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 Analytic Techniques 

Bayesian Methods

  • Predict the brand a household will choose for a given purchase occasion

Bayesian Networks

  • Build integrated models of consumer behavior that can be estimated with limited amounts of data using monte carlo simulations


  • Identify and differentiate characteristics of best customers from others in the database

Cluster Analysis

  • Segment  customers into discrete groups based on multiple dimensions
  • Group products into bundles based on similarity
  • Segment markets and determine target markets
  • Develop product positioning and launch new products
  • Select test markets

Collaborative Filtering

  • Predict items (movies, music, books, news, Web pages) that a user may be interested in, given some information about the user's profile

Conjoint Analysis

  • Determine the combination of attributes that would be most satisfying to consumers

Discriminant Analysis

  • Predict propensity to respond vs. buy based on prior purchase and promotion history

Factor Analysis

  • Obtain underlying dimensions from responses to product attributes identified by the researcher

Fuzzy Logic

  • Determine baseline sales to enable a more accurate measurement of promotion and advertising effectiveness

Genetic Algorithms

  • Optimize the placement and numbers of callouts within a web page layout to grow, on an ongoing basis, a page's marketing gains

Linear Regression

  • Predict the dollar value of purchases associated with a mailing

Logistic Regression: Binary

  • Predict customers that are most likely to respond to a mailing

Logistic Regression: Multinomial

  • Predict customers that are most likely to purchase different products in a catalog mailing

Logit Analysis

  • Assess the scope of customer acceptance of a product, particularly a new product. Determine the intensity or magnitude of customers' purchase intentions and translate them into a measure of actual buying behaviour.

Markov Chains

  • Forecast store choice

Multidimensional Scaling               <<

  • Obtain underlying dimensions from respondents' judgements about the similarity of products

Neural Networks

  • Predict customer demand and segment customers into well-defined categories

Perceptual Mapping

  • Display the perceptions of customers or potential customers on attributes such as position of a product, product line, brand, relative to competitors

Preference Regression

  • Determine consumers' preferred core benefits. Supplement product positioning techniques like multi dimensional scaling or factor analysis to create ideal vectors on perceptual maps.

Structural Equation Modeling

  • Hypothesise models of market behaviour, and test or confirm these models

Survey Design and Analysis

  • Collect information on product attributes and/or spending potential on a sample of the customer base

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