Not every customer is a good customer. Businesses that focus on blanket customer acquisition will fail to realise the higher profit margins that could be achieved through a more deliberate and focused customer engagement management strategy – a strategy that is focused on engaging great customers.
Some customers deliver high value to a business and are likely to remain loyal. The higher the proportion of these customers a business has, the more profitable it will be. Conversely, some customers can be so high-maintenance and low return that they actually cost money. Identifying and focusing efforts on great customers will help improve the bottom line.
The relative value of customers is a basic business reality. Businesses must therefore determine how to identify which customers to develop and which ones to spend less time on or abandon altogether. Once the profile of a great customer has been defined, businesses can go about developing and executing a strategy aimed at attracting and engaging these sorts of customers.
It is important to note that using blanket customer retention or acquisition strategies can have a negative result, despite being based on a seemingly-sound strategy. For example, an acquisition campaign could result in a higher number of new customers, but those customers are of little value if they are neither brand-loyal nor high-value.
The key to avoiding this type of result is to understand potential customers better. There are three levels when it comes to customer segmentation modelling: geo-demographic modelling; customer data propensity modelling; and uplift modelling.
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Geo-demographic modelling involves ‘geocoding’ the physical addresses of potential customers on a map and overlaying that information against demographic data and life-stage models for that geographic area. This will yield information for any address the like average income and expenditure, average number of children, spending preferences, risk profiles and more. Based on the product a business is selling, strategy can be quickly focused on geographic regions that have the clusters of geo-demographic profiles that match great customers.
Customer data propensity modelling
The next level of sophistication is customer data propensity modelling. It works by gathering as much customer data as possible, then building propensity models that identify the common characteristics that predict a customer will be high-value. This information can then be used to engage other potential customers with similar characteristics. This can be extended with Big Data, linking vast amounts of behavioural and psychographic data to build out the customer profile.
Uplift modelling is the most sophisticated form of customer segmentation and the one likely to deliver the best results when it can be used. Uplift modelling categorises customers into one of four segments. Based on what segment a customer fits into, a business can decide whether to engage them, what proposition to engage them with and nurture them through the sales pipeline.
These uplift segments are >>>
Uplift analysis identifies what, if any, behaviour change is likely to result from receiving a marketing message. It is the equivalent of a marketing crystal ball and it allows marketing departments to allocate funds far more effectively.
Some of the benefits of uplift analysis include >>>
By using uplift analysis, businesses can identify the customers that are most likely to yield value as a result of marketing activities. They can then limit those marketing activities to the most desirable segment of customers – great customers. This approach both creates efficiencies and directly affects the bottom line by reducing the cost of customer acquisition.
About the author
Andy Moy is the Director of Customer Engagement Management at Pitney Bowes Software. Across a range of industry verticals, including Financial Services, Telecommunications, Retail, Automotive and Public Sector, Andy is passionate about customer engagement strategy being the enabler of measurable success.