David Raab Article Archive

Personalization Systems
David M. Raab
Relationship Marketing Report
April-May, 2000
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“Dear Current: Picture yourself and the entire Resident family on a fabulous, all-expense-paid vacation….”

Such are the embarrassingly plebian ancestors of personalized marketing. From its early days as an attention-grabbing gimmick on pitches for magazines and shady real estate deals, personalization has grown in respectability to become the bedrock of modern customer management. Indeed, some would argue that personalization pretty much IS customer management.

Like any grand success, personalization has attracted hangers-on who are eager to use its popularity for their own purposes. Today, nearly every marketing-related system lists the ability to help plan, execute or analyze personalized interactions as one of its main benefits. (Come to think of it, those pitches themselves are rarely delivered in a personalized fashion. But let’s not dwell on what this might imply.)

Of course, marketers rarely bother to define their terms with any precision. As a result, so many conflicting claims being made about personalization that it faces an identity crisis. So, without getting bogged down in the theory of why personalization is important in the first place, it’s worth taking a look at what different kinds of personalization systems actually do. (For a range of opinions on some of the broader issues, try the excellent Web site at www.personalization.com.)

The most ambitious usage of personalization treats it as synonymous with interaction management–a process whose goal is to generate the most appropriate response to any customer action. Obviously the key to this definition is the meaning of “most appropriate”, which really boils down to the question of “most appropriate in terms of what?” That is, what factors are taken into consideration when trying to identify the most appropriate response?

Part of the answer has to do with measurement–what, exactly, are we trying to maximize? Is it short term profit? Net present value? Customer lifetime value? Retention rate? Market share? Customer satisfaction? Loyalty?

Personally, I favor a measure of return on investment–making sure that each dollar yields the greatest possible long-term profit. This isn’t particularly controversial, although it does mean accepting less-than-maximum profits from relationships with some customers. What’s really important is the definition treats every decision as an investment–even decisions that involve no incremental out-of-pocket expenditure, such as allocating limited resources within a call center or field force. How to actually perform this sort of measurement is a knotty problem. Fortunately, personalization can be deployed without solving it, so long as marketers are willing accept whatever imperfect approximations are available.

The other answer to “in terms of what” has to do with which factors are considered in selecting a reply. This is where today’s personalization systems vary most broadly.

The first factor to consider is information about the customer herself. This might seem pretty darn obvious, but in fact it’s possible to create “personalized” promotions that treat all customers the same–as in the “Dear Current” letter itself. It’s also possible to treat customers differently without knowing anything about them as individuals–consider a Web site that shows different pages depending on the visitor’s domain name or incoming link. Even systems that do identify individuals and treat them differently can vary significantly in the types of data they access. These range from a simple segment code appended during a batch process, to real-time look up of detailed historical information such as past purchase transactions, to access to information provided during the current interaction.

In fact, the breadth of available customer data is a significant differentiator among today’s customer management systems. For integrated Customer Relationship Management products like Siebel, a single customer transaction database is touted as a key advantage over single-purpose call center or sales automation systems that do not share data across touchpoints. Among Web-based personalization products, vendors who can integrate data from non-Web “legacy” systems take great care to distinguish themselves from products whose personalization is limited to profile data gathered within the system itself.

The second factor that systems might consider is the current interaction itself. Again, this may seem trivial: surely if the purpose of a personalization system is to respond appropriately to the current interaction, then the system must take that interaction into account? Yet, again, there are current products that do no more than distribute messages chosen in advance–say, by running a nightly batch process that produces a list of messages to be sent to individual customers if they should appear the following day.

This is nowhere as silly as it might sound. In fact, if each customer interaction is considered an opportunity to deliver a free advertisement, then delivering no message during those interactions would be as wasteful as buying a minute of TV time and broadcasting dead air. Viewed from this perspective, even a slightly off-target message is better than nothing–so long as some mechanism ensures the message isn’t so inappropriate in the context of the transaction that it actively antagonizes the customer.

Still, it does seem much smarter to deliver messages that are directly related to the current transaction. Obvious examples are recommendations for related products (cross sell) or more profitable alternatives (up sell). Many systems provide this sort of personalization, although they often develop the recommendations without considering the individual involved. For example, systems frequently present a list of products that are most often purchased together, without checking whether different recommendations might be appropriate for different customer segments, which products this particular customer has purchased or whether the customer has a history of accepting such suggestions at all.

