Customer insight

Customer Insight is the intersection between the interests of the consumer and features of the brand. Its main purpose is to understand why the consumer cares for the brand as well as their underlying mindsets, moods, motivation, desires, aspirations, and motivates that trigger their attitude and actions.^[1]

Another definition of consumer insight is the collection, deployment and interpretation of information that allows a business to acquire, develop and retain their customers.

Analysis

Firstly, the collected data must be audited to fully understand the quality and opportunity within the database. Once this is done, there are a number of different types of analysis that can be applied.

Impact Assessment will help a business to understand how actions taken by the business affected their customer behavior, and also allow for some predictions of customer reaction to proposed changes.

Customers as Assets measures the lifetime value of the customer base and allows businesses to measure several factors such as the cost of acquisition and the rate of churn.

Propensity Modelling predicts the future behaviour of customers based on previous actions and helps businesses understand how likely it is that a customer will behave in a given way.

Cross-Sell Analysis identifies product and service relationships to better understand which are the most popular product combinations. Any identified relationships can then be used to cross-sell and up-sell in the future.

Critical Lag allows a business to deliver specific customer communications based on the individuals purchase patterns, helping to increase loyalty and improve customer retention.

Customer Insight in Practice

Customer Insight provided the basis for success for the Marks and Spencer (M&S) lunchtogo service. Using the Critical Lag strategy (outlined above), the company analyzed each customers' ordering behavior, mainly focusing on the frequency and time lapse between each lunchtogo purchase. Through statistical reasoning, EWA (the company hired by M&S to handle their Customer Insight) developed a critical lag formula, which helped M&S to judge when a customer’s current lag since they placed their last order, fell outside of their statistically expected behavior and heightened their risk of lapsing.

If a customer fell outside of this lag, M&S communicated with them via email or over the phone to find out the reason for this behaviour and therefore reducing the number of lapsed customers.^[2]

References