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.

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ClickSquared System Combines Marketing Database, Campaign Management and Multi-Channel Message Delivery

Summary: ClickSquared is marketing services agency that, unlike most of its peers, has built its own marketing automation system. The main advantage is tight integration of database build, campaign management and message delivery. The vendor has just officially launched its system, which should meet the needs of most mid-tier consumer marketers.

In a post last week, I casually described ClickSquared as a vendor delivering multi-channel messages for external campaign management systems. This was not wholly accurate. Although integrated multi-channel delivery is indeed a key differentiator for ClickSquared, the firm also offers its own campaign management system, called “Click 3G”. In fact, Click 3G was officially launched last week, although the company has been migrating clients to the platform since Fall 2008.

The more important clarification is that ClickSquared is a marketing services agency, offering database management, campaign development, creative, execution and analysis. The company got its start in 1999 as a direct mail house specializing in overnight execution of trigger marketing programs. Since then it has added email and other services through acquisitions and internal expansion. It now offers a relationships relationships ranging from full-service to self-service, with a particular focus on full-service solutions for mid-tier businesses and on special programs for very large enterprises. It sends emails for about 85% of its 150 clients and maintains marketing databases for about half of them.

In other words, ClickSquared competes with firms like Epsilon, Merkle and Acxiom for enterprise clients, and with a host of smaller firms for mid-tier clients. It also competes to some degree with email providers like Responsys, ExactTarget and InfoGroup YesMail, which are themselves expanding into other channels. (Apologies to all for over-simplification. Properly identifying the overlapping spheres of industry competitors would take a post of its own.)

One feature that stands out about ClickSquared is its choice to build its own campaign management system. This contrasts with the vast majority of marketing services agencies, which rely on industry-standard products such as Unica and Alterian. The fundamental argument for using industry-standard software is that continuously updating a home-grown system costs too much for most marketing services vendors, who can’t spread the expense across as many clients as a dedicated software company. Nor is software development a core competency of many marketing services agencies. Ultimately, this line of reasoning concludes, marketing services agencies compete on database management, analytics, marketing strategy and client service, so software is a poor investment for their necessarily limited resources.

To put matters in historical perspective, most big marketing services agencies did create their own campaign management systems when the category first developed in the 1990’s. But once satisfactory third-party products became widely available, the big firms largely dropped their in-house products. So it’s intriguing that ClickSquared (and a few other firms including Entiera , which I reviewed last July) have again chosen to build their own.

It’s much too soon to consider this a trend, but perhaps the cost/value relationship has shifted back in favor of in-house systems. The logic would be something like this: the prices of commercial systems haven't change, while the cost of building in-house systems has fallen because the requirements are well understood and developers can take advantage of third-party components and agile development methods. Thus, in-house development is relatively more attractive.

But in talking with ClickSquared (and Entiera, for that matter), I hear slightly a different story. It’s true that they avoid hefty license fees by using their own software. But main savings seems to come from integrating several capabilities, including customer data integration, message delivery and reporting, in addition to campaign management itself. This reduces both the total software cost and the labor needed to combine the separate systems. For example, ClickSquared says it can deliver a new marketing database in one to three months, compared with six months or more using third party systems.

Of course, an in-house system must still meet business needs for the savings to be worthwhile. Part of the reason that ClickSquared targets Click 3G at mid-tier companies is that their needs are somewhat less complex than enterprise marketers. That said, the system offers a respectable set of capabilities.

- Customer data can be loaded via API posts or self-service file uploads. The system provides automated data cleansing and customer matching capabilities. It can also gather data with an advanced email survey tool that supports for dynamic questions (i.e., questions change based on previous responses) and complex question types such as rankings and allocations. Marketing content can be uploaded and edited within the system and then shared across campaigns.

- Analytics are largely handled outside the system. These is no built-in predictive modeling, although scores can be imported and used as variables into segment definitions and business rules. The system does provide its own Web analytics module, or it can import data from Omniture or Coremetrics. ClickSquared captures online response using standard link tracking and can generate heat map reports showing how often different links were clicked within an email or Web form. Users can execute custom attribution rules during their database build.

- Campaigns are based on business rules. These can be executed in batch or triggered by events posted to the system API in real time. The rules can consider file segmentation, offer selection, channel preferences and limits on contact frequency when selecting messages. Click 3G also supports “distributed marketing” campaigns that allow users such as branch offices to execute predefined programs by setting a limited number of parameters. Campaign outputs can include dynamically-customized content for direct mail, email, and mobile (SMS) messages, as well as messages sent to CRM systems via an API.

