Doing 1:1 marketing is not a feasible option when you own a large ecommerce store.
However, an in-depth RFM analysis coupled with a CRO audit can reveal enough customer insights to help you make informed marketing and product decisions.
If you’re not familiar with RFM segmentation, this article will give you a good overview of the Recency, Frequency and Monetary Value model.
Before diving into the theory, if you own a Shopify store, go ahead and install the REVEAL app. It does the RFM analysis automatically for you!
Customer segments and preferences, with a real life example
I love coffee. When I was living in Italy, I developed a habit of having one macchiato every morning before work.
What I found interesting about coffee in Italy is the fact that you can choose from a variety: espresso, cappuccino, marocchino, americano, stretto, ristretto, cafe latte, macchiato. It depends on your preference.
We are all different and enjoy coffee in our own special way. Italians know this and found the secret formula for keeping us happy: tailor a product based on customer’s preferences.
What has coffee to do with customer engagement?
One day, as I was walking in the bar next to our office to ask for my regular macchiato I heard one of the baristas say:
“Ciao Bella, il tuo machiatto e pronto” / “Hi gorgeous, your machiatto is waiting for you”.
I had just entered the bar and my machiatto was already there on the counter, ready and waiting for me. Big smile on my face!
The baristas knew me. They knew approx the time of day I would walk in and what I like to drink and proactively made me an offer I couldn’t refuse.
They transformed a normal, repetitive experience into an extraordinary and personalized one by saying “I know what you need. Here it is, waiting for you”.
They made my day. And they did something more than this. In marketing terms, what the barista did through his action was to :
- generate positive emotions,
- form a habit, and
- upscaled me as a customer
After that experience, I kept coming back to them for my morning coffee. Besides my usual machiatto, I would add a “brioche con crema” (sometimes on the house) and also started taking my lunch and dinners there (late office hours).
I became a full customer for them.
1:1 Marketing Gets You Loyalty, But It Doesn’t Scale
Coming back to Earth, my barista story can’t scale.
But that doesn’t mean you can’t start to know your customers through their buying behaviors. This can help you begin a relevant conversation which may lead to a happy customer and a long-lasting relationship.
First things first. In order for you to have a relevant conversation with your customers, you first need to know them.
How often have you, as a Marketing Manager, addressed the following questions:
- Which customers spend the most?
- Who are the most loyal customers coming back and placing a second, third, fourth order?
- What’s the profile of the newest customers?
- Who are those customers I am about to lose?
- Who are those customers I already lost?
RFM analysis is a pretty cool way of answering these questions.
It goes hand in hand with customer centricity, segmentation, personalization, tailored marketing campaigns and customer lifetime value. And the internet is hyped on all these indicators.
It’s on everyone’s lips: know and understand your customers will be this blog’s mantra.
What is RFM analysis in eCommerce?
RFM segmentation and analysis is a marketing method that can help you, as a marketer or data analyst, segment your customers based on their purchase history.
By answering these 3 questions: (1) how recently, (2) how often and (3) how much did a customer spent with you, you will be able to uncover buying behaviors.
Customers who recently placed an order on your website are more willing to order again compared to those who haven’t bought in a while.
Clients who frequently buy are more likely to buy again, compared to those who only bought once;
Customers who usually spend lots of money are more willing to buy again and generate revenues;
RFM is your ally in the retention game. When you know your customer’s buying behaviors, you are one step closer towards decoding their needs and preferences. So it is a step closer to starting a meaningful conversation.
According to an HBR article, retaining your existing customers is more effective for your business rather than going out and chasing after new customers.
“acquiring a new customer is anywhere from five to 25 times more expensive than retaining an existing one. It makes sense: you don’t have to spend time and resources going out and finding a new client — you just have to keep the one you have happy.”
Plus, it’s good for your company’s revenue (HBR article):
A 5% increase in customer retention can increase company revenue by 25-95%
How do you start with RFM analysis in eCommerce?
The first step is to have a look at your customer’s database and extract historical data available for calculating the recency – frequency – monetary values:
- Recency – the date of the latest purchase made by a customer. What date is today? Deduct from this date the date of the last purchase made by a customer. You will get the number of days since the last purchase aka the recency. Smaller values indicate an active customer while bigger ones a dormant or lost one.
