How can businesses boost their turnover with their existing customer base? The “share of wallet” concept is a good point of reference, defining itself as a company’s own share of a specific customer’s turnover in relation to competitors’ shares, whereby this indicator can be predicted with the Target Group Predict algorithm.
One of the main problems in managing sales and marketing is the fact that businesses don’t understand their real customer potential. Managers throw themselves at their best customers, not realising that their “poorer” customers harbour enormous revenue potentials that are just waiting to be exploited. The core question is: Which of the class C customers represent the highest potential and should therefore receive the strongest sales attention?
Share of wallet offers a means of finding this out, because it defines a company’s own turnover in relation to competitors’ turnovers with the same specific company. A company clearly knows its own turnover, but the difficulty lies in relating it to the theoretically achievable turnover, because this requires adding one’s own value to those of the competition – and the latter is something we don’t know.
How can we predict competitors’ turnover and therefore our individual share of wallet? In many sales organisations, the salespeople are required to document individual customers’ competitive positions. For example in the area of car sales, where salesmen enter car pool data in the customer data sheet, or B2B sales managers who are required to record the number of mobile phone contracts held with competitors. In many sectors, customer surveys are the order of the day to determine the share of wallet.
All these approaches go in the right direction, but unfortunately don’t normally present an accurate picture. Potentials data for use by sales organisations will not be reliable if a salesperson sees no point in gathering it, or if his relationship with the class C customer is so distant that the data cannot be determined. There is also a degree of unwillingness among salespeople, who are quite often measured by their realisation of potential; as this influences their salaries, they have little interest in raising the bar.
The Target Group Predict algorithm developed by us establishes the information on potentials by statistical means. This method, based on lookalike modelling, examines which of a customer’s “external attributes” are deciding factors for a product. To achieve this, masses of data are available from company registers, reference databases and from a website analysis database specially created for this purpose. Target Group Predict, a Predictive Analytics Solution, indicates the probability of any given customer being a high-potential (class A) customer, and states the reasons.
In this process, signal words such as ‘automation technology’ connected with electronics, ‘nuclear’ with tubular steel or ‘ISO9001’ with automotive can be identified by the evaluation models as being relevant indicators for class A customers. This enables the sales organisation to concentrate on promoting class C customers, i.e. those with potential, whereby the system not only provides them with the revised customer list, but also the signal words that have led to each customer’s upgrading. Sales resources are thus targeted by predictive analytics to raising “share of wallet” and therefore to effectively increasing market share with large potential customers.