By Andy Wood, MD, GI Insight
For businesses trying to encourage customer loyalty via direct mail or loyalty schemes, segmentation plays a vital part. 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 - the practice of tailoring direct marketing communications to individual recipients using variable digital print and variable insertion so that relevant offers are targeted at to suit the recipient.
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 and the two practices combined enable business 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 inducements may be offered to encourage (a) faster migration and (b) product migration all bought from the one provider. This kind of migration analysis also helps 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 store 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 spent with the store in the run-up to Christmas. 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 (i.e. spends frequently but does not spend much in terms of value), suggesting that targeting him or her with personalised, relevant offers could lead to and increase in the amount spent while maintaining loyalty.
Many brands gather data without knowing if it is significant or not. By significant, we mean influential on the customer’s existing or potential buying behaviour. The key thing is to always start from transactions – actual purchases. A retail brand has all this information to hand anyway.
An FMCG brand has to find a method of gathering that data, such as through sponsored questions on a lifestyle survey. From transactions, one 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 helps 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”.
This helps the brand whittle down the data it needs to collect and streamlines the whole process. When the refined model is then used to guide consistent communications across each channel, the result tends to be much more effective because all the white noise of insignificant data has been removed.
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