If there’s no hope of a customer accepting the recommendation, then making it is a poor investment of company resources. Of course, the expected return on the investment depends on the expected revenue compared with the cost, and cost itself varies by channel: an email is much cheaper than the time of a call center operator. Then again, even a “free” Web message may carry a cost in terms of customer annoyance. As already noted, resolving this with absolute precision requires advanced metrics that do not really exist. But it’s still possible to set intelligent policies without those metrics–so long as the system provides access to the relevant data.

Systems also differ in exactly how much information they take into account about the current interaction, and in how broad a range of responses they consider. For example, some Web-based systems can distinguish the behavior patterns of a leisurely browser from that of a hurried buyer, and treat each appropriately. Or, to be a more precise, some Web-based systems capture enough data to make these distinctions based on rules defined by a marketer. Even the best of today’s Web-based systems couldn’t discover and react appropriately to these categories by themselves. Similarly, some personalization systems can consider a wide range of responses, such as whether it’s better to make a cross sell offer or to send the customer a thank-you note. This calls for more sophistication than just choosing the best cross sell offer, since it’s harder to find a common basis to use in ranking the alternatives. Once again, this is an issue of metrics and once again, today’s systems would mostly rely on rules created by marketers rather than on rankings the systems develop autonomously.

A third factor considered by some personalization systems is the customer’s status in current marketing campaigns. Many of today’s marketing systems assign customers to one or more campaigns that involve a sequence of multiple messages. Part of the challenge faced by any marketing system is to limit and coordinate these assignments so the customer is not overwhelmed by too many messages or sent messages that conflict. Like personalization, this poses the challenge of ensuring that the most productive of all possible choices are made. But campaign assignment is generally done from a campaign rather than customer orientation. (That is, the question is which customers to assign to a given campaign, not which campaigns to assign to a given customer.) This is not itself a personalization issue.

Where personalization and campaigns do intersect is when choosing the specific message to deliver during an interaction. If a customer is active in multiple campaigns, then the customer may be eligible to receive several different messages at the moment an interaction occurs. Since the system can presumably send just one message at a time (or, at least, needs to rank the different messages by priority), the personalization system must choose among them. This may be the knottiest of all the analytical problems associated with personalization, since it requires estimating not just the value of message by itself, but the incremental value of one message within a sequence. In fact, a truly thorough system would go further to estimate the value of repeating the same message compared with switching to a different message later in during the same interaction, and also somehow take into account the number and timing of future message delivery opportunities. It’s not even clear that a sound theoretical basis for making these decisions exists today. Certainly no personalization system can make such decisions in practice.

Still, decisions must be made. One common approach is to prioritize the campaigns when they are set up, using a single sequence that is the same for all customers. This has the virtue of simplicity, although it’s possible that a particular campaign will be of greater value for a specific customer than some other campaign that is ranked higher over all. In fact, it seems almost certain this will happen in some cases. When differences among the values of messages within the same campaign are added into the mix–for example, the sixth renewal offer to a magazine subscriber is worth less than the first–choosing solely on the basis of campaign priority seems inevitably suboptimal.

Some of these problems can be overcome through setting different priority sequences for different customer segments. If these segments are defined with enough precision, then the campaigns should be ranked correctly for almost every individual. Most systems that accommodate campaign rankings can be made to support this approach, although it might take some awkward tricks–such as creating several versions of the same campaign with different rankings and selection rules. A refined scheme along these lines is likely to be an administrative nightmare.

A more manageable approach would be to define campaign priorities on an individual basis. This could most easily be done when the customer is first assigned to a campaign. After all, if the system is well designed then it should already calculate the value as part of the selection process. Ideally the value would be recalculated over time. This could be done at fixed points during the life of the campaign–say, after every message–which would take into account the change in expected returns. Or, the value could be recalculated on-the-fly when messages need to be chosen. Systems that can execute scoring models in real-time can perform these sorts of selections. Sometimes the scores are used within a larger rule-based framework that makes broad choices–say, between retention campaigns and cross sell campaigns–before letting the system make the final specific selection. Since a customer may be eligible for many different campaigns, some sort of rule-based filter may in fact be necessary to limit the number of score calculations during each step in an interaction.

