A Framework for Real Time Decision Management: How SAS RTDM Fits In

I’ve had a couple of consulting projects recently that involve real-time decision systems (a.k.a. real time interaction managers), which are used to select the best treatment during a Web visit, telephone call, or other interaction.  This type of software has been around for two decades or more and repeatedly proven its value, but still has relatively few implementations.

There are many possible reasons for the slow adoption.  Maybe marketers don’t realize how much  improvement they get from driving recommendation with predictive models rather than simple rules.  Perhaps the decision capabilities built into delivery systems are already adequate.  The delivery systems are controlled by Web and call center managers who are not incented to generate revenue and may not be interested in a shared decision engine to coordinate customer treatments. Maybe each of these plays a role.

Still, the interest among my own clients has been enough to spur a fresh look at the vendors in this field.  To gather this information systematically, I need a framework that lists standard features and options within those features.  This makes it easy to isolate critical differences among the products.

For real-time decision managers, the framework includes:

• connecting to external systems.  This includes the touchpoints (customer-facing execution systems), such as Web sites and call centers, and other systems with relevant data, such as order processing and marketing databases. Connections to touchpoints are typically through Web Services calls; connections to other sources are usually made through API calls and SQL queries. The connections are set up during system implementation and then used in real time to look up information about a specific individual during an interaction. One important difference among real time decision systems is whether they look up information each time they are asked for a decision, or whether they look it up once at the start of an interaction and then retain it in a session until the interaction is complete.  The session reducing workload and helps to run multi-step dialogues. Another difference is whether the system maintains its own permanent database of individual profiles and contact history or must query external systems for all data.

• making decisions based on rules and predictive models. Rules are always available; systems differ greatly in how hard they are to build and maintain. Predictive models are optional.  They be built outside of the system, built within the system in periodic (batch) processes, or built and updated automatically. Systems also differ considerably in how they choose among competing treatments, a process called “arbitration”. The ranking may be as simple as picking the offer most likely to be accepted, or it may involve complex user-specified considerations such as offer value, sales targets, and business priorities. Some systems let users apply weights to multiple factors.

• integration with campaign and content systems. Early decision systems were not connected to outbound campaign managers or to content stores. But today they are often part of a larger marketing suite that includes an outbound campaign manager. The decision system may share campaign flows, offer definitions, customer data, analytics, and other features with the campaign manager. This simplifies training and facilitates integrated, cross-channel customer treatments. But even the unified systems typically run the outbound campaigns and real-time decisions on separate engines, each optimized for its particular type of processing. Regarding content: the decision systems traditionally returned a content ID that the execution system converted to actual content internally. When the decision manager is part of a marketing suite that includes a content repository, it can return the content itself.

• deployment model. Most real-time decision managers are deployed the old-fashioned way, as on-premise software. This gives clients the greatest control over security and performance. Some are cloud-based or vendor hosted (not precisely the same thing, but close enough), which simplifies deployment. Several vendors offer both options.

With that framework in mind, let’s take a look at another product in this group: SAS Real Time Decision Manager (RTDM).

RTDM meets all the framework requirements: it receives a Web Services request from an external system for a decision, runs the request through rules and models, and returns one or more choices. The results are usually displayed in a slot on a Web page or call center screen, although they could also be presented in an email, mobile device, or other channel.

RTDM leans toward the simpler end of most framework options. Each request loads fresh data from the touchpoint and other source systems, even within a multi-step interaction. At best, users can create continuity by storing a session token at the end of one interaction and retrieving it at the start of the next interaction. The systems returns tags, IDs or URLs but not actual content.

Decisions are based primarily on rules. These can incorporate predictive models, but the models themselves are built outside of the system, using SAS or other products, and do not self-adjust based on results. The system can select among multiple results by sorting on one or more user-specified variables, although any more complex arbitration requires custom coding in the SAS language.  Such  formulas could be registered in the system and reused across campaigns. Users can define a group of treatments, called a “campaign set”, that share a single set of eligibility rules. Individual treatments can also have their own eligibility rules that are applied whenever the treatment is used.

RTDM is tightly integrated with SAS’s campaign management system, SAS Marketing Automation. It shares the same campaign flow interface, treatment library, and database of contacts and responses. Predictive models built with SAS tools are also available to both.  Both use other SAS platform components including data structures, reporting tools, and other general functions. RTDM can be installed on-premise or hosted by SAS.

RTDM has been around in some form since 2008, although integration with the Marketing Automation treatment library is more recent. The system has sold more than 50 licenses, although fewer than half have been deployed. SAS says most deployments have been single-channel, single-purpose projects. Deployment has come slower where RTDM is part of a larger multi-channel deployment involving other SAS marketing products.  The other components must be put in place before the client is ready for RTDM.

Pricing of the system is based on the number of decisions processed or call center seats.  Cost starts around $150,000.

