Predictive Analytics

Actionable insights powered by machine learning.

Why do I need this?

You need to more accurately assess future outcomes

Whether it be forecasting future sales, the potential likelihood of customer churn or what offer will optimize profitability, predictive analytics provides data-driven expectations of future performance. These insights enable more timely, intelligent and informed decisioning to drive topline and bottom-line growth.

How does it work?

Machine Learning facilitates the process

Predictive modeling with machine learning algorithms coupled with business and marketing knowledge take big data and turn it into useful, meaningful and actionable insights. Models are continuously trained and validated to inform right time decisioning.

What makes it effective?

Analysis tailored to data and business objectives

We know no two brands have the same data or business challenges and therefore each predictive model incorporates your specific inputs and addresses your unique challenges.

Example use cases are within customer analytics:

Predict profiles of new customers based on first purchase/touchpoints and enable tailored marketing and experiences for those at first entry to the franchise.
Predict early indicators of churn to ensure the right messages reach the right customer at the right time.

Examples to increase penetration:

Suggestive sell techniques – recommend products, menu items and/or content that will resonate most with each customer to increase basket size, conversion rates, profitability, and brand affinity.
Offer optimization – personalizing the right offer at the right time to the right customer or persona.

Examples to forecast sales/demand:

Incorporating sales, marketing efforts as well as external drivers such as weather to forecast demand; enabling more effective marketing, supply chain efficiency and accuracy of sales targets
Predicting revenue from new stores, renovations, other changes to identify potential opportunities and risks