Still simpler methods of choosing among campaigns are also employed, and may even be appropriate when meaningful campaign values cannot be calculated. For example, systems that display Web page banner ads often look at a pool of messages for which a customer is eligible and select from this pool at random or in a rotating sequence. These systems often support general business rules such as the number of times a message can be displayed to a customer, the number of days each message is active, and the interval to wait before showing the same message again. Other approaches select campaign messages based on their scheduled delivery date–choosing whatever message is due soonest. This assumes the campaign system generates a list of future messages, which only some of them do.

Personalized messages also often vary depending on the medium in which the message is to appear. Ideally, this would be based on a measure of the effectiveness of different messages in different media. For example, a complicated offer that can be explained in detail on a Web page may be unsuitable for the one-line display of an automated teller machine. Similarly, some media can easily present multiple offers simultaneously while others cannot.

Few, if any, systems explicitly consider medium in their message selection mechanism. But separate versions of the same message must generally be prepared for different media, so marketers can implement a crude form of medium-based differentiation by simply not creating message versions for some media. Much of the value of this strategy depends on what happens when a message version is not available for the current medium. If the system goes back and selects the next-most-valuable message, there is a reasonable chance that something approaching maximum value will be achieved. But if the system simply presents a standard default message–or no message at all–the value is considerably less. Of course, the worst possible situation is if a missing message causes the system to crash altogether. Given the inherent complexity of managing an advanced personalization process, it’s essential that missing versions be handled smoothly and automatically. Of course, all this is a poor substitute for explicit measurement of message effectiveness–in a given situation, even a marginally effective version of the right message may be more valuable than the alternative.

This all assumes a central system is choosing messages to be delivered in different media. In fact, many systems today are limited to a single touchpoint such as Web pages or call centers. These systems often are highly sophisticated in some of their personalization abilities, but of course cannot coordinate messages across media. At best, it’s up to the marketers in charge to create rules and messages for different media that will generate similar outputs in similar situations. But the administrative effort required for such an approach makes it doomed under all but the simplest circumstances. This is a particularly important issue for systems that service just one side of the Internet/non-Internet divide. Unless customers conveniently limit themselves to one side or the other–which most won’t, given the option to use both–maintaining consistent customer management policies will likely prove impossible.

Channel workload is still another factor that some personalization systems consider. The most common examples are inbound telephone call centers, where managers routinely turn off options such as cross sell recommendations during busy periods to reduce the time spent on each call. Ideally, the system itself would decide the degree of personalization used for each call, balancing the value of maintaining a high service level with the value expected from extending a particular interaction. It’s quite likely that an accurate calculation would show that the value of personalization in some calls–say, where there is a very high probability of a lucrative incremental sale–outweighs the loss in revenues from other customers. As usual, a precise on-the-fly calculation is hard to develop but rules can provide a reasonable approximation. This is why elite members of frequent flyer programs have a special phone number for reservations.

Personalization can also be balanced against service levels at retail point of sale, kiosks, automated teller machines, and even high-traffic Web sites. Outbound marketing campaigns sometimes take into account other channel capacity issues, such as the number of leads that can be handled by a field force. In this case, the system has to determine which leads will yield the greatest value and automatically assign the rest to alternate channels such as telemarketing or email. This can be a tricky problem, since there may be other constraints such as target sales levels for individual products or a required mix of business for individual salespeople or dealers. In non-real-time situations, the system must also consider future capacity–it may make sense to hold off sending some leads to telemarketing today, if there is a high probability that a field sales person will have time to handle them tomorrow.

Finally, personalization systems must also adjust to other business considerations such as inventory positions, demand levels, and special promotions. For example, it makes little sense to cross sell a product in short supply–profits will be higher if the company offers other products and saves the product in limited supply for customers who request it by name. Conversely, it makes considerable sense to push a product that is overstocked, since it otherwise may end up as excess inventory. Airline yield management programs are one example of promotions that are dynamically adjusted to market conditions, although these are generally not tailored to individual customers. Better examples are programs that offer discounted tickets on excess seats to customers who have indicated an interest in flying to a particular city. While the details vary depending on the exact nature of the business consideration being accommodated, the general principle is that the personalization system needs to access information about that consideration and somehow factor this information into its value calculation.

