Data Science

Turn your data into powerful one-to-one marketing that drives results

Data science mixes computer science and statistics with business knowledge to gain insights and business advantages. By analyzing your customer or purchase data, our team is able to give you insights into strategies that will allow you to grow sales, increase retention percentage, reactive lapsed customers, and compete more effectively.

How does it differ from typical marketing analytics that some companies have employed?

Data analysis is said to fall into three types: Descriptive, Predictive and Prescriptive.

  • Descriptive analysis tells us about what has happened. “Last year we did $500 million in sales.”
  • Predictive analytics allow us to make a prediction about what’s going to happen in the future. “Next year we predict that we will do $600 million in sales.” This isn’t just a prediction out of the thin air. Mathematical models are used along with numerous insights and lots of data to make the prediction.
  • Prescriptive analytics allow us to get recommendations about what we should do in order to meet a goal. “If we increase our marketing in category A, sales will increase by X%.” Sometimes prescriptive analytics take the form of recommendations. Other times they might directly control certain systems, for example SEM bidding.
We help our clients understand what is possible when applying predictive and prescriptive analytics to your data. We analyze your data to your goals and work with you to put together a plan to reach your goals. Whether it’s achieving higher year-over-year same store sales, increasing profit margins, maximizing consumer market share, or improving efficiencies, we work with our clients to make their goals a reality.

How can we help?

We use the treasure trove of data that retailers have in their Point of Sale systems, along with their product and promotional information to create insights that can help retailers with:

  • Pricing
  • Promotions
  • Discounting
  • Customer Segmentation
  • Marketing Optimization
  • Demand Forecasting
  • Marcomm Effectiveness
  • Price Sensitivity
  • Seasonality Effects
  • Customer Lifetime value
  • Consumer Targeting
  • Customer Segmentation
  • New Customer Acquisitions
  • Existing Customer Retention