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SAS Unveils High Performance Analytics Technology

I spent the early part of this week at SAS’s annual analysts conference, where the company reviewed the past year and presented its vision for 2012. The story this year was simple: “big data”, and SAS’s “high performance analytics” approach to taming it.

Of course, “high performance analytics” is what SAS has always done and, like “big data” itself, the term is relative. What SAS specifically presented was a re-engineering of its core analytical procedures to run in “shared nothing” multi-processor environments.  Each data set is split into pieces that are loaded into separate units, processed independently and simultaneously, and then brought together for a result. SAS cited tremendous performance improvements, such as reducing the time to build a loan default model on a billion rows of records from 11 hours to 50 seconds. This obviously makes possible new, tactical applications.

The high performance architecture is becoming available in stages as each SAS procedure is rewritten to support it.  The change from a customer perspective is purposely minimal: existing SAS procedure calls are simply modified by adding a "HP" prefix.  This will make it easy for clients to take advantage of the new capabilities. 

The company revealed a just a few new products at the conference, most notably a Visual Analytics tool that uses in-memory processing to render billion-row data sets in seconds. But the real benefit of high performance will come less from new products than from using it with existing SAS procedures and tools. The SAS product that may benefit most of all is SAS Decision Management, which creates rule-based decision flows that can call on scoring models and other analytics to help guide tactical processes. The product itself isn’t new, but high-performance analytics will let it do new things.

SAS’s “big data” story also included Hadoop integration and expanded cloud deployments. By the end of March (if I understood the roadmap correctly), SAS will be able to read from and write to Hadoop data sets, embed Hadoop commands within SAS scripts, and send SAS metadata to Hadoop. Over the coming year, it will support cloud deployments through a variety of enhancements related to virtualization, open APIs, and eventually an app marketplace. The cloud-based initiatives also support SAS’s own on-demand business, which grew 57% last year to reach more than $100 million.

These are all positive developments for SAS, which must certainly support "big data" to remain relevant.  The new capabilities will also create some business changes as SAS competes more directly with companies like IBM and Oracle to embed analytics within operational processes. SAS itself noted the company is now more involved in architectural discussions of how its systems interact with the rest of the enterprise infrastructure. Other issues may include educating non-technical users and providing technology to protect privacy.  SAS leaders seem to think they can leave those issues to others, but I’m not so sure.

The conference produced little news directly related to marketing systems.  The company reports 38% growth in marketing applications – which it reports under the label of “customer intelligence” – so that is clearly a healthy business.  But the product road maps showed just incremental improvements of existing products, without any major new offerings. Again, high-performance analytics will make new things possible without other changes in the products themselves. The high performance version of marketing optimization is due by the end of the year.

If you want more evidence of how little attention was paid to marketing systems: SAS's biggest recent piece of marketing-related news, last week’s acquisition of online ad server aiMatch, got exactly one mention during the day-long presentation and was positioned as simply filling a small gap in the marketing product line. The company did announce, very casually, that aiMatch would be extended to include ad buying optimization as well as its current ad-selling optimization. That struck me as a pretty big deal, since ad buying is the heart of an already-huge industry that’s clearly the future of marketing. Then again, it’s also an intensely competitive, heavily-funded space that’s crawling with advanced technologies. Although SAS's high performance analytics could have a huge impact on ad serving, that won't happen unless SAS makes a major commitment of people and money.  We’ll see whether they make one.

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SAS Acquires AssetLink: Great for Enterprises, But What About the Rest of Us?

Summary: SAS's purchase of AssetLink ensures it's a viable alternative to IBM/Unica and Teradata/Aprimo for integrated marketing management. The real question is whether mid-size firms will be able to afford those systems.

SAS today announced its acquisition of marketing resource management vendor AssetLink. The move makes perfect sense: the other big MRM vendor was Aprimo, and once they were acquired by Teradata, SAS and AssetLink had no alternative partners in the enterprise marketing space.

Let me make clear that when I say “enterprise”, I mean “enterprise”, as in big business. Our friends at Gartner have recently proposed replacing “enterprise marketing management” with “integrated marketing management” as the general term for, um, integrated marketing management. Makes sense. But AssetLink told me their 50+ clients are all big companies. SAS’s marketing systems are also sold mostly to big firms. So we’re really talking about the “enterprise” market here. Once you starting considering mid-size businesses, there are other players, most notably MarketingPilot for MRM and Alterian and Neolane for business-to-consumer marketing automation.

Naturally, SAS and AssetLink don’t intend to limit themselves to enterprise buyers. Like IBM/Unica and Teradata/Aprimo, they hope to sell integrated marketing systems to mid-size firms too. This may be easier for the other two vendors: IBM and Unica certainly have more mid-size marketing clients than SAS and AssetLink, and although Teradata is mostly a big-company vendor, Aprimo has a broader client mix and a relatively new “Marketing Studio On Demand” product that was designed for smaller buyers. Still, we can expect SAS to try.