By now, a few points should be clear. The first is that even though rule-based approaches are by far the most common today, they cannot manage the complexity of a system that considers all the listed factors–customer, current interaction, campaigns, medium, channel workload, and business constraints–in its personalization decisions. The only practical solution is a value calculation that produces a simple ranking of alternatives. While current value calculations are admittedly imperfect, they at least have the potential to be improved. By contrast, the rule-based approach is ultimately a dead end.

The second point is that there is no fundamental distinction between the personalization requirements for real-time, interactive systems and for outbound, batch-oriented campaigns. Both must make decisions based on a view of the entire customer relationship; in fact, each must take into account actions by the other. Similarly, there is no fundamental difference between “proactive” systems that scan transactions for opportunities and “reactive” systems that guide interactions initiated by the customer. Even though different technologies are needed to execute personalization through the different types of systems, they must share the same core decision functions.

A third point is that personalization systems must include mechanisms to help marketers refine their business strategies. These will range from standard test capabilities such as random sampling and champion/challenger comparisons, to automated data analysis, relationship discovery and model creation. They also include extensive storage of historical data, to allow the reconstruction of the conditions under which each interaction took place. Many of today’s personalization systems are very weak in these areas. But personalization cannot meet its promise without a systematic process to improve decision making. And as a practical matter, it will be virtually impossible to deploy such a process unless it is built into the personalization system itself.

The final point is that personalization systems will evolve into specialized products that are distinct from the operational systems which deliver the personalized messages. This is a substantial change from today’s situation, where most touchpoint systems provide their own personalization capabilities. Core capabilities of the new breed of personalization systems will include access to data from multiple sources, sophisticated value calculations, business strategy optimization, and communication with message delivery systems. Primitive versions of such solutions are beginning to appear, but it will be some time before a complete implementation is available.

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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.

Differences Among Campaign Managers
David M. Raab
Relationship Marketing Report
March, 2000
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Is campaign management a commodity?

Not so long ago, campaign management vendors competed on core capabilities: which system handled the most complicated promotions, included the most data, ran fastest, or was easiest to use. But over the past year or two, the terms of competition have shifted. Now vendors tout the scope of their offerings, citing integrated analytical reporting, email production, and statistical modeling. Basic campaign management itself–the ability to select names, keep a promotion history, and match responses to promotions–is almost an afterthought.

In some ways, this is a natural result of past industry trends. Over the past decade, systems that use standard or “open” relational databases like Oracle or SQL Server have steadily displaced products that use “proprietary” databases created by the campaign management vendors themselves. The main argument in favor of the open systems was that the data could be read by any number of third-party reporting and analysis tools. This eliminated a dangerous dependence on the campaign management vendor. But precisely because the “open” systems all use the same database engines and query language (SQL), their performance depends more on the underlying database engine, database design and hardware than on the campaign managers themselves. Similarly, third-party query, reporting and analysis tools are at least as good–and often better–than anything a campaign management vendor can build for itself. So, having given up control over most of the things that previously differentiated them, campaign management vendors now need new ways to compete.

Integrated suites meet this need nicely. They simplify implementation–one of buyers’ key concerns–while helping shift attention away from specific functional details. There is some irony to this development, since the integrated suites are in some ways as “closed” as the proprietary systems they initially displaced. Of course, most vendors don’t see it that way.

But back to the original question: are the campaign management functions in today’s leading systems so similar that buyers need not examine them in depth?

I think not.

Take query complexity. This is where the constraints of SQL bind most tightly: nearly any modern system can support a simple SQL query, and very few systems can do much else. The simple functions boil down to examining a single field to see whether its value is equal, greater than or less than a specified value or another field in the same record. Most systems can extend the comparison to include simple math operations such as addition and subtraction, and can also combine separate statements with “and” or “or” conditions (although it’s easy to make mistakes in setting up “or” logic.) But it’s hard to generate SQL to handle other situations, such as finding sets that do not contain a particular value (practical example: people who haven’t purchased a given product) or summarizing many-to-many relationships (example: multiple purchases linked to multiple returns). Most of today’s SQL-based campaign managers don’t even try to support such queries, relying instead on precalculation, hand-written SQL, or multiple steps in a segmentation flow. The most important exception, Decision Software Inc.’s TopDog (www.dsitopdog.com), actually extracts the data from the relational database, manipulates it without SQL, and then reinserts it.

