For businesses trying to encourage customer loyalty via direct mail or loyalty schemes, segmentation plays a vital role. It is part of the process that helps marketers target the right customers with the right offers. To be able to do this successfully, segmentation needs to go hand-in-hand with personalisation.
Thanks to the affordability and availability of variable digital print, personalisation has become a viable option for businesses of all sizes. As direct mail personalisation is now more widely used, segmentation is growing in popularity. Combined, these two practices enable businesses to identify trends in order to effectively target individuals with personalised marketing communications.
By analysing customer data, customers can be tracked between value segments creating powerful insight into the customer value trends whether they have decreased, increased or stayed the same. This knowledge is critical in deciding what action must be taken to encourage defecting customers to stay with the brand, or, conversely, identifying very loyal customers and taking pre-emptive action to make sure they don’t defect.
When undertaking segmentation, the real focus should be on developing strategies that move people up the value segments, with a particular focus on how people move. For instance, with many types of organisations, customers have clearly identifiable sequences of product purchase as they move up the value tree. If such a migration path is identified, then incentives may be offered to encourage: a) faster migration and b) product migration all bought from the one provider.
This kind of migration analysis helps to identify product portfolio gaps where the customer has to go elsewhere for their next logical product purchase. The company can then either strategically fill the gap themselves, or partner with another company to do so.
For truly successful segmentation it is necessary to ensure that segments are thoroughly refined. For example, a business may say that ‘customer A’ has spent £x in the past year and is therefore its best customer. But without looking at frequency or regularity of spending this could be misleading. Customer A may have only made purchases in Q1. Customer B, on the other hand, has spent half the amount of customer A but has made a purchase in each quarter of the year. Here, segmentation analysis on a more refined level would show that customer B is a more loyal customer, but of low value. In other words, they spend frequently but do not spend much in terms of value. This suggests that sending targeted, relevant offers that use sophisticated personalisation could lead to an increase in the amount spent while, at the same time, maintaining loyalty. On the other hand, customer A needs to be encouraged to spend more regularly.
Many brands gather data without knowing if it is significant or not. Much work has to go into understanding which pieces of data are truly significant for managing and encouraging customers to stay loyal and spend more.
Yet all too often, this critical piece of work is not conducted at the right time right at the start. The key thing is to always start from transactions actual purchases. A supplier has all this information to hand anyway, but a brand has to find a method of gathering that data, such as through survey questions. From transactions, marketers can identify regular or multi-buyers, particularly high-value high-loyalty individuals, and analyse their profile to see what they have in common.
Some market leaders are combining database marketing with market research. In other words, database analysis identifies common characteristics of the target group. The market research then helps to identify which of those common characteristics is most significant in predicting purchasing behaviour. In other words, database analysis identifies the ‘what’, and then market research adds in the ‘why’.
Combining database marketing with market research can help the brand whittle down the data it needs to collect and streamline the whole process. When the refined model is then used to guide consistent communications across each channel, the result is more effective because all the white noise of insignificant data has been removed.
Too many business-people believe that automated CRM systems can give them all of the answers if fed with large volumes of customer data. However, this is not the case. The ability to analyse and manipulate large data sets to produce meaningful and actionable insights is a sophisticated and rare skillset.
The most proficient analysts and database marketers tend to be those with experience within a third party provider. Precisely because they are used to looking at data in many sectors and contexts, these professionals are able to conduct their interpretation in the context of a wide-ranging knowledge of buyer behaviours.
