David Raab Article Archive

So much software is offered today to marketers that just trying to evaluate each new product is more than a full time job. But important as it is to understand the strengths and weaknesses of individual products, it’s also necessary occasionally to step back and understand how products relate to each other. This helps to ensure whatever product you are thinking about buying today will fit into the larger structure that will evolve over time. It also helps answer the important question of whether the product’s developer is likely to survive and prosper in the future.

Most computer industry analyses use a two dimensional matrix. This limits the amount of information that can be conveyed about any individual product, but it has the virtue of being easy to understand. Let’s just accept the two dimensional limit and consider what those dimensions should be.

The answer depends on what you’re trying to accomplish. Take the standard matrix used by a well-known IT advisory firm, comparing company “vision” with ability to execute that vision. Those are pretty useful measures if you’re considering investing in a company, either financially or as a buyer of its products. The vision axis gives some idea of whether a company’s products are likely to meet the long term functional requirements of a sophisticated user, while the execution axis hints at both financial stability and resources available to help less sophisticated users with implementation. In combination, the two measures are terrific at annointing “leaders” in a given category–an item of considerable interest to certain buyers and great promotional value to the vendors themselves.

Unfortunately, both measures are also highly subjective. In particular, you may not agree with an analyst’s definition of what constitutes a quality “vision”. More dangerously, this sort of competitive ranking implies there is a single “best” product for all users. In reality, user’s needs vary widely and the right product for one user may be totally inappropriate for another.

Let’s assume you want concrete help in selecting a marketing system. Now you want dimensions that more specifically indicate the functions provided by a product and differentiate similar products from each other. Of course, no two dimensions can capture all the issues. Still, some interesting efforts have been made.

One approach distinguishes analytical vs. execution functions–based on the observation that these have been done by separate systems in the past, but today some products offer both. Purely analytical products would include model building tools like SAS and ASA ModelMax, while pure execution tools would be telemarketing and list generation systems. Hybrid products would include Recognition Systems Protagona and Unica Impact!, which have tightly integrated modeling and campaign management. This method has several advantages: it distinguishes integrated from non-integrated products, helps determine which systems would be complementary rather than overlapping, and lets users choose the quality of analytical and execution functions they require.

But this method doesn’t indicate which channels a system supports. This means that an email broadcasting system and inbound call center application could occupy the same spot on the matrix–even though the two are utterly different products. It also means that a system supporting multiple channels looks the same as one supporting a single channel. Either way, the matrix is missing a key distinction.

It’s possible to imagine a matrix where one side represents the channels served by a product, perhaps arranging the different channels in a logical sequence such as cost per contact or speed of execution. The other dimension would then indicate how well the system supported each channel. The result would be a visual profile of the strengths and weaknesses of each product–a pretty useful thing for some purposes. But this approach displays each product as an irregular blob with multiple data points, which means the simplicity of the two dimensional matrix is lost. When more than a few products are plotted, the results quickly become unwieldy. If you want this type of detail, it is better to use a table with checkmarks or scores for each product’s capabilities in each channel or function.

A simpler approach that does fit within two dimensions would arrange systems based on the number of channels (or other functions) they support. The second dimension could indicate quality–that is, how well the system supports the channels it services. Like the earlier analystical vs. execution matrix, this breadth vs. quality approach has the problem of placing very different systems next to each other. But by putting multi-purpose systems at one end of the matrix and specialized systems at the other, it does distinguish two of the most common vendor strategies: providing a large number of integrated functions vs. doing a single function better than anyone else. This makes it very helpful for buyers who prefer either an integrated package or to assemble their own system from “best of breed” components. Such a matrix would also identify any integrated vendor with high quality components–since it’s at least theoretically possible for an integrated system to be good at everything. Of course, where multiple components are involved, the quality measure would need to be some sort of average, and thus require more detailed explanation to assess quality of individual functions.

As an alternative to quality, some analyses look at the breadth of vendor offerings–specifically, indicating whether a vendor provides software only, software plus supporting services such as application hosting or implementation, or services only. Such a breadth of function vs. breadth of service matrix is very helpful in further distinguishing different vendors’ strategies and identifying vendors who match a particular buyer’s needs.

