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In the second to last chapter about RFM segments, you’ve read about Apprentices, a group of new customers that is very important to your eCommerce store. We’ve suggested some tactics to encourage them to place the second order and transform these new customers into repeat customers.
We dedicate the last chapter to Breakups.
This RFM group is represented by deal hunters and their scores reflect that they are only interested in your discounts. The breakup segment includes inactive customers, likely until you have another discount campaign rolling.
This is the default configuration of the Breakup segment, as seen in Reveal. In this representation, the segment and sub-segments can be analyzed by five metrics: number of customers, order count, revenue, revenue per customer, average order value.
Let’s take a look at the Breakup RFM scores for all variables – Recency, Frequency, and Monetary Value.
Their score for Recency is 1 and reflects that they are inactive customers. The Frequency score varies from 1 to 5 and even though it reaches the highest scale value, don’t get too excited. Their Monetary Value score is low regardless of how often they buy from you, reflecting that they are attracted by your discount campaigns and low-value products.
What should you do with this RFM segment?
Well, you will go a long way to change their behavior and transform them into loyal, valuable customers.
Considering the inactivity and current behavior of Breakup customers, is it worth the effort? Here are three possible scenarios:
1. You might educate them by capturing their attention with valuable information, not just cheap products or discount emails, and ads. Show them the value beyond the products in your e-store: the value they get by becoming loyal customers.
2. You could generate loyalty but expect low Average Order Values as they might:
- Have a lower income;
- Not be interested in the high-value products of your e-store;
- Not be interested in buying products at full price.
- You might find this group useful when you want to get rid of stock because it’s a target that responds to discounts.
3. Note that when we’re talking about valuable customers, we’re also referring to those who have a low order value but are loyal to your brand. Do not mistake their value to your business for their purchase power. Take a close look at the RFM scores and behavior analytics because they might be your Platonic Friend or Apprentice customers, not Breakup customers.
The number of customers in the Breakup Segment compared to other RFM segments (as seen in the Customer Count by RFM Group report in Reveal).
It’s not easy to deal with Breakups. To shrink this RFM segment’s size, monitor its evolution and avoid attracting more customers like Breakups as they aren’t a segment that can generate sustainable growth.
Looking at historical and predictive customer lifetime value (the revenue produced by the average customer in the Breakup group) will help you decide if and how you should include this group in your marketing strategies.
Historical CLV helps you understand their behavior until now, while predictive customer lifetime value is a metric that uses a predictive model based on the last 12-month data and is calculated automatically in Reveal.
How to avoid customers like the ones in the Breakup segment?
Without an optimized customer journey, Platonic Friend or Apprentice customers might end in the Breakup group. This is why you should monitor customers’ behavior across all groups and the evolution of their Recency, Frequency, Monetary scores.
Reduce the number of discount campaigns
If you’re attracting too many deal hunters, you’re probably creating too many discount campaigns. We’re not talking about campaigns that help you get rid of stock as a season ends but about always promoting price deals for an extensive array of items.
Discounts attract new customers that have increased price sensitivity and they are hard to retain as they do not show loyalty to your brand or any of your competitors. Sales promotions are not healthy for your business either as they generate short-term results such as sales volumes that tell you nothing about the quality of newly acquired customers.
Discount campaigns might even scare away high-value customers looking for more than a random eCommerce store and bargain prices. They want to buy from a trustworthy brand that offers high-quality experiences.
Lookalike audiences based on your Soulmate and Lover customer segments
If from all segmentation techniques you choose RFM analysis, you get a better understanding of your customer base and puts things into perspective regarding customer retention strategies and other eCommerce processes that help your business grow.
Building an RFM model helps you identify your most valuable customers and attract more new customers that are more likely to become loyal customers, with high scores for Recency, Frequency, and Monetary Value.
You can manually segment your customer base into RFM groups, which is more time-consuming and inefficient as you have to regularly update your segments. An alternative to this is using Reveal for automated segmentation and customer value optimization. This will allow you to tag your customers according to their RFM group and generate lists that can be easily used for email marketing or exported paid advertising.
Export customer lists that include Soulmate and Lover segments and use those lists to create lookalike audiences for your paid media efforts. Once you’ve converted new customers, make sure you have various tactics to generate the second purchase, then form a purchase habit that will transform into loyalty.
We talk about effective tactics you can use to generate loyalty across RFM segments in our previous chapters, so check them out and find the ones you’re missing from your strategy:
Retaining valuable customer segments is a marathon. You need the right strategies, tactics, products, and analytics tools to obtain maximum results.
Use your valuable resources to focus more on actions that positively impact your customer experience, loyalty, and profits, and let an advanced eCommerce analytics tool show the way towards customer value optimization.
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