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Real-Time Marketing:
Logical Next Steps – Planning Your Customer Interaction Architecture

online columnist Steve Schultz     Column published in DMReview.com
October 7, 2004
  By Steve Schultz and Roman Lenzen

Steve would like to thank Roman Lenzen, senior analytic consultant from Quaero Corp. for contributing this month's column.

Our last column focused on an overview of real-time marketing and an approach to gauge how much real-time customer interaction marketing investment is appropriate. Environments with a low instance of customer interactions may not need a comprehensive real-time or near real-time system as the lack of data volatility over a small period of time would not provide the added insight needed for a satisfactory ROI. A company needs to assess their environment and develop the test plan, supported by sufficient metrics, to determine the appropriate investment that will produce the desired ROI.

The Architecture

Given that suitable data capturing and warehousing systems are in place, the first step includes developing the data transformation and scoring process. Producing a data conversion and scoring engine, which will be used to apply the analytic insights, further reduces the time required to deliver the customer treatment. Analytic insights include business intelligence (BI) reports, predictive models and segmentation systems. Models are developed to predict future actions (potential to respond, cancel, become delinquent) as well as grouping individuals into similar segments.

In many instances, the complexity of the aggregations and transformations dictate the need to move this process off the data warehouse and into a standalone process. The results are then transferred back to the data warehouse in the form of scores, segments and derived fields.

The next step is to design and develop the decisioning and delivery mechanism. The final derived fields, scores and metrics are accessible from the data warehouse, but they must be analyzed to determine the most appropriate offer or treatment. Similar to batch campaign management systems, the decisioning system must be able to seamlessly access the customer's metrics, apply priorities and deliver the "best" anticipated action. An example of an action or treatment may be a dynamic offer based on a certain customer segment. Another application may include dynamically adjusting the billing method, interest rate or credit limit based on the credit worthiness or previous payment history of the customer in order to reduce the risk exposure.

All systems and their interfaces should be developed with the results of the most appropriate real-time environment in mind. Systems should only be as complex as needed to deliver the message in the time determined. Embarking on a more complex design than needed may not provide the ROI desired. Systems include:

  1. Data capturing system(s)
  2. A centralized data warehouse
  3. A data transformation and scoring process
  4. An interactive decisioning system
  5. Optional: In certain environments a separate data store is created which holds all the scores so that the data warehouse is not involved in a high transaction environment.

It is best to have the data transformation, scoring and decisioning processes removed from the data warehouse, but in certain environments this may be unavoidable. The additional costs to build systems may outweigh the power provided. All industries, companies and environments vary - one size does not fit all.

Storing the Data for Future Analysis

Although the customer and customer profitability are the focus of a real-time or near real-time interaction system, Figure1 shows that the data driven process is centered around the data warehouse. The data warehouse stores all historical interactions, treatments, scores, segments, decisions and resulting metrics. Unlike a production environment, a central data warehouse is crucial in capturing the process and customer actions.

Figure 1: Customer Interaction Architecture

Knowing a customer's scores, decision method, final treatment and the resulting action at any point in time will provide several advantages:

  1. Optimize the decisioning matrix or offer priority grid.
  2. Monitor model effectiveness.
  3. Develop additional sequential treatment insights (e.g., never offer product A based on criteria D).
  4. Continue to create scenarios and further analyze additional opportunities that may make moving closer to real time cost-effective.
  5. Use customer segmentation migration within the treatment decisions.

Marketing and Future Interaction Implications

Within the decisioning process and priority grid it is important to allow random control groups to flow through the system. Control groups satisfy two purposes in the continued test-and-learn customer valuation process.

  1. A mechanism to compare, verify and optimize previous and ongoing analytical efforts.
  2. A subset of the customer base to be used for subsequent analytic processes. The adjustments placed on the customer flow based on the results of previous analytical efforts will affect the behavior of those customers.

A well-planned, highly tuned customer interaction system will only deliver a satisfactory ROI when customer profitability or projected customer profitability is an included dimension. Retaining an ongoing unprofitable customer could be more costly then retaining a profitable customer; however, many unprofitable customers can become long term, loyal customers. The opportunity that comes with further understanding the characteristics and drivers that cause an unprofitable customer to become profitable is well worth the effort.

The customer interaction architecture, no matter how complex or simple, should be developed based on one goal - maximizing the difference between increased customer profitability and additional infrastructure and maintenance costs. The final tools and technology will vary, but the processes will remain the same.

  1. Capture the customer interaction.
  2. Store the request.
  3. Determine the optimal treatment based on all historical customer actions and marketing interventions.
  4. Deliver the treatment.
  5. Capture the response.

The only prerequisite is that the full process is stored in a central location to be used for further interaction refinement.

Real time is not the goal. Real time is only a term - an end state that for most may not be required. A company's appropriate customer interaction time (ACIT) is defined by the amount of time required to deliver a message or treatment that will maximize profitability given the additional infrastructure costs. Always determine the ACIT then develop the architecture around the timing requirements.


For more information on related topics visit the following related portals...
Database Marketing, Enterprise Achitecture and Real-Time Enterprise.

Steve Schultz is a leading customer relationship management (CRM) practitioner who combines an understanding of information technology with extensive business process design experience and information-based decision-making methodologies. As executive VP of Client Services for Quaero (www.quaero.com), he helps clients identify, justify, implement and leverage leading edge analytical CRM environments to create or/and improve their database marketing capabilities. Schultz has worked with companies in the financial services, telecommunications, retail, publishing and hospitality industries. Contact him at schultzs@quaero.com.

Roman Lenzen is analytical director at Quaero, a marketing consultancy focused on helping companies accelerate marketing performance. With more than ten years of analytical CRM and risk experience, Lenzen has delivered value-added analytical processes in several industries. His significant analytical, technical and business process experience provides a unique perspective on improving process efficiency and customer profitability. He can be reached at LenzenR@Quaero.com. For more information on Quaero please visit www.Quaero.com.

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