A different approach focuses on the characteristics of systems that support different marketing functions–that is, distinguishing conventional campaign management from email campaigns, customer service systems from Web-based message delivery, and so on. It is possible to array these different systems based on response cycle (from batch to real-time) as one dimension and interaction complexity (from simple rules to complex customer strategies) as the other. Such a matrix would range from simple list generators (batch processing, no rules) in the lower left to online interaction managers (real-time reaction, long term strategies) at the upper right. Other types of systems would have different combinations: for example, recommendation engines like NetPerceptions give real-time results but rarely look beyond the goals of the current interaction (upper left); conventional campaign management software supports long term strategies with batch processing (lower right).

This matrix offers some interesting insights, since very different technologies are needed for batch vs. real-time processing and for simple rules vs. long-term strategies. In particular, it suggests that vendors claiming to straddle more than one category need to be questioned closely about exactly how they do it. It also raises questions about vendors who started in the simple rule segment but are now attempting to support more complicated strategies. For example, many of today’s “customer relationship management” products started with sales automation or call center systems (simple rules, real-time interaction). Based on where this puts them on the matrix, should be no surprise that campaign management is the weakest feature of their products. Conversely, the matrix correctly predicts that conventional campaign management vendors (batch processing, complex strategies) will have difficulty adapting their systems to handle real-time interaction.

By now it should be clear that no pair of dimensions can fully describe the relationships among different marketing software products. But it should also be clear that a carefully chosen matrix can highlight issues that are important in a particular situation. As always, the burden is on the user to understand her needs and structure an analysis that addresses them correctly.

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Last month’s column described the impossibility of capturing all the significant differences among marketing software products in a single two-dimensional matrix. It’s still impossible, but the reality is that buyers and vendors do need a way to make sense of the different systems. So let’s look at yet another matrix that at least manages to distinguish the main classes of products and how they relate to each other.

The horizontal dimension of this matrix measures reaction cycle–ranging from batch processes on the left to real-time interactions on the right. Batch processes sometimes run every few minutes, but in marketing systems they usually run no more often than daily, and many times just weekly or monthly. Whatever the interval, the important point is the systems respond too slowly to influence whatever transaction is taking place. By contrast, true real-time systems react immediately, in a few seconds or less, and therefore can participate in an on-going interaction. Common real-time systems are telemarketing scripts that tell an agent what to say next and Internet servers that return a page in response to a mouse click. Between batch and real-time are systems that react promptly but not immediately, such as customer support products that reply with an email or fax within a few minutes of a customer inquiry.

Loyal readers will remember that last month’s column also proposed a matrix with a reaction cycle dimension. The other dimension of that matrix had to do with interaction complexity, which roughly corresponds to the sophistication of the contact management strategy a system can execute. That was a pretty useful matrix, but it lumped together fundamentally different systems like low-tech call centers and high-tech collaborative filtering products (which both belong to the real-time, simple strategy group). And that matrix totally excluded modeling and analysis systems, which don’t manage interactions at all.

The second dimension of the new matrix measures analytical sophistication, which ranges from automated modeling systems (high) to user-specified segmentation schemes (low). Assume the high sophistication is at the top and low sophistication is toward the bottom. In between would be rule-based systems that can make sophisticated decisions but rely heavily on user input to specify the underlying rules.

It also turns out that analytical sophistication generally correlates inversely with execution capabilities–that is, systems built to execute marketing programs tend to have limited analytical power, while those with high analytical power rarely do much execution. There are some exceptions to this rule, but they are intriguing enough that it’s actually useful to have to deal with them separately.

So let’s look at how this new matrix lays out. It proposes two major distinctions: batch vs. real-time and analytical vs. execution. The four possible combinations do indeed correspond to familiar classes of systems:

– in the “batch analytical” corner (upper left) are the traditional advanced analysis tools, including conventional statistical packages like SAS and SPSS, neural network software like Trajecta and Advanced Software Applications, and multidimensional analysis tools like Hyperion Essbase and Oracle Express. In fact, sophisticated analysis has always required batch processing, which has become an increasing problem for marketers who want to reduce cycle times. The best these traditional tools can do is to build their models in batch, but score individual records in real-time or near real time.

