The RFM model offers marketers a way to identify customer segments that can be targeted with highly personalised and impactful messages. Given that boosting customer retention by just 5% can boost a brand's profits by 25% there is no doubt that this is something worth investing in.
What is RFM?
RFM stands for recency frequency and monetary value.
While modern marketers have access to a huge amount of data it can be difficult to know which details are most important when it comes to segmenting and scoring customers.
The RFM model provides a simple and effective tool for doing this. Let's take a closer look at each RFM attribute in more detail:
- Recency: The length of time since a customer bought something
- Frequency: How often a customer buys products from your store
- Monetary value: How much a customer has spent with your brand
What is the RFM model?
The RFM model is a customer segmentation method which revolves around recency frequency and monetary value.
By assessing these three behavioural variables brands can successfully segment their customer base and target each group with marketing messages based on their past actions.
Although the RFM segmentation model was first conceived way back in 1995 for direct mail marketing today it remains useful for eCommerce marketers because it provides an objective and effective process for identifying high value customers.
Advantages of the RFM model
While there are countless ways eCommerce marketers can segment their audience the RFM model remains popular for the following reasons:
The data is readily available
RFM analysis requires data that you're sure to have on-hand. All the information needed can be plucked from your store's transaction history.
It is also possible to apply this model to your marketplace customer base - where insights into audience behaviour and demographics can be limited.
The simplicity of the RFM model means marketers can implement it without the help of expensive software or expert data analysts.
It can power personalised communications
By using the RFM model to segment customers brands can differentiate between new customers and long-term clientele as well as loyal customers and those who are no longer interested. You can even use it to spot potential brand advocates.
With these groups set out it's possible to put in place bespoke communications and eCommerce personalisation to generate higher engagement increased loyalty and greater customer lifetime value.
It makes your eCommerce marketing more impactful
The RFM segmentation model offers a high level overview of your customers. This provides insight into the overall health of your online business the effectiveness of your retention strategies and a better understanding of your customers generally.
It will also identify customers that are most important to your business. Marketers can use this information to focus resources where they can truly make a difference. They can also identify trends or traits that tend to be common among high value groups and then build lookalike audiences to target with PPC advertising.
Three steps for using the RFM model
It is possible to use software to implement the RFM segmentation model. But you can also run through the process yourself using your analytics tool and a spreadsheet.
Whichever option you choose it's worth being familiar with each step of the manual process as it will give you a greater understanding of how your audience is segmented.
Step 1. Pull the right data
Be sure to add the sales amount number of orders and the most recent order date for each customer to your spreadsheet.
Step 2. Apply an RFM score to each customer
Assigning RFM scores to each customer will allow you to group them appropriately.
Your recency value is simply the number of days it has been since a customer last bought something. Depending on your eCommerce niche it may make more sense to use weeks or months here instead.
For frequency you simply list how many times each customer checked out during a set time period. A month six months or a year may all be appropriate depending on your industry. Then to calculate monetary value you simply tot up each customer's total sales for a set time period.
Step 3. Creating customer segments
Finally you need to divide your customer list into groups based on these values. This will vary depending on how many customers you have and how granular you want your targeting to be.
If you set three tiers for each variable you'll end up with 27 distinct customer segments because they can be combined in that many different ways.
Recency:Frequency:Monetary value:Most recentMost frequentHighestLess recentLess frequentMiddle groundLeast recentLeast frequentLowest
You'll need to set the parameters for each tier yourself. The simplest approach would be to divide your customers into thirds. For example the top 33% of your customers would be considered the most frequent the next 33% would be less frequent and the bottom 33% would be least frequent.
You can add more tiers to each variable if you prefer to get more granular. Using four will result in 64 different customer segments while using five will give you 125.
This is a good idea if you have a huge customer base and a large marketing budget as this will give you more in-depth insights and greater targeting capabilities.
Working with these segments
Once your RFM analysis is complete you need to figure out the most effective way to communicate with each one. You'll require different approaches for your active high-potential and at-risk customers.
For example your most frequent high-spending customers should be sent emails chat messages mailers push notifications and other communications that make them feel valued. You can afford to send free gifts to this group too. Just make sure they know it's an exclusive token of appreciation that isn't available to everyone.
You could also consider sending this group review requests cross merchandising promotions early access offers event invites and referral programme information.
On the other hand the likes of low-spending frequent buyers could be sent offers with monetary thresholds to increase their Average Order Value while inactive customers could be re-engaged with enticing discounts and new product news.
More tips for successful customer retention
While the RFM model can help improve your acquisition marketing it is particularly effective for improving existing customer experiences. Here are some other great ways for eCommerce brands to increase retention:
Launch a loyalty programme
This is particularly effective when used alongside RFM analysis as it provides brands with a consistent and fair way to reward their most impactful customers. Treat them to exclusive rewards content and events. Not only will this boost retention but it should also strengthen the advocacy stage of your brand's eCommerce consumer journey.
Act on customer feedback
It's a good idea to monitor ratings and reviews across your sales channels to see what people are saying about your brand. (ChannelSight's Digital Shelf tool can do this for you automatically.) You can also conduct Net Promoter Score surveys to figure out what's driving customers away.
Offer a discount to subscribers
Encouraging people to sign up for a subscription is a sure-fire way to boost retention. Alternatively you should ensure that reordering past purchases is simple. Offer quick checkout options show past purchases in user accounts and send emails asking customers if they'd like to purchase replenishable products again.
Invest in your customer service
According to research from PwC 32% of consumers will stop buying products from a brand they love after just one bad experience. So top-class customer support is essential to your eCommerce store's retention efforts. Fast and friendly service is important. But you can also wow customers with proactive communications such as post-purchase check-ins and product tips.
The RFM model is a relatively simple yet powerful method for customer segmentation. However it is worth noting that it only takes three behavioural variables into account.
Although recency frequency and monetary value are all highly important factors many other variables can be used to make tactical targeting decisions. Down the line you could consider further dividing each RFM group based on other attributes.