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Conversion Rate Optimization (CRO) has been a big buzzword for quite some time, and everyone wants a piece of the pie. I don’t want to discount the importance of that work as it is what I’ve hung my hat on for the past few years. I still believe it’s a thriving practice and essential for organizations that want to increase the effectiveness of their online presence. The tactics of A/B testing, heuristic analysis, etc. will always be important to the understanding of website visitor behavior and optimizing websites.
However, it’s crucial that optimizers also consider how to drive customer retention as part of their process if they want to do full-funnel growth marketing. This work starts with developing a Customer Value Optimization (CVO) strategy. To start that work, you first have to analyze the buying patterns of your customers. An RFM analysis will set you on the right path to doing this.
RFM analysis is a CVO tactic and is the start of building out any plans to increase the dollars that customers spend with your shop.
What’s the difference between CVO and CRO?
But wait… before we get too far along, let me clarify what CVO is and how it’s different from CRO.
If you run an e-commerce store, CRO is all about increasing the likelihood that a customer will purchase from you. These approaches are useful for getting customers in the door. CVO, on the other hand, is the art of increasing your share of the customer’s wallet over time. Ultimately, CVO drives the retention of customers who have already purchased from you.
Now, to jump into RFM Analysis. For clarity’s sake, RFM stands for:
- Recency – when was the last time this customer purchased from your store?
- Frequency – how often has this customer purchased from your store?
- Monetary Value – how much money has this customer spent in your store?
Here’s a chart from the Omniconvert blog that shows it in practice:
|Points||Recency (days since previous six purchase)||Frequency / Monetary values (number of orders and orders value)|
|5||within the previous month||customers who are in the top 5% in the database|
|4||within the previous three months||customers who are in the top 20% in the database|
|3||within the previous six months||customers who are in the top 30% in the database|
|2||in the previous year||customers who are in the top 60% in the database|
|1||more than a year ago||the customers who spent and bought the least|
This analysis is relatively similar to assessing customer lifetime value. Except if you are looking only at Customer Lifetime Value (CLV), you’re only looking at one part of the puzzle.
How does RFM analysis give you more insight than CLV alone?
For example, let’s assume a first-time customer (Customer A) visits your website and purchases $2,500 worth of merchandise but never orders again. If your average order value is $125, that customer’s purchase is the equivalent of 20 orders. Awesome!
Let’s also assume that the average loyal customer (Customer B) purchases from you every 60 days. That means that in a year, an average customer will have spent $750 with you. Even in two years, they will only spend $1,500 with you.
So Customer B is purchasing from you regularly but at a lifetime value that is $1,000 less than Customer A. When rating these customers, Customer A would bubble up to the top of the list, though they haven’t bought anything in two years. If you spend your time and dollars on the wrong marketing to this customer and ignoring more loyal customers, you may falsely believe that your retention efforts are failing.
That’s where RFM comes into play, as it adds a couple of additional dimensions to the revenue analysis that you’ve already done to ensure you’re as effective as possible in your analysis.
Here’s what this looks like using both CLV and RFM analysis:
Customer Lifetime Value
|Customer Name||Total Revenue (last two years)|
This analysis is as simple as listing out how much a customer has purchased over time. Customer A is the top priority customer here.
|Customer Name||Recency||Frequency||Monetary Value|
In this case, Customer B is stronger with an RFM score of 544 (numbers from the Recency, Frequency, and Monetary Value columns, respectively). Customer A has an RFM score of 115. In the “real world,” you’d also need to account for things like returns, the impact of discounting, etc., so your results wouldn’t be as cut and dry as before.
According to the graphic below, Customer A would be in the “Don Juan” (unfaithful) category, and Customer B would be in the “Lover” (very loyal but still room to grow) category.
Usually, several people may match the profiles of Customer A and Customer B. In an RFM analysis, you’d work with clusters of customers and assign them to distinct groups, based on what you see above. From there, you can refine your marketing, optimization, and personalization strategy to focus on engaging customers where they are.
How do you use RFM scores to drive your marketing and optimization strategy?
A customer’s RFM score can help you determine what strategy is more appropriate for keeping them engaged and buying from you. Think of an RFM score as your “customer love” metric. As a customer spends more money, more frequently, their score increases over time, as long as there hasn’t been much time past since their last purchase activity. When it comes to using this data, you likely want to do one of a few things:
- Keep them spending
- Get them to spend more / make more purchases
- Get them back to your store to buy again
- Eliminate them
For each customer, you want to make sure you’re taking the right actions. Following the concept of “customer love,” here are a few categorical breakdowns for customers and ideas around engaging them.
Keep them spending
Some of your best customers are likely already active, and the biggest goal is to keep them spending money in your online store. Naturally, your best customers would be your Soulmates. These customers have the highest RFM scores (likely 555). They spend a lot of money with you regularly and are a high-value customer.
An excellent example of a marketing initiative that drives positive behaviors for Soulmates is the My Best Buy Rewards program. It not only rewards customers with points for purchases, but there are special rewards like extended return policies (45 days vs. 15 days) for customers who spend over $3,500 per year with the retailer.
Best Buy’s program is an example of a company offering an obvious incentive for customers to keep all of their electronics purchases under “one roof.”
Get them to spend more/make more purchases
There are several categories of customers who you want to focus on driving additional spend or additional purchases.