- Frequency – the number of orders each customer placed with you up until today. Bigger values indicate a loyal customer on your website.
- Monetary – sum-up the value of all the orders placed by a customer. Bigger values indicate a big spender on your website.
The second step: now that you have the data for all 3 dimensions, you need to group it and give each group a score.
Essentially, you need to come up with a score from 1-5 given to each dimension:
- Recency – the highest score of 5 goes out to the customers who bought most recently;
- Frequency – the highest score of 5 goes out to the customers who placed the highest number of orders;
- Monetary – the highest score of 5 goes out to the customers who spent the most in terms of value.
You will get something like this: 555. A customer with a 555 RFM score is your most valuable customer. Call it a Champion or a VIP, they recently bought from you, placed the highest number of orders and bought of high value.
Look at RFM as a labyrinth. If you change one value from the equation, you are suddenly dealing with a new type of customer, with a different buying behavior, different needs so different marketing tactics.
The third step is making sense of your RFM scoring model.
NOTE: REVEAL, our Customer Lifetime Value Optimization software, does the RFM segmentation automatically for you.
We’ve written an in-depth guide on how the RFM model works here: How REVEAL’s automated RFM segmentation and analysis works.
Also, here you can learn how to think and what to look for when doing an RFM analysis in eCommerce: Customer lifetime value analysis in Reveal.
Now let’s get back to our numbers game.
What does a score of 125 (R=1, F=2, M=5) tell you?
What you have here is a group of customers who (R) bought from you a long time ago, (F) they place few orders but (M) those were valuable orders.
You can’t afford to lose these customers! Maybe write a hand-written letter? Or maybe drop them a courtesy call to ask about their experience with your products and services.
What about a 442 (R=4, F=4, M=2)?
What you have here is a group of customers who (R) bought recently from you, with an (F) high frequency (so many orders placed) but of (M) low value.
So what would the next step be in this case? Maybe run a loyalty campaign? Send them some discounts or coupons? Maybe recommend other programs?
How can you use RFM segments in real life?
It’s time to look in your backyard and see whether the marketing activities you have in the pipeline to execute in the next quarters are in sync with the customer segments you have in your database.
- Are you properly rewarding you VIPs?
- Do you offer differentiated discounts?
- Are you touching base with your most loyal customers through a courtesy call to find out about their experience with the products and services you are delivering?
- Do you actively work on building a relationship with your customer through the onboarding process?
- Are you talking to those customers you are about to lose?
- Are you creating tailored emailing campaigns?
Here is an example which may help you to put things into perspective when you think about how to make use of RFM analysis in eCommerce.
The way you think and segment your customers will vary. You can play around with the points and create as many and as diverse groups of customers as you see fit.
Ask someone from your team to give you access to the information which will help you extract the recency – frequency – monetary information.
Then, see how many customers you have in your 1% of revenue. 1% of the best customers generates up to 30 times more revenue.
Can you send an email to them to reward them? What would you write? Would you pick up the phone to ask about their experience with you? Maybe start with 5 or 10 customers to get a feeling of the real world.
Use RFM to turn customers into loyal brand advocates
In an RFM analysis, I would be considered a “loyal customer” based on:
- Recency – days since last purchase – I would get the highest score of 5 because, for my entire stay in Italy, I would regularly come in and get a coffee from that bar;
- Frequency – the number of purchases made – I would get the highest score of 5 because I was having one and sometimes two coffees a day;
- Monetary – the value of the purchases made – I would get a score of 2 because my purchases were not very valuable (money wise) compared to what I could have spent there.
Drawing the line, my RFM score would be 552, a score which makes me a loyal customer. So what did the baristas do with this information?
Seeing that I am a loyal customer and knowing what my favorite type of coffee is, they provided a personalized experience. They started building a relationship with me as a loyal customer by allowing me to pay later or the next day.
What were the results of this tailored approach?
A happy and loyal customer! Not only did I become a loyal customer for breakfast, I would also come back for lunch and dinner. I would go for a second cup and even bring in my colleagues.
As we created a connection, they soon found out my name and started greeting me with “Buongiorno Anca!” which created a