- Message delivery for email is handled directly by ClickSquared, which helps to manage ISP relationships, ensures compliance with anti-spam regulations, and can spread large blasts over time. The system provides similar services for wireless (SMS) messages, although (like most marketing service vendors) it works with a third party to integrate with carriers. For direct mail, ClickSquared can handle preprocessing such as NCOA and then deliver a file of printer-ready personalized PDFs. Although campaign manager-to-email integration is more common today than when ClickSquared began, its multi-channel integration is still an advantage.

- The system also provides several “Web 2.0” options. Most notable is “clickShare”, which lets users register and then upload, share and comment on materials in an online forum. Other applications support referrals, mapping mash-ups and product ratings. Activities in these applications are fed into the marketing database, where they can be used for segmentation and triggers.

Click 3G lacks some refinements of the main commercial campaign management products, such as embedded predictive modeling and detailed project management. The vendor argues that its mid-tier clients don’t necessarily need such features, or at least need them less than tightly integrated database building and message delivery. Click 3G’s largest installations are currently in 15 to 20 million customer range, firmly within mid-tier territory.

Pricing for ClickSquared is based on the combination of professional and technical services used by each client. For Click 3G, factors include database size, channels used, message volume and system modules. A self-service client with 50,000 customers and 100,000 emails per month would pay $1,500 per month for the system. A client with two million customers and a proportionate mix of email, direct mail, text messages, surveys and social content would pay $15,000 per month. Clients commit to a contract of one year or longer.

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Real Examples of Social Media ROI

Summary: some published examples of "hard" ROI from social media.

As part of the preparation for next Tuesday’s Webinar with 1to1 Media and Neolane (register here), I poked around for some concrete examples of ROI from social media. Here’s what I found.

Socialnomics blog by Erik Qualman offers a dynamic video with 33 “salient examples and data points” about social media ROI. Some are pretty vague but the concrete ones include:

- Wine TV Library gained 1,800 new customers from Twitter

- Lenovo attributed a 20% reduction in call center activity to use of a community website for answers

- Burger King received 32 million media impressions from a Facebook app promotion costing less than $50,000

- Genius.com reports that 24% of its social media leads convert to sales opportunities

- Moonfruit sales of its Web hosting service increased 20% on a $15,000 social media investment

Jacob Morgan cites a Computerworld article describing how online community platform vendor Reality Digital generated 72 leads over the first three months of its social media project, at a cost of roughly $9,000. The company expected this to yield at least one sale which would cover the entire annual cost of the program.

ReadWriteWeb reports “a Cisco study in 2004 found that 43% of visits to online support forum are in lieu of opening up a support case through standard methods.”

Socialtext corporate blog cites an estimate by TransUnion CTO John Parkinson that his $50,000 investment in Socialtext has avoided $2.5 million in tech spending by helping users share ideas on how to solve their problems more cheaply.

10e20 corporate blog gives three examples of social media conversion:

- response to a LinkedIn group query became a 10e20 client

- a "couple of hours per week" spent social bookmarking the contents of an online magazine at StumbleUpon and other sites drove “10’s of thousands of visitors as opposed to hundreds”, resulting in much higher ad pay-per-click ad revenue

- major national fashion brand invested the equivalent of "one mid-level employee’s salary" to run a dedicated social media presence, yielding 75,000 fans and followers and “several hundred thousand dollars in new sales in three months of marketing” as well as reaching a new audience, improving public relations and customer service, and gaining feedback for product development

HubSpot's The State of Inbound Marketing 2010 survey found that 41% to 46% of the companies using Twitter, LinkedIn, Facebook or a company blog had acquired a customer from that channel.

Predictive Marketing Blog by Bob Hodgson reported that eight Tweets by a high tech conference with 350 followers generated 10 completed registrations worth $15,000.

I also found plenty of insightful content that doesn’t include specific numbers. In general, there are two schools of thought on social media ROI: some think it really must be tied to revenue and profits to be meaningful; others argue just as passionately that different measures are appropriate depending on the program objective.

Truth be told, my heart is with the “revenue and profits” school. But I suspect it may be too simplistic, so I do accept alternative measures as a valid alternative. The problem with tying social media to "hard" ROI is this often relies on complex intermediate calculations, which are subjective in themselves. That being the case, alternative measures are not necessarily less valid; it depends on the details. (Fallacy alert: just because neither is perfect, it doesn’t follow that both are equally bad).

In any case, here are a few discussions I found particularly worthwhile:

A SlideShare presentation from Peashoot (a social media campaign manager) listing different metrics for different campaigns. These are good examples even though there are no actual results.

An eConsultancy blog post sharing comments on social media ROI from a collection of British experts.

Another eConsultancy post listing ten specific ways to measure social media success.

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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.

