When is retention not retention? When it’s just reacquisition! More and more companies are paying attention to customer lifetime value and how they can build on their investments in winning new customers. This applies equally to consumer organizations, business to business organizations, and membership organizations. All the heavy lifting has been done getting that new customer on board, but how can we generate more value and how does customer data analysis help do that?
The role of customer data analysis in retention is to increase the propensity that a customer will do business with you again at a lower cost than the first time they did business with you. There are two things going on here: increasing propensity and lowering costs. Increasing propensity is about making yourself more relevant by getting the value proposition message across. The more relevant you are, the more likely that someone will come back again, and by analysing your customers and their preferences, has the potential to make you more relevant.
In the digital world, we’re seeing a lot more use of classic offline retention strategies such as RFM (recency, frequency, monetary) analysis. RFM is a type of behavioral segmentation approach whereby customers (or visitors, or members…) are categorized on three dimensions:
- Recency – how recently someone transacted
- Frequency – how frequently someone has transacted in the past
- Monetary – the value of those transactions
The idea is that you can develop different strategies for different segments depending on where they are on each dimension of the RFM model. If someone is high on all three dimensions, then they are the most valuable customers that the company has and the retention strategy is to keep trust. There may not be a need for masses of activity as they probably have a string affinity to the organization or brand. However, they need nurturing and looking after, but you don’t need to “re-acquire” them. Customers who have low recency but high frequency are ones that are slipping away. Some kind of reactivation strategy is going to be needed to bring them back and it’s possible that that might require some kind of incentive.
Customers who are high on the recency axis but low on the frequency axis are new customers and they are a particularly interesting case. The main challenge for an organization is to get them to buy again, and what customer buying theory shows is that recency is the strongest predictor in this model of whether someone will transact again at some point in the future. So if someone has just transacted with you, then that’s the best time to try and get them to transact with you again. That’s why in the retail catalogue world if you buy something you tend to get a new catalogue with your order.
Completely new customers are a special case because they generally require the most effort to get them to buy again. Once someone has transacted three or four times, it doesn’t take so much effort to get them to transact again. So getting someone who has bought for the first time to buy for a second time is incredibly valuable. In some of the work that we’ve done and I’ve seen others talk about, there is a strong correlation between someone’s propensity to transact again and the time that they receive some form of marketing communication. Again, this is really relevant for new customers; the quicker you can get some form of relevant message to them, the more likely they are to buy again.
RFM is a powerful behavioral segmentation model for retention marketing activity. It’s been used for years to good effect in offline direct marketing and retaining and its use is on the increase in digital channels as well. Where I believe classic RFM is a bit blunt though is that is doesn’t explicitly take into account what the transaction was and how that knowledge can be used to increase relevance. So RFM on its own is a good start but it needs to be developed so that a customer’s interests or preferences can be included in the communication development and targeting as well. This might be looking at actual purchase history, browsing history, or previous marketing collateral that they have responded to. That data can be used to create more meaningful and personal marketing, and so improve propensity to transact again.
By using their customer data to increase relevance, organizations are better placed to execute more cost-effective retention strategies rather than repeat the initial costs of acquisition.
In this article, we look at how to use data to tailor discounts to specific customer segments, reducing the cost of customer acquisition and ultimately optimising conversion in the user journey.
As an organisation, finding the right marketing channels is an essential part of your marketing strategy.
When measuring the effectiveness of discount codes, retailers often get it wrong. In this article, we'll look at how data-driven attribution can help businesses better understand where discount codes produce the best ROI.
Many businesses invest a great deal of time and effort into knowing their customers - but too few focus on understanding them.