I'll admit to being skeptical that enterprise-oriented firms like IBM, Teradata, and SAS can successfully sell their products for the mid-market. The transition faces some technical roadblocks, mostly about hiding complexity and reducing the need for customization. But those can be solved. The larger challenges are rooted in corporate culture and require changes in areas like pricing and sales compensation. To put it more bluntly, enterprise firms like to sell big deals.

In fact, I think big deals are exactly what have attracted IBM, Teradata, and SAS to focus on marketing systems. They all seem to have adopted the grand vision of integrated marketing automation as a centrally-managed, analytically-driven process to coordinate customer contacts across all touchpoints. This requires integrating the marketing system with sales, customer service and Web systems. While I also love that vision, I suspect that only a few large firms will have the resources to implement it. This could mean that, in practice, integrated marketing management is limited to enterprise buyers.

This leads to another question: What are Oracle and SAP up to? They’re the other big enterprise software vendors and they’re surely interested in offering integrated marketing automation to their own clients, both to increase revenue and to block account penetration by competitors. Both vendors do have some marketing automation products but these don’t have much of a public presence. If integrated marketing automation really takes off at enterprise accounts, I suspect we’ll see Oracle and SAP pay more attention to this market, either through acquisition or enhancement of existing products.

You’ll notice I haven’t said much here about the SAS/AssetLink deal itself. That’s not due to any lack of enthusiasm: it’s just that the pairing was so obvious that it doesn’t require much explanation. It ensures that SAS’s marketing automation suite remains a viable alternative to IBM and Teradata, by giving it the scope that those other vendors possess. Of course, there are still substantial differences among the products, so buyers who are free to choose any of them will need to dig into the details and match them against their requirements. But I suspect that many companies will have a strong predisposition towards one vendor or another, depending on what other systems they have in place. In that case, there may be fewer truly competitive deals than we saw in the past, when marketing could choose its system without the rest of the company really caring all that much.

My bottom line, then, is that this deal doesn’t reshape the market, but it does clarify its structure and ensure that SAS continues to compete. My real concern is whether mid-size companies will be able to participate or integrated marketing automation will remain the preserve of enterprise marketers.

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Notes from SAS Analyst Conference

I spent yesterday at an analyst conference for SAS, being briefed on the company’s plans for 2009. The presentations are considered confidential, so I can’t go into details. But I think it’s okay to share a few points that struck me.

- credit and other financial risk analysis are a very strong business for the company right now. Of course this makes perfect sense, but it’s nice to see on two levels: first, that financial institutions are working on the issue and second, that there’s some good news somewhere.

- SAS sees itself as selling software applications for specific tasks (like credit risk analysis) rather than the underlying analytical tools. This isn’t news, but I hadn’t quite realized how fully the company had committed to this approach. One big difference with SAS is that they essentially create a separate data warehouse using SAS technology, instead of drawing on other systems for data integration and database management. The SAS “framework” (they avoid the term “platform”) might draw from a conventional warehouse, exist alongside it, or replace it altogether. But however you structure it, the SAS approach implies a relatively substantial investment to bring up the initial SAS application. The advantage is it's easier to add later applications – a major selling point when SAS tries to expand its footprint within an existing client.

This approach also puts SAS in conflict with several other parties: corporate IT departments who may view its foundation as redundant; other application software developers; and channel partners who may want to integrate solutions from several sources. SAS did mention several initiatives to cooperate with other players (except maybe other application software developers), but also seems quite willing to go it alone when necessary. The underlying attitude seemed to be that their solutions are good enough to win on their merits, and ultimately that’s what matters.

- improved user interfaces are a major initiative this year. SAS leaders mentioned several times that they had lost deals to less capable products that looked more attractive, and clearly this rankles. However, SAS seems to believe that better user interfaces are all about flash (and, in this case, specifically Adobe Flash). I saw and heard little that suggested a deeper understanding of usability as a way of helping people to do their jobs more efficiently and effectively. One red flag: the sample dashboard they proudly displayed included many gauges, which are almost universally rejected as a poor use of screen real estate. Ironically, one of SAS’s own presentations made this exact point about gauges—but then someone illustrated emailing a picture of a gauge as a way of alerting a colleague to a value. Ouch!

(In case it's not clear why you wouldn't email a gauge: if you just wanted to tell someone the number, you'd just send the number and save the person the need to look at the gauge, find the needle, and then relate it to the scale. If you wanted to show the number in context, you'd send a note along the lines of "revenue is $45,000, 20% ahead of plan". Again, much easier than trying to interpret values and color zones on the gauge, and conveys precisely the point you intended.)

But those are just my little quibbles. SAS remains a tremendously strong company with great technology, far-sighted management and dedicated employees. I have no doubt that they’ll continue to succeed and, more important, deliver great value to their clients.

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