Monday, April 21, 2008

MPM System Checklist

I’m planning a series of posts to review individual MPM reporting systems. Before doing that, it will help to set a framework of items to consider. I may expand this over time.


  • treatments given to individual customers across all channels (this refers primarily to marketing messages, but could also include operational messages such as bills and service interactions)

  • customer behavior history (purchases, refunds, service requests, etc.; this includes costs and revenues)

  • customer attributes (demographics, location, needs, etc.; these should be standardized and grouped into audience segments to make analysis easier)

  • treatment attributes (campaigns, products offered, pricing, positioning, media cost, etc. These should also be standardized and aggregated for analysis.)

  • external conditions (competitive advertising and promotions, economic conditions, weather, etc.)


  • customer data integration (to associate all information related to individual customers. This is the clichéd 360 degree view.)

  • response attribution (to measure the influence of each treatment on subsequent customer behavior. This a complex analytical process if done in depth; otherwise, it can be as simple – and simplistic – as directly matching responses to promotions.)

  • brand value attribution (to measure the influence of each treatment on brand value. This is an unholy marriage of two frighteningly obscure methodologies: response attribution with brand value measurement.)

  • predictive modeling and optimization (to recommend treatments during interactions and for outbound campaigns. This would also recommend optimized spending levels across channels based on business objectives and constraints.)

  • lifetime value calculations. (These include projections for audience segments and estimated incremental lifetime value impact of specific treatments.)


  • campaign reports (Basic information includes quantities, costs, and responses. Financial information includes return on investment, cash flow and profits by period. The financial results are based on response attribution. Depending on how attribution is handled, reports may show only direct effects or show direct effects plus long-term impacts.)

  • treatment reports (Basic information shows how often different treatments are delivered. Advanced information shows the impact of treatments and treatment attributes on campaign response, customer value and brand value. Like campaign reports, treatment reports are based largely on response attribution.)

  • customer reports (Basic information includes segment counts, demographic profiles and behavior profiles. Trends show changes in segments and migration of customers among segments. Customer reports also includes levels and changes in lifetime value.)

  • Brand value reports (These show current brand value and changes in brand value. They report both aggregate values and details for brand value components.)

Key considerations within each of these topics include:

  • the scope of data included (for example, which channels)

  • degree of detail (or “granularity” if you want to impress someone. For example is customer information reported for individuals, audience segments or campaigns? What is the level of financial detail?)

  • data latency (how long does it take before new data becomes available to the system?)

  • implementation effort (time, skill levels, flexibility)

In addition, there are the standard considerations for any system:

  • delivery model (hosted vs. on-premise)

  • technical skills required to deploy and maintain

  • end-user skills required to benefit from the system

  • costs (hardware, software and services; implementation vs. on-going operation)

  • scalability (relative to your own data volumes and user counts)

  • vendor experience and stability

  • technologies used (databases, .NET vs. J2EE, ‘fat client’ vs. browser-based, etc.)

A word of caution: any list like this favors products with many features, even though some of those features may not be poorly implemented. I consider MPM systems to be in an early stage of development, which means there will be a variety of specialist products (a.k.a. “point solutions” or “best of breed”) that do just a few things but do them very well. This will change over time as requirements for different functions become better understood and standard practices emerge. For now, it’s probably more important to make sure any give product meets your critical needs than to try to find a one-size-fits-all solution that does everything. It’s a safe bet that the major platform vendors (of enterprise systems like SAP or Oracle, or marketing systems like Unica, Aprimo, SAS or Teradata) will eventually provide comprehensive solutions, so anything you buy now will only last a few years at best.

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