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Matching Social Media to Your Needs and Resources

Summary: Marketers face so many choices that just deciding what to test is a major challenge in itself. Here are some ways to match social media to your business objectives and resources.

I’ll be giving a Webinar on March 23 (register here) with Neolane about cross channel marketing. At least that’s the official topic. In my mind, it’s really about helping marketers choose among the ever-increasing media options available today and in the future.

I won’t go into the details of the presentation, but thought I’d share this chart for selecting among social media.

The chart makes two major points:

- different social media meet different business objectives. I suppose this is self-evident, but it still helps to think about this systematically when you’re trying to decide which to explore. As the chart indicates, most social media can in fact serve more than one objective. Incidentally, the chart lists the objectives in roughly the sequence of the customer life cycle, starting with market preparation activities at the left and moving through purchase and post-purchase support, which further helps you visualize where a particular project fits into your larger customer treatment strategy. You may disagree with particular details on this chart, but that’s less the point than thinking about putting each medium into a larger context.

- media must be matched to your resources. This is also pretty obvious, but, again, it’s easy to ignore it when considering your options. It's also worth pointing out that resources include more than data, technology and experience. My list also includes public interest in your topic and media reach (i.e., your firm’s ability to attract attention to its program, largely by paid advertising). Both make possible social programs that would otherwise fail because no one would participate. It's worth noting that funding can make up for shortfalls in other areas and that strengths in other areas reduce the need for funds.

An Example

The table below gives a simple example of these ideas in action. It analyzes the situation of a hypothetical company facing a major product recall. Objectives in this case are “monitor and respond” to public opinion and provide “customer support” to previous buyers. Highlighting these shows that social networks, Twitter, message boards and Wikis are appropriate options. But let’s assume it’s a small company, with limited media reach and funding, and that it also lacks technology and experience for social networks and Wikis. This leaves Twitter and message boards as the best candidates -- Twitter because there's very little technology involved, and message boards because we assume that company has the necessary resources in place.



Although this example is limited to social media, the same approach can be applied to other media as well. Tune into the Webinar for more details.

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Eloqua SmartStart Speeds Marketing Automation Deployment, But It's Still Work

Summary: Eloqua's SmartStart gets marketers rolling in less than one week. It does require extensive preparation, but Eloqua leads you through that too. Let's face it, folks: putting a good demand generation program in place is real work.

Eloqua last week announced a money-back satisfaction guarantee for clients who participate in its SmartStart deployment program. Skeptical creature that I am, I wanted to hear the details before writing about it. By happy coincidence (OR WAS IT?), Eloqua Director of Key Accounts Jill Rowley scheduled a talk with me a few days later and filled me in.

SmartStart is a two-to-five day paid consulting engagement that helps new Eloqua clients fully deploy their systems. It’s not to be confused with the free QuickStart program (which I wrote about last May) which provides a smaller set of services. More than 150 Eloqua clients have now completed the SmartStart process, which is delivered by both Eloqua’s own professional services group and certified consulting partners.

The scope of SmartStart is indeed impressive. By the end of the program, marketers have initial email, forms, landing pages, Website tracking, CRM integration, reporting, and either lead scoring or nurturing programs. One key is preparation – the on-site sessions are preceded by extensive information gathering and technical groundwork, guided by Eloqua templates. This covers CRM integration, adding Web tracking scripts to company Web pages, assembling images and email formats, data cleansing, landing page subdomain set-up, specifying forms content and designing the lead scoring matrix. The process also includes a marketing maturity assessment that helps to define long term plans for improving the client’s marketing operations.

Rowley said most small companies can assemble the necessary information in a few days, although larger organizations take longer. Similarly, the SmartStart process itself works best for firms with relatively simple marketing operations, which Rowley said has less to do with size than numbers of regional offices and lead scoring programs, CRM integration, and existing automation. The single biggest challenge is the complexity of rules that govern CRM data synchronization, which can get very detailed when companies want different treatments in different situations.

The other key to the program is concentration during the SmartStart execution itself. The primary system administrator must devote full time to the project, while other users are brought in as needed. Because most policy decisions are made in advance, the company’s chief marketer doesn’t need to be constantly present.

The price of SmartStart varies from $4,000 to $19,000 depending on the version of Eloqua and type of CRM integration. Although that particular bit of information isn’t published, Rowley did point out to me that Eloqua’s Web site now shows basic price data, which used to be a closely-guarded secret. Pricing rules have also been vastly simplified.

That money-back guarantee? It’s good for six months and applies only to future portions of a subscription: so if you pay for a year and cancel after four months, you get refunded for the remaining eight months. That’s not quite a full refund, but it puts Eloqua on par with competitors who allow month-to-month agreements without an annual contract.

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