Marketing Science

Today, brand managers and marketers can use sophisticated analytic methods to help them better understand the customer, quantify various market effects, and prioritize and optimize their efforts. Our Marketing Analytics solution covers many applications of data analytics in this area.

Customer Segmentation

In order to effectively engage with customers or optimize marketing efforts, interactions need to be personalized. When individual level personalization is not feasible, companies need to divide their customers into groups based on similar behaviors and attitudes. Our market segmentation solutions strike the right balance of statistical fit, business relevance, and targeting ability.

Churn Modeling

Churn models  are used to identify customers with a high risk of dropping out. This information can be used to pre-emptively adopt individual-specific retention measures. There is no ‘one-model fits all’ solution to this problem, and the approach has to be tailored to the data at hand. We employ a variety of modelling frameworks to score individual customers with a likelihood of attrition - also called churn propensity.

Market Mix Modeling

Marketing resources are typically limited in every company, so the objective is to maximize ROI. Marketing professionals need to determine what mix of marketing efforts will yield the highest lift in sales. Our solutions incorporate models that help allocate spend in a scientific manner across online vs. offline ads, promotions, and event sponsorships.

Customer Database Scoring and Classification

Many companies might have an internal way of looking at customers – typically as segments – and have their marketing strategy designed around that. While the marketer is aware of the company’s target segments, they often do not know which segment each of their customers belongs to. Our classification models help tag customer databases with pre-defined segments/clusters/tags, and in real time.

Survey Data Analytics

We combine primary research (survey) data with secondary data (e.g. sales, financials, GRPs) and estimate models to guide decision making for marketers. Examples of analytics in this area include market segmentation, key driver analysis, discrete choice modeling, demand forecasting, pricing, and portfolio optimization among others.