Marketing. analytics focuses on making predictions regarding the likely consequences of particular marketing mix actions contemplated by the firm; and proposing actions that improve performance on critical attributes. It is particularly useful for identifying profitable customer segments, optimizing customer acquisition and retention, managing brands, determining pricing strategies and designing promotions, providing dynamic forecasts, assessing market values of patents and intangibles, and assessing relative contributions of different organizational units or business partners.     


Customer analytics uses data from observed, collected or inferred customer actions or preferences to inform managerial decisions with a view to improving inter alia communication, sales promotion, direct marketing, site selection, and customer relationship management.

We typically use clustering or mixture models as a first step toward effective customer analytics; this process combines data mining and data analytics methodologies and uses scanner data, clickstream data, SEM data, procurement data, survey data, financial data, and point of sale data, among others. Customer analytics can identify key customer dimensions linked to profitability that can be used to forecast business-critical customer-buying habits. Retail businesses including online marketers, airlines, financial services providers, and the hospitality sector routinely use these methods to achieve profitability.

marketing analytics

marketing analytics

marketing analytics

marketing and customer analytics

our services

We specialize in statistical and regression analysis, including factor analysis, clustering, multivariate methods, simultaneous equation models, choice models, mixture models, duration models, Bayesian methods, and non-parametric regressions. We use these techniques for forecasting, marketing and sales effectiveness modeling, simulation studies, counterfactual analyses, structural model predictions, association rules, basket composition analysis, churn analysis, customer retention analysis, and scanner data analysis.

We work with major statistical and econometric software packages, including SAS, STATA, SPSS, Gauss, NLOGIT (LIMDEP), R, Python, Winbugs, and Minitab. We assist with data collection, cleaning, organization, restructuring, and interpretation. We have expertise in designing incentivized and non-incentivized experiments, surveys, and conjoint studies to uncover motivations, preferences, and monetary value associated with product attributes and possibilities.