Embedding precalculated values in a record is one way to overcome some SQL limitations. Today’s systems also vary considerably in this area. The most powerful allow users to design, save and reuse complicated formulas that incorporate multiple variables and can even look up values in related tables (for example, the median income of the customer’s Zip code). Systems also differ in whether the values can be recalculated automatically as a query is executed, and how much work it takes to store them permanently on the database.

Promotion complexity is also tightly bound to the nature of SQL. Most of today’s systems allow users to define hierarchical nested selections: that is, they apply a series of queries that use the records selected (or rejected) by one query as input to the next, in order to split the database into ever-finer segments. This is another way to create complex selections that would be difficult or impossible with a single SQL statement. The differences among existing systems arise in two areas. The first is that not all products actually allow this sort of nesting–some limit the number of levels of queries that are allowed. More subtly, systems also differ in whether they literally use the output of one query as input to the next, or instead generate independent queries that all run against the main database itself. So long as the independent queries reexecute the logic of all the preceding queries, either method will yield the same result. But the independent query approach requires a great deal more processing where large databases or many segments are involved. This can raise a scalability issue. Some products offer a choice of whether the queries are independent or not, although even within this group there are differences in how easy it is to set up the processes.

A related distinction is how systems handle segments that are separated in time–for example, where one message is sent thirty days after another. Here, the different queries pretty much have to run independently, since the latter message may depend on customer actions after the initial selection. The difference among systems lies in whether they create the underlying queries automatically, based on an interface that lets the user simply specify a time interval. Many do, but others require the user to build the time relationships into the queries themselves. This is difficult and error-prone, and makes it impossible to show the relationships on system reports.

Query and promotion complexity are areas where marketers’ needs vary widely–which is why leading systems have found it possible to treat them differently. Similarly, there are still large differences in the products’ ability to limit the number of promotions sent to an individual, in support for complex response analysis, and in administrative functions such as budgeting, project management and approvals.

On the other hand, some requirements are pretty much universal and these have been incorporated in every leading product. A good example are the Nth or random selections needed for standard testing procedures. These are available in all major systems even though random selects are quite difficult to create in standard SQL. Today’s systems also let the user design the underlying customer database rather than forcing data into a fixed schema, although many do have a standard schema for internal data such as campaign information. Most products also include job schedulers that can execute campaigns automatically over a period of time–another key requirement for successfully executing an advanced database marketing program.

In short, there are still substantial differences in the core campaign management features of today’s leading products–differences that can cause major problems if they are not compared to user requirements before a system is purchased. So while integrated suites offer real and intriguing advantages, it’s not yet safe to ignore the details of their embedded campaign managers.

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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.

Mergers Point to a Consolidated Industry
by David M. Raab
Relationship Marketing Report
January-February, 2000
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Maybe it’s a sign of maturity or maybe it’s a sign of decadence, but today’s marketing software companies now seem more interested in acquiring other people’s products than building their own. The past year saw a spate of mergers and acquisitions among members of the industry. Some were traditional efforts to strengthen capabilities related to a company’s core offering. For example, Kana (www.kana.com), a provider of email response management software, purchased companies that do outbound email campaigns (Connectify), real-time Internet dialogs (Business Evolution, Inc.) and self-service Web-based support (NetDialog). Similarly, Sagent (www.sagent.com), a provider of tools to help load operational data into a warehouse or marketing system, acquired QMSoft, which has specialized technologies for data matching and enhancement. Roughly in this category–although perhaps more a consolidation in a shrinking segment–was the combination of Retail Target Marketing Systems with Experian’s AnalytiX group. Both provide traditional campaign management systems using proprietary database engines–an increasingly tough sale in a world committed to using standard technologies.

