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
CHAID
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 behavior.
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
Hypothesize models of market behavior, 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