Customer analytics aims to understand customer behaviour. Understanding your customers, you can adjust the customer relationship management strategy to improve customer satisfaction and profitability.

A few examples:

• How much is a 38-year old female professional from the UK that stays at your hotel twice a year and prefers a deluxe room, worth to your business?
• Will marshmallows sell better being stacked next to hot chocolate or grilling accessories?
• Will you just waste money sending a brochure on investment products, to a 42-year old credit customer who has already refused similar offers twice in the last 6 months? Or will a follow-up phone call from an investment advisor seal the deal?

Data mining and machine learning techniques have been proven to deliver efficient solutions to the hundreds of questions arising regularly in customer relationship management. Customer analytics can develop models highlighting cross-, up-sell or conversion opportunities, customer risk, value or attrition signs, to name but a few. Analytical capability has become the source of competitive advantage in most industries and its significance is still to grow.

Analytical models support marketing departments, call centres, customer service and sales representatives. More and more companies develop comprehensive customer value management based on the insights from analytics. Intelligently combining all the relevant information, a customer value management system will guide the front-office agent to the best business decisions.