– this leads to the “real-time analytical” corner (upper right), which today is populated by recommendation engines like Net Perceptions and Andromedia Likeminds, and by interaction managers like RightPoint, Manna FrontMind and Trivida. These products both predict a specific individual’s actions in real time and actually adjust the underlying models as new behavior is recorded. Like conventional modeling tools, the real-time systems have very little execution capability of their own–they only feed their predictions to other systems that manage the actual customer contacts.

– specifically, they feed “real-time execution” systems (lower right). These include conventional call center and contact management products like Siebel and Clarify, as well as personalized Web site systems like Broadvision and Vignette. Although there are major technical differences between conventional and Web-based execution systems, from a marketer’s standpoint they are just different ways to deliver the same contact strategy. So it does make sense for the matrix to group them together. And, regardless of the technical differences, vendors are striving to integrate the two sets of products–so there will soon be no choice but to treat them as one.

– the final corner holds “batch execution” products (lower left), which perfectly describes old-style campaign management software like Experian AnalytiX and MegaPlex FastCount. These products use proprietary database engines that are loaded in batch and used primarily for batch selections of mailing and telemarketing lists.

So far so good–the four corners of the matrix describe distinct and important classes of systems. In fact, people who care about such things might notice that the four corners correspond to the major components of a standard enterprise architecture: operational systems (real-time execution), data warehouse (batch analytical), campaign management (batch execution) and interaction management (real-time analytical). Kinda neat, huh?

But what about the spaces between the corners? Along the execution edge of the reaction cycle dimension (the bottom of the matrix), today’s advanced campaign managers like Exchange Applications ValEx and Prime Vantage might be considered “near batch” products: they mostly use batch loads and selections, but have schedulers and other functions that let them respond to events fairly quickly. They have also been integrated to some degree with outbound email and email responses, also pulling them slightly in the real-time direction. Further along that edge are email campaign managers like Responsys and RevNet, which are used primarily to broadcast batch-selected emails but can also capture email replies and issue a predefined response. Still closer to real time execution are email customer service systems like Acuity and Brightware, which can provide unstructured responses to email inquiries in near real time. Like the call center and Web site systems mentioned earlier, these products are increasingly being expanded to handle additional media, including true real time interactions such as telephone calls and live Internet chat. Nestled between email customer service and real time interactions are the various “marketing automation” products like Imparto and MarketFirst. These can handle both near-real-time response via email and true real-time interactions via personalized Web pages.

Above the pure execution layer lies the middle ground between execution and pure analysis. This is occupied by rule-based systems that rely on people to define a set of policies, but then can combine and apply them independently. Systems including Harte-Hanks Allink Agent and NCR’s CRM trio of Marketing Agent, InterRelate+ and Relationship Optimizer can scan for significant operational transactions in near real time and apply rules to determine how to respond. Black Pearl’s Knowledge Broker, along with RightPoint, can do the same thing in true real time.

Also in this middle ground are the exceptional products that offer both analysis and execution. (I have somewhat arbitrarily placed them above the rule-based layer.) In pure batch processing, Unica Impact! offers a powerful campaign manager plus extensive model building. E.piphany and Broadbase also combine analysis and selection capabilities, although they are less capable in both areas. In the near batch group, Recognition Systems Protagona offers its own integrated modeling, an excellent campaign manager, and a respectable degree of email interaction. Web traffic analysis–a batch or near-batch pure analytical application in products like Accrue and net.Genesis–is also combined with execution by several systems including iLux, GuestTrack and Personify.

As the list of exceptions suggests, today’s relatively neat distinctions can be expected to fray over time, as vendors expand their products to encompass functions in more categories. The matrix has other flaws as well: it doesn’t indicate which channels a product supports, doesn’t identify vendor services such as application hosting, and says little about quality. But it does manage to encompass most of the systems marketers worry about today, and hopefully that is useful enough.

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David M. Raab is a Principal at Raab Associates Inc., a consultancy specializing in marketing technology and analytics. He can be reached at draab@raabassociates.com.