Black Pearl Reduces RulesThe bedrock technique of software evaluation is comparing products with similar functions. But today's marketing systems so often provide different combinations of functions that this method is increasingly ineffective. Even specialized products often straddle functional boundaries in ways that make classification difficult, if not irrelevant.
Black Pearl Knowledge Broker (Black Pearl Inc., 415/357-8300, www.blackpearl.com) is one such product that does not fit standard categories. Its basic function is to make recommendations, but it recommends complicated things such as stock portfolio allocations, not the book or record selections offered by a typical "recommendation engine."
More likely, Knowledge Broker is an "interaction manager" - a system that coordinates decisions across touch points to implement customer management strategies. With sophisticated technologies for rule management and data access, Knowledge Broker provides two of the three key interaction management functions. But its approach to the third function, touch point integration, is limited.
What's important about Knowledge Broker is it mounts a plausible assault on one of the central challenges facing interaction management: managing the number of rules needed to execute a sophisticated customer relationship strategy. This assault operates on two levels, technical and administrative. For administrative, where most marketers operate, Knowledge Broker conquers rule complexity by dividing the problem into layers.
The lowest layer handles connections with data sources; the next layer combines data to form business concepts; the third layer specifies how one business concept can change its meaning in the context of another; the fourth layer holds rules for how to respond in a given situation.
The first layer might create an entity of "stock price" by reading data from a legacy system; the second layer could define a concept of "volatile stock" that involves several data entities; the third layer might give different definitions of volatile stock for customers with different levels of risk tolerance; and the fourth layer might have a rule that "if customer is high risk, then recommend buy volatile stock."
Each level builds on the previous level and each level requires successively less technical skill to set up. The business rules in the fourth level can be created by marketers with no technical background - though they may make costly errors if they don't understand how the earlier layers were constructed. In fact, business users must be involved in setting up every level to ensure the entities reflect actual business knowledge and objectives. Still, most of the work on the lower levels is clearly technical. Entities on all levels involve a mix of data and rules with the proportion of rules increasing at higher levels.
While the multilayer structure and graduated division of labor help make Knowledge Broker more workable, the main value of its approach lies in the third layer. Here, "contexts" can change the meaning of a term depending on current conditions, allowing a single rule to cover many situations.
According to Black Pearl, this lets Knowledge Broker use one-quarter to one-tenth as many rules as a conventional rule-based system. Since a comprehensive customer management process can involve literally thousands of business rules, reducing their number and making them easy to manage remove critical stumbling blocks.
Knowledge Broker also provides offline tools for data mining and predictive modeling. These can build multilayer perception models and decision trees whose outputs, which could be scores or predictions, can be used within rules. Values are calculated in real time for individual customers as needed. Modeling also helps reduce rule complexity, since a concept such as "best offer" can be based on a model score rather than on elaborate if/then logic.
On the technical level, Knowledge Broker employs sophisticated technologies that make heavy use of Java, XML and distributed processing. It can connect to virtually any data source in real time, looking up data about an individual customer and translating it to an XML format as a transaction proceeds. It also can write back a history of offers made to each customer and how each customer responded. This can be stored in XML, a relational database or whatever format the user prefers.
The recommendations are transmitted back to the touch point system as an XML string. This also can be converted to another format such as HTML or WML (wireless markup language) if needed. The string-based approach lets Knowledge Broker pass specific information without requiring extensive customization to integrate with each new touch point. These features are particularly appropriate for recommendation engines. But this method does not provide Knowledge Broker with internal information about the touch point, such as what specific Web pages or telemarketing scripts are available. Some interaction managers allow for more intimate touch point integration.
Knowledge Broker also resembles its recommendation engine cousins in treating each decision as more or less separate. There is no flowchart to lay out a sequence of marketing contacts - a common feature among interaction managers. While a clever user probably could create a sequence using rules and contexts, it would be a challenge. But the focus on individual decisions has advantages. It has led the vendor to include start and stop dates for each rule, automated gathering of information on rule performance, support for random testing of different rules against each other and simulation of how proposed rules interact.
Knowledge Broker was released in February. The vendor is focusing on complicated recommendations for financial services and telecommunications. The system is designed to scale by spreading across multiple central processing units, both within a single computer and across clusters of computers. Pricing is set at $100,000 per CPU with discounts as numbers rise. An average installation is expected to cost $500,000 to $700,000 for a one-time license, plus annual maintenance.
David M. Raab is a partner at Raab Associates, Chappaqua, NY, a consultancy specializing in marketing technology evaluation.