David Raab Article Archive

The vision of enterprise-wide, real-time, tightly-integrated customer relationship management is enticing, but maddeningly difficult to cost-justify. This means that marketers must often settle for less comprehensive approaches with a more certain payback. One of the best documented applications is using statistical models to predict customer purchases, payment and attrition. To be useful, these predictions must only be better than predictions from alternate methods such as static business rules or sales agents’ intuition. Statistical methods can beat such competition pretty consistently.

This may be why of the ten or so systems designed for cross-channel interaction management, the five with integrated modeling account for over 80% of the total installations. In other words, systems designed as the hub of a corporate customer management architecture are often deployed in the much humbler role of a score delivery mechanism. Still more deflating to visions of enterprise-wide grandeur, they may serve only a single customer touchpoint such as a call center or Web site.

Norkom Alchemist (Norkom Technologies, 617-303-1900, www.norkom.com) offers both predictive modeling and broader customer management. The modeling functions are comprehensive, including tools to import and transform data, build and evaluate models, and generate scores in real time for individuals or in batch for groups. Users set up each project on a flow chart, defining steps from the initial data import through the final model production.

The system automates these steps as best it can. But most marketers will rely on technicians to set up connect with external data sources, which can be flat files or relational databases read via JDBC. Once imported, the data is stored in a repository and reused as necessary. Similarly, most marketers would probably want a statistician to help with such choices such as how to treat missing or unusual values, which elements to use in an analysis, and what derived variables to construct. Still, once a project is set up, a non-statistician could execute it and use Alchemist’s visualization and tabular reports to review the results.

These reports provide several useful measures, including model reliability and the importance of each data element in the model. Providing such measures a particular advantage of the regression technique, called Vapnik algorithms, used in Alchemist. The algorithms also can calculate the importance of each data element in a single customer score. This can be interpreted as showing “why” a customer falls into a specific category and used to select appropriate marketing treatments.

The current version of Alchemist can also build models using c4.5 decision trees, Bayesian networks, and neural networks.

Alchemist lets users assign values to the “most likely” and “least likely” outcomes, to use in financial analysis. Most systems use a slightly different approach, based on cost per offer and value per response. But while the Alchemist approach takes some getting used to, both methods can give the same result.

Once a model is built, it can be used by the customer management portion of the system. This relies on “agents”, which are processes that can gather, manipulate and output data. Connecting agents with external databases, touchpoints, and content management systems requires custom integration by technical staff. Again, marketers can take over once the setup is complete.

Agents are built as flow charts with steps for the different tasks. Input tasks can query a database, scan email, or search the Web. This can happen continuously or at set intervals. Once a process begins, other tasks can apply a scoring model, run a stored or external procedure, branch based on logical conditions, bring several branches together, or call another agent. Random splits to support champion-challenger testing are planned for future release. Users can insert “wait” tasks to execute a series of steps over time. The system can also send a request for approval before continuing with a process, and automatically proceed once the approval is received. Agents can send personalized messages via email or SMS (wireless), or accumulate records in a file for later batch transfer. The system can measure the acceptance rate of an agent’s offers and automatically notify management if performance is above or below a specified range. Agents can terminate on a fixed date or after they execute a specified number of times.

In short, Alchemist agents can do much more than deliver model scores to customer touchpoints. One airline uses it to automatically notify passengers when their flight is delayed. But Alchemist is not, and does not claim to be, a true real-time interaction manager. One limit is that the system communicates with touchpoints indirectly, by scanning messages or database entries, rather than through an Application Program Interface (API). This slows response time and limits process integration. Nor do agents automatically track the status of customers through their processes. Instead, they read customer history from external data sources or, if a custom feed is built, from the Alchemist repository. It is also up to users to coordinate across agents by setting priorities or checking for conflicting actions related to the same customer. Nor is the system real-time in its model development: models are built and updated in periodic batch processes, although scoring does occur in real time.

Alchemist provides integrated visualization tools and Business Objects reporting software. A log report shows which agents are active and when they have executed, but users would have to put a counter inside the agent to determine how many customers have been affected. Notifications and reports are delivered through a Web portal developed by Norkom. All components of the system run on Unix or NT servers and are accessed via Web browser.

Alchemist was originally released in 1999 and has about 40 installations. The software costs $375,000 plus $50,000 to $200,000 for implementation. Norkom is based in Ireland and has recently established a U.S. presence. To speed deployment, the company offers prebuilt Alchemist applications for specific purposes such as retail banking attrition. These include data models, analytic algorithms, automated modeling and notification schemes, interfaces with touchpoint systems, and related implementation and training. They cost $75,000 to $100,000.

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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.