Analytic Techniques - Data Square
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Analytic Techniques

Combining statistical techniques with Artificial Intelligence methods for maximum impact.
Over two decades of hands-on experience, Data Square has finessed strategies to identify analytic techniques that will deliver the highest ROI for specific business applications. Frequently used methods are shown below.

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 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

Data Square's analytic offerings include consulting, modeling services, analytic data mart development, CRM platform, technology, automation, and infrastructure set-up.
Find out how Data Square can improve your business outcomes using next generation AI and analytic insights.