegasystems, with which Chordiant had already been integrated. The new relationship will let Pegasystems offer added value to its clients and better compete with CRM vendors.

As an aside, it's interesting to compare the position of decision management vendors with execution vendors like Conversen (which I wrote about last month) and ClickSquared (yet another vendor I hope to review shortly). Both sets of products unify a single function that is otherwise spread across multiple systems: offer selection for decision engines and message delivery for execution engines.

The challenges faced by independent decision engines may suggest that the execution engines will face similar problems. But the execution engines sit at the end of the messaging sequence, rather than in its middle: that is, they process outputs from marketing systems and send them elsewhere, rather than feeding them back into the same systems for delivery. This may make it easier for them to survive.