- Lovers: While these customers haven’t spent as much as your Soulmates, they have bought from your store recently and on more than one occasion. Your goal is to get these customers to spend more with you. Devising a plan to get them ordering more regularly involves getting to know what they’ve purchased and using that insight to make an appropriate purchase recommendation.
- New Passion: These customers place high-value orders and have done so recently, even though they may not have made many separate purchases over time. Your goal is to get these customers to come back and spend with you again. These customers already order in high-value, so resist the urge to tempt them with a coupon code (I’m looking at you!). Instead, aim to get them to buy again (if appropriate) or make an offer that is highly complementary to one of their past purchases.
- Flirting: These customers are somewhat inconsistent at best. They have placed some high-value orders but are not necessarily dependable to keep ordering. Your goal is to get these customers to buy more, ideally offering an appropriate complement to their prior purchase history.
- Potential Lovers: These customers place orders that are likely higher than your average order value, but they don’t purchase frequently enough or recently enough to be a “Lover”… yet. Your goal is to get these customers to buy more regularly. The best way to do that is to offer targeted marketing for repurchases based on relevant timing parameters, if applicable.
- Platonic Friend: These customers are buying from you fairly regularly but likely below the average order value. Your goal is to get these customers to spend more when they place an order. Try offering special bonuses when they reach a certain spend threshold (free shipping works well here if that isn’t already part of your e-commerce store’s value proposition).
It may sound simple, but one of the best ways to re-engage these customers is with a targeted email campaign. Here’s a fun example of how to roll this out: https://sleeknote.com/blog/customer-retention-emails.
Pro tip: don’t send the same email to all of these customer groups. Ideally, you’d develop a specific engagement strategy for them, based on where they are as a customer. That will allow you to tap into the real power of RFM Analysis.
Get them back to your store to buy again
When you’ve either lost or are at risk of losing a customer, you have to focus your energy on getting them back to your store. The below categories of customers can benefit from a warm welcome back to your site.
- About to Dump You: For all intents and purposes, this customer is inactive. They haven’t ordered for a while, regardless of average order value. Your goal is to get this customer back to your store. This scenario is an appropriate use for a “win-back” campaign that is likely coupon driven. You can see a few examples of “winning” win-back campaigns (see what I did there?) here: https://www.smartrmail.com/blog/12-examples-of-irresistible-win-back-email-campaigns/. You may also want to leverage other channels (social ads, programmatic, etc.) to recapture this customer.
- Don Juan: These customers spent a lot of money on one order but only ordered once and disappeared. Your goal is to get this customer back to the store again, but you also need to understand their motivation for buying. If they bought from you one time and it was situational, you may not get them to purchase again. So, you must dig a bit deeper to understand your Don Juans and make an appropriate offer. Be careful, though; you don’t want to spend too much time working to recapture your Don Juans, as they may have only bought from you for a reason. If you have insight into NPS or other survey data, this can help you gain a deeper understanding of this group.
- Ex-lovers: These customers used to be either Soulmates or Lovers, but they stopped ordering from you and likely haven’t bought anything for several months. Your goal is to get this customer back to your store. You may want to invest some time here because this is a customer that you’ve previously had a good relationship with but lost. Like with your Don Juans, you want to review any additional insight you may have in the form of NPS or other survey data to understand the breakdown. A way to reach these customers is to develop a personalized email outreach strategy where you ask them why they stopped buying (unless you already have that information) and present a specific offer to that customer based on the reason they give for not being a customer any longer.
- Apprentice: This is the first time customer. Their spending may be either high or low, and not enough time has passed to determine if they will be a repeat customer and move into the “Lover” or “Soulmate” category. Spend the most time here. If you can get a customer to come back for a second purchase, they are more likely to keep purchasing from you. Data from Yotpo says that nearly 15% of customers who shop in an e-commerce store are returning shoppers and returning customers account for approximately 33% of all store revenue. That’s huge, and it makes this group ever so critical to get back to the store again. Invest in a multi-channel approach (email, ads, etc.) to reach them.
Unfortunately, there is no one-size-fits-all approach to getting customers to come back to your e-commerce store. But, using the data you have from your RFM Analysis, you’ll be able to create a retention strategy that makes sense for each type of customer.
For some customers, it may be more costly to invest time, energy, and attention into getting them back to your store. Those customers are your Break-ups. These customers are inactive, likely until a deal rolls around. Then they go dormant again. If these customers buy from you and add to your revenue, that’s fantastic, but I’d remove them from any active marketing lists, as these aren’t your loyal customers. Can they change? For sure. And you’d want to make sure you include them in any larger email newsletters, etc. Focusing on a retention strategy for this group is likely a wasted effort; as you know, you’ll probably only attract them when you have a discount of interest.
Wrapping it up
So there, now you know what RFM Analysis is, how to do it, and how to use it to drive your customer retention strategy and essential optimization activity.
As you can see, it’s entirely possible to complete an RFM analysis on your own using nothing more than a spreadsheet. This process can be a lot of manual work, though, and you’d likely need to spend a lot of time not only thinking through the logic for how you score your customers, but you’d need to maintain this on an ongoing basis.
Or, you can use a tool like REVEAL to calculate everything dynamically without a lot of labor hours. Oh, and it’s free through December 31, 2020.
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