The purpose of these columns is to discuss software purchased by marketers. This means they exclude the call center, sales automation and customer support products generally referred to as customer relationship management (CRM) systems. Those are operational systems purchased and run by operational departments, not by marketers. This is true even though these systems are often sold on their ability to coordinate all contacts with each customer ­ a process with value only if the coordination is guided by marketing input. Otherwise, CRM systems simply let companies do the wrong things more efficiently.

Interaction management systems are important precisely because they provide the connection between marketers and CRM. Interaction management systems assess each customer contact and recommend the optimal response from a marketing (that is, corporate) standpoint. It is this marketing standpoint, which takes into account the long-term impact of each choice, that distinguishes interaction managers from recommendation engines such as collaborative filtering systems. This latter group makes immediate predictions, such as which product is the customer most likely to buy or which document is most likely to answer to his question. While important, these predictions do not address the strategic issue of whether making a recommendation is the right thing to do in the situation at hand. For example, in the classic book-buying scenario, the best choice in some cases may be not to recommend a related book, but to offer the customer a discount certificate or even just say thanks for past business. A recommendation engine could not make this sort of judgement; an interaction manager can.

To function effectively, today's interaction management systems must meet three challenges. The first is to integrate in real time with the touchpoint systems ­ call centers, automated teller machines, Web sites, etc., ­ that execute the customer interactions themselves. Real-time operation is unusual among marketing systems which are generally more analytical and batch oriented. Thus, the technology of interaction managers is significantly different from traditional marketing products.

The second requirement is to handle an astonishing volume of data ­ every click on a Web site or every transaction on a bank statement. This volume would be a challenge under any circumstances, but is especially daunting when the system must assimilate and react to the data in real time.

The third requirement is to simplify the overwhelming variety of real-world situations that can arise. Because so much detailed information is available, no two customers may have exactly the same history. An ability to group individuals and find common patterns of behavior is essential to limiting the number of cases to something manageable.

This last point deserves a bit more explanation. A fully automated system might, in theory, be able to handle the naked complexity of thousands or millions of unique customer histories by independently calculating an optimal approach to each. (Whether the results would justify such massive processing is a separate question.) But today's reality is that interaction management systems use rules that are conceived, created and maintained the old-fashioned way ­ by human beings. This means there is a natural, and relatively low, limit to the number of cases that can be treated separately. So the ability to define a reasonable number of significantly different situations is essential.

Several products to exist to perform these functions, including Black Pearl Knowledge Broker, Harte-Hanks Allink Agent, Right-Point Real Time Marketing Suite (now owned by E.piphany) and Verbind LifeTime. Each takes a slightly different approach to the three challenges, but the general process is roughly the same.

The system identifies situations that require intervention. Generally this is done by having the touchpoint system call the interaction manager in specified circumstances, such as a particular point in a Web page or telemarketing script. This usually involves modifying the touchpoint system to call the interaction manager via an application programming interface. It may also involve running a part of the interaction management application on the touchpoint server.

Alternately, the interaction manager may scan a stream of replicated transactions and intervene when a specified transaction or set of transactions appears. This gives more control to the interaction manager and its users (the marketers), since they can use the interaction manager's own interface to specify when it will intervene. This also lets the interaction manager employ specialized scanning and data storage techniques to handle the data volume. But even with the scanning approach, the interaction manager needs some integration with the touchpoint system to deliver whatever messages are finally selected.

The system gathers data necessary to make a decision. This involves information provided by the touchpoint system about the current interaction as well as historical information stored elsewhere. Some systems access external databases through standard SQL calls ­ an approach that is flexible but may cause performance problems. Other products generate their own profiles for faster access; these may be updated in batch, real time or both. Like scanning, internally generated profiles permit systems to deploy special techniques to update, store or access large data volumes efficiently.

The system chooses a response. This is where the interaction manager truly diverges from a recommendation engine. Most recommendation engines rely on some sort of automated predictive model or scoring algorithm to select the "best" answer, while interaction managers usually apply hand-built business rules expressed in some form of Boolean logic or SQL query. The rule-based approach allows much more flexibility, since the rules can embody whatever business strategies the marketers choose. Some systems allow the rules themselves to include scoring algorithms or even to call a recommendation engine as part of the decision-making process.

The rules are often arranged in hierarchies. This lets the user establish priorities and manage complexity, while it permits the system to minimize processing by testing only rules that are relevant to the situation at hand. Rule definitions also often include references to standard customer segments that are defined outside of the rule itself which further simplifies maintenance and enforces consistency.

A good interaction manager assigns explicit start and end dates to rules and lets users group related rules into strategies or campaigns. It also lets users set up random sampling to test alternative treatments. The system needs a graphical interface to help users visualize the rules they have created and security to let different users maintain rules for different functional areas or customer segments.

For systems that rely on external data rather than prebuilt profiles, it is important to ensure the data needed for all rules is gathered in advance ­ otherwise separate queries will be generated for each rule, slowing the process unacceptably.

The system generates the chosen response. At a minimum, the interaction manager must be aware of whatever content is available to the touchpoint system, so it can tell that system which item to deploy. At the other extreme, the interaction manager could generate and transmit the chosen message itself. However the responsibilities are divided, the two systems must coordinate closely to ensure that each is aware of changes made in the other. Since marketers control the interaction manager and operational managers control the touchpoint system, this is an organizational challenge as well as a technical challenge.

The system reports on results and adjusts as appropriate. Because most of today's interaction managers rely on manually created rules rather than automated scoring mechanisms, there is little opportunity for the system to be self-adjusting. This is another difference from recommendation engines, where self-adjustment is extremely important. But interaction managers can report how often each rule is executed, the results it achieved, trends in response and, perhaps, even the characteristics of people who reacted differently. A good system would provide alerts or exception reports to bring urgent matters to the user's attention quickly. Again, relating individual rules to larger categories such as customer segments, business objectives (retention vs. acquisition) or long-term strategies is essential to making sense of the mass of detail. The system must also keep a log of changes to rules over time, so historical reports can accurately determine which set of rules was active when particular results were achieved.

The details of each of these functions provide major points of differentiation among interaction management products. Systems vary significantly in the throughput they can handle, the sophistication of the situations they can distinguish and their degree of touchpoint integration. In the future, differences are also likely to arise in the degree of automation they apply to rule definition and evaluation. As ever, each user's specific situation and requirements will determine which is the most appropriate product to deploy.

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