But other acquisitions had a bolder purpose: assembling a complete set of customer management capabilities. This group includes E.phiphany’s (www.epiphany.com) purchase of RightPoint, Broadbase’s (www.broadbase.com) purchase of Rubric, and, to a lesser extent, Exchange Applications’ (www.exapps.com) purchase of GBI and ClientLogic’s (www.clientlogic.com) purchase of MarketVision. The object of such transactions is to provide an instant infrastructure to firms with no existing customer management capabilities–transforming them from laggards to leaders in one simple step. Clueless to flawless, as it were. For marketers afraid of being left behind as their competitors do a better job of customer management, this is an irresistable proposition.

It is also the purest snake oil. Any experienced marketer or remotely honest vendor will tell you that the real work in becoming customer-centric is the organizational and process change. Delusions that simply buying the right product will solve the problem will be quickly shattered–though not until after the vendor has been paid.

This does not mean that integrated product suites lack value. The technoloyg of customer management is far from trivial, so selecting appropriate products does have a major impact on project success. This means it’s worth looking at just what the vendors of integrated suites have to offer.

The first step is to establish a framework for comparison. Nearly every marketing product claims to support “closed loop marketing”. The general impression conveyed by this term is of a system that executes marketing programs and captures the results so they can be evaluated and fed into improved future programs. But it turns out there are more loops in customer management than a three-mile roller coaster. Vendors naturally tend to define the kinds of loops that they can actually deliver. So it’s important to identify the components of the full customer management cycle in order to see which pieces any given vendor can support.

In broad terms, the cycle can be broken into five components. The first includes ‘front office’ systems that actually interact with customers: these are where transactions originate and where, eventually, marketing messages will be delivered. The second component includes the data extract and preparation tools that pull transactions from the front office systems and load them into the marketing database or data warehouse. For simplicity, assume this component also includes the database itself. The third component is the analysis tools used to assess the marketing data and guide marketing strategies. The fourth component is the campaign engine that assigns customers to groups and associates those groups with specific campaigns that embody the strategies. This component also generates outbound campaign messages. The final component is real-time interaction management, which feeds marketing decisions to the front office systems as customer interactions occur. Reactions to these decisions are captured by the front office systems and then fed back into the marketing database for analysis, thereby “closing the loop”.

It seems self-evident that a true “closed loop” system would contain all five components. But in fact there is a great divide in the marketing system world. Standing alone on one side are the front office systems, which are large, real-time transaction processing products run by operational departments like sales and customer service. These systems may have thousands of users in a large company; because of their size, they are where most of the money in customer management is spent. This is the turf of traditional “customer relationship management” (CRM) vendors like Siebel, Vantive and Clarify, and is being approached by other enterprise software vendors like Oracle and SAP. A related and somewhat overlapping set of vendors like Pivotal, Kana, Brightware and Silknet provide Internet-based front office products.

On the other side of the divide is everybody else: database, analysis, campaign and interaction management tools. These systems have much smaller numbers of users and (except for interaction managers) use mostly batch processing, which is better suited for most kinds of analytical work. Traditionally, these components have been provided by many small vendors who are specialists in their particular field.

With this picture in mind, the recent mergers and acquisitions come into clearer focus. E.piphany, Broadbase and Exchange Applications all are on the non-front-office side of the cycle, and so are the companies they acquired: E.piphany provides data transformation, analysis and some campaign management, to which RightPoint adds interaction management; Broadbase provides data transformation and analysis, to which Rubric adds campaign management; Exchange Applications provides campaign managment and some analysis, to which GBI adds outbound email. Thus, these acquisitions all represent an attempt to build multi-function integrated systems that can rise above single-function, stand-alone products that have traditionally dominated this sector. This strategy has several advantages: it leads to larger revenue streams; it makes the vendor attractive to companies that are new to customer management (still the vast majority); and it lets the vendor win against single-purpose products that are superior within their specialty. In fact, by helping deny revenues (and investment financing) to single-function products, the strategy has the welcome long-term effect of reducing competition and stifling the growth of firms that might eventually grow into full-spectrum rivals.

No strategy is guaranteed to succeed, and this one does has its drawbacks. The most obvious is that not all firms want to buy an integrated solution–and, in fact, the companies most likely to want a complete customer management solution are precisely the early adopters who already have partial solutions in place and therefore only need some incremental enhancements. These are a minority of the business world but may well constitute the majority of the market for the foreseeable future. Of course, the integrated product vendors are willing to sell their different modules separately. But these components are often less than best-of-breed and will be increasingly difficult to deploy independently as they become more tightly integrated with the rest of the vendor’s suite.

There is also the cost of expending technical and management resources on absorbing acquisitions rather than adding new system functions. This could lead the intergrated vendors to fall further behind specialists who create best-of-breed products in one area and build alliances with best-of-breed providers in others. This is the strategy adopted by Exchange Applications in setting up alliances with SAS and MarketSwitch for predictive modeling and with Microstrategy for reporting.

The integrated vendors also risk a backlash when buyers and investors realize that their “closed loop” products exclude the front office portion of the customer management cycle. Software buyers, who are used to vendor hyperbole, will presumably make this discovery between any money changes hands. But the investment community, entranced by visions of a multi-billion dollar CRM software market, may be less forgiving when discover most of the funds will be spent in the one area–the front office–that these vendors exclude.

Indeed, skillful handling of front office integration will probably be the key to long term success for the non-front-office vendors. Because the sheer size of the front office systems–in terms of complexity, number of users, operational impact and acquisition cost–they tend to be purchased on their own merits rather than as an adjunct to the other customer management products. This means it would be extremely difficult for a non-front-office vendor to successfully introduce its own front office products: in fact, this is so obvious that none of those vendors has even tried. Instead, they have created generic data exchange capabilities and, in many cases, have also built specific connectors to major CRM systems. Their problem is that the front office vendors themselves have their eyes on the non-front-office sector, and can either build or buy the components to provide the necessary services. So far, there have been no major acquisitions across the front-office/non-front-office divide. (ClientLogic’s purchase of campaign management vendor MarketVision is a near exception, but ClientLogic provides front office services rather than software.) But when the front office vendors start crossing the line, the non-front-office vendors will face a serious challenge.

Since interaction management represents the connection between front-office and non-front-office systems, it is the most likely point of contact between the two sets of vendors. Interaction management is also an uneasy fit with other non-front-office products, since it is inherently real-time rather than batch. This makes E.piphany’s acquisition of RightPoint–the most mature interaction management product–particularly intriguing. How E.piphany integrates RightPoint with its other offerings and manages its connections with front-office systems will be extremely important to its long term success.

Other products for interaction management are being developed mostly by traditional campaign management vendors (Exchange Applications, Harte-Hanks, Recognition Systems, Prime Response) or independent companies (Verbind, Trivida, Manna FrontMind, Black Pearl, DataSage). Whether the front office vendors acquire any of these, develop their own interaction managers, or leave interaction management to others bears close watching by both customers and investors in the marketing software industry.

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The thrust of last month’s article was that mergers and acquisitions among marketing system vendors have generally expanded capabilities in either front-office or non-front-office areas, but rarely crossed the line between the two. (Front office systems, such as sales automation and call center systems, execute customer interactions; non-front-office systems, such as campaign managers and data analysis tools, help define strategies to guide those interactions.) The split is not surprising, since the two kinds of systems involve fundamentally different technologies and users.

But the split is also inevitably temporary. Once vendors fill out their capabilities in either area, they will want expand into the other to generate more revenues, block entry by potential competitors, and serve buyers who prefer one-stop shopping. While vendors in both areas will eventually want to expand, most of the revenue is on the front office side. This means the front office vendors are much more likely to become the buyers. Front-office systems are generally priced on the number of users, while non-front-office prices are usually determined by the size of the database. So this will be the Invasion of the Body Counters.

No sooner was last month’s article written than it became clear that the invasion had begun. In December, SAP (the largest provider of enterprise resource planning software, and a new contender in front office systems) announced an alliance with Recognition Systems Inc., developer of one of the most advanced and integrated campaign management/data analysis/email marketing tools. Ordinarily, alliances among software vendors are about as significant as two junior high school students announcing they are “going together”–they’ll talk on the phone a lot and attend a few parties together, and that’s about it. But SAP has historically avoided such relationships, so their decision to participate may actually indicate more significant intentions. If nothing else, the deal represents a recognition of the importance of non-front-office marketing functions as components of a complete customer management solution. Tactically, it also seems to be an attempt by SAP to outflank rivals who are well-entrenched within the front office itself.

