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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.
A further definition of customer insight is offered by Paul Laughlin, after over a decade of creating and leading customer insight teams within the Insurance Industry: "A non-obvious understanding about your customers, which if acted upon, has the potential to change their behaviour for mutual benefit".
This latter definition is explained further on both Customer Insight Leader blog and in an article for Institute of Direct & Digital Marketing (IDM) Journal.
The author emphasises four components of this definition: First, such insight is “non-obvious”, so it does not normally come from just one source of information and often does not come from just analysis or just research; rather there is a need to converge evidence to glean insights. Second, true insights need to be “action-able”; hypotheses which stay theoretical and cannot be tested in practice are not insights. Third, customer insights should be powerful enough that when they are acted upon customers can be persuaded to "change their behaviour". Just benefitting from targeting based on analysing past behaviour and assuming people will be creatures of habit does not reveal any depth of understanding them, certainly not insight. Fourth, to be sustainable, the goal of such customer change must be for "mutual benefit". As  argues, a key law for marketing today is “earn and keep the trust of your customers”, which is achieved by acting in their best interests as well as the long term value for the organisation.
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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.
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.
The above components only cover the scope of customer analysis or marketing analysis. Best practice is now expanding to including customer data management, behavioural analysis, predictive analytics, consumer research and database marketing within the fuller definition of Customer Insight or Holistic Customer Insight. Such an expanded definition is further elaborated in a short post on Customer Insight Leader called "The Breadth of Customer Insight" and in a formal article for the IDM Journal.
- Tucciarone, Kristy (2007). "Vying for Attention: How Does Advertising Affect Search and College Choice?". College and University: 26–39.
- Peppers, D. (2008). "Rules to Break and Laws to Follow". New Jersey, John Wiley & Sons.