Advanced Analytics White Papers

• Bullet Holes in Bombers: Operations Research and Management Science Applied to Marketing by Jerry W. Thomas
  Most analysts define operations research and management science to mean the application of the scientific method and advanced analytics to the solution of business problems. OR/MS almost always involves building a mathematical model of some business process or system. There is an objective function; that is, a mathematical definition of the object or thing to be optimized (to maximize profits or sales revenue or minimize costs, typically).

• Business Segmentation: Emerging Approaches to More Meaningful Clusters by Michael Richarme
  Conducting opinion research among businesses is problematic. This is particularly evident at the simplest level of analysis, customer segmentation. However, segmentation techniques are evolving and techniques that were common practice in the recent past are rapidly being supplanted by newer, more meaningful segmentation techniques.

• Choice Model Calibration by John Colias
  A look at how the calibration of survey-based choice models can make a substantial difference in predicted demand and revenue resulting from price changes. Calibration of brand part-worth utilities based on in-market data such as that derived from store scanner data can deliver more accurate measurement of price elasticity and better market predictions of demand response due to price changes.

• Choice Modeling Analytics—Benefits of New Methods by John Colias
  An overview of the benefits of several technical advances in choice analysis, including experimental design algorithms, segment- or customer-level models, and model calibration. The recent advances discussed in this paper have the potential to reduce survey length for choice modeling research and deliver more accurate market simulators to measure bottom-line revenue impacts.

• Choice Modeling for New Product Sales Forecasting by Jerry W. Thomas
  Choice modeling makes it possible to simulate the shopping and decision-making process, with all of the important variables carefully controlled by rigorous experimental design, so that the new product's sales revenue can be accurately predicted. Equally important, choice modeling helps marketers understand the many variables that underlie that forecast.

• Comparison of Segmentation Approaches by Beth Horn and Wei Huang
  Segmentation approaches can range from throwing darts at the data, to human judgment, to advanced cluster modeling. We will explore four such methods: factor segmentation, k-means clustering, TwoStep cluster analysis, and latent class cluster analysis.

• Consumer Decision-Making Models, Strategies, and Theories, Oh My! by Michael Richarme
  The focus of this paper is to examine the major decision-making models, strategies, and theories that underlie the decision processes used by consumers, and to provide some clarity for marketing executives attempting to find the right mix of variables for their products and services.

• Eleven Multivariate Analysis Techniques: Key Tools in Your Marketing Research Survival Kit by Michael Richarme
  An executive understanding of eleven multivariate analysis techniques, resulting in an understanding of the appropriate uses for each of the techniques. This is not a discussion of the underlying statistics of each technique; it is a field guide to understanding the types of research questions that can be formulated and the capabilities and limitations of each technique in answering those questions.

• Global Segmentation—Dealing with Cross-Cultural Differences in Survey Rating Scale Usage by John Colias
  Developing segmentation solutions that are global in scope requires dealing with cross-cultural differences in scale usage. Given cross-cultural differences in scale usage, marketing research analysts frequently develop ways to adjust survey responses, so that a particular survey response value means the same thing regardless of country of origin.

• Improving Customer Satisfaction and Loyalty with Time-Series Cross-Sectional Models by John Colias, Beth Horn and Ellen Wilkshire
  Customer satisfaction and loyalty surveys typically track brand perceptions both overall and with respect to specific performance areas. For example, a survey might ask customers to rate brands based on overall satisfaction, likelihood to purchase again, likelihood to recommend, customer service, product performance, and brand image. Time-series cross-sectional (TSCS) modeling incorporates both across-units and across-time variation in data variables. The results from this application illustrate the value of adding the time-series component to the analysis.

• Market Segmentation by Jerry W. Thomas
  When the term "market segmentation" is used, most of us immediately think of psycho-graphics, lifestyles, values, behaviors, and multivariate cluster analysis routines. Market segmentation is a much broader concept, however, and pervades the practice of business throughout the world.

• Marketing Mix Modeling by Jerry W. Thomas
  A look at how marketing mix modeling can assist in making specific marketing decisions and tradeoffs, and also create a broad platform of knowledge to guide strategic planning.

• Modeling Customer Service Segments in the Utilities Industry by Joel Mincey
  The utilities industry has seen a great deal of consolidation, restructuring, and deregulation of late. Any one of these events has the possibility of negatively affecting the level and quality of service. As this paper shows, it is also critical to understand the different customer segments and the level of attention required to maintain satisfaction.

• Multidimensional Segmentation By Felicia Rogers, Diane Brewton, and Elizabeth Horn, Ph.D.
  Regardless of the length and complexity of a survey, the overarching task is to glean actionable business recommendations from the research you implement. This paper presents a case study to demonstrate how you can steer through what may seem like too much data, using a technique we call multidimensional segmentation (the intersecting of multiple segmentation solutions driven by different consumer characteristics and attitudes).

• New Product Sales Forecasting by Jerry W. Thomas
  The development and introduction of a new product is an inherently risky venture. In an effort to reduce the risks associated with new products, the forecasting of year-one sales has become an established practice within the marketing research industry. The goal of this article is to take a bit of the mystery out of the methods used to derive year-one sales forecasts for new consumer packaged goods.

• New Statistical Tools for Key Driver Analysis by John Colias
  Key driver analysis is used by businesses to understand which brand, product, or service components or attributes have the greatest influence on the customer’s purchase decision or a physician’s prescribing decision. The focus of this paper is to discuss the potential application of a relatively new tool, Ensemble Prediction, which combines thousands of regression models to produce a prediction of the overall market performance based on attributes that influence the purchase decision or physician’s prescribing decision.

• Optimizing Messaging & Positioning with Choice Modeling by John V. Colias and Wei Huang
  Messaging and positioning choice modeling is recommended when the primary research objective is to obtain information that would allow a company to develop the most effective communications message to consumers, maximizing attraction to its specific brand, product line, store, or department within the store.

• Perceptual Mapping: What do Restaurant Brands Really Mean? by Michael Richarme and John Colias
  A look at using advanced analytics, including perceptual maps, in determining the brand positioning in the minds of consumers. The article includes a perceptual map of national restaurant chains. This data is from the Health and Nutrition Strategist™ syndicated study.