Chief among these rivals is Siebel Systems, which countered SAP in January by purchasing Paragren Technologies, another provider of advanced campaign management software. Paragren relies on third-party systems for statistical analysis, but does have some interesting capabilities in data extraction and aggregation–other key functions missing in traditional front office systems. Since this is a full acquisition rather than an alliance, close integration of Paragren with other Siebel products seems more certain than integration of SAP with Recognition Systems.

While SAP and Siebel are the market leaders in the enterprise resource planning and front office segments respectively, Oracle is a major player in both. It has also moved to expand its non-front-office marketing systems, acquiring Darwin data analysis software from Thinking Machines and releasing significantly enhanced versions of its own campaign management products.

Of course, Oracle is primarily known for its database management software, and its purchase of Darwin also fits a pattern among database software vendors of expanding their analytical capabilities. For example, NCR last year added substantial analytical capabilities to its Teradata database, as well as announcing a major alliance with analytical software vendor MicroStrategy. Microsoft embedded a large set of analysis functions within its SQL Server software. And database vendor Informix purchased Ardent, a provider of data transformation tools.

As it happens, Ardent’s assets included a pretty good campaign manager called Customer Advisor, and NCR also released a suite of campaign management tools. But except for Oracle, none of these vendors seems especially interested in attacking the front office market itself. Their addition of analysis and campaign management functions is probably more related to their ambitions in the data warehouse market.

Naturally, front-office vendors also have their eyes on the Internet. Most have enhanced their front office capabilities to support email and Web interactions, although the prices of Internet software companies are so high that this is usually done through development or alliances rather than acquisition. Of course, the Internet companies themselves don’t have this problem, since they can buy each other using their own inflated stock as currency. This has let Internet front office vendors like Kana be active purchasers of other Internet software companies. Still, most of these acquisitions have stayed within the front office.

One recent exception is the acquisition of DataSage, developer of well-regarded data analysis and personalization software. The buyer was Vignette, whose Web site management software runs the Internet equivalent of the front office. Vignette’s primary competitor, personalized Web site expert Broadvision, has taken the opposite approach of building alliances with many non-front-office vendors rather than acquiring a single product of its own.

Whether alliances or acquisitions will prove the more successful strategy is so far unknown. Either way, the development of integrated product suites represents a major change for the marketing software industry. Instead of evaluating stand-alone products primarily on their technical merits, buyers now must first choose whether they want to accept the cost, time and risk of integrating separate products, or avoid those by purchasing a suite. This makes it considerably harder for stand-alone products to survive, both because their market is smaller–comprising only firms willing to undertake the necessary integration–and because they must lower their price to compensate buyers for the integration costs they could have avoided by sticking within a suite.

The existence of suites also changes the evaluation process itself. The larger set of functions makes it more work to compare products based strictly on functionality, and means it is less likely that one product will be superior to the others in all areas. In addition, functionality becomes less important because buyers must pay more attention to the vendor’s long-term viability: with so many more eggs in the same basket, buyers are at higher risk if the vendor fails. Both factors make it harder for new firms to compete even if they are technically superior or priced below the more established products. Of course, the desire to shift the terms of competition in their favor is exactly why vendors seek to create integrated solutions to begin with.

For software buyers, the significance of all this is that they cannot ignore industry mergers and acquisitions even though they might like to. At the minimum, buyers must consider whether any suite really does a good enough job to meet their current and foreseeable future needs–recognizing that once committed to a suite, it will be much harder to substitute a superior stand-alone application for any particular function. Of course, the suites themselves will also improve over time, but the need to retain close integration with many different components makes the suite vendors move more slowly than independent competitors. Buyers should also closely examine the actual degree of integration among suite components, particularly if some parts were acquired: components developed separately do not suddenly become compatible simply because ownership changes hands.

Above all, suite buyers should assess–and insist on–a suite’s ability to integrate easily with third-party tools. The marketing software industry is still evolving too quickly to assume that any single vendor will remain at the forefront in all areas. This makes it almost inevitable that the buyer will want to substitute an external product for some suite component at some point in time. Moreover, today’s marketing systems are increasingly expected to interact with other systems, both within the company and at outside suppliers and customers. This means the ability of a suite to work with other products is essential. Happily today’s technologies make such integration possible with little cost in performance, so it is not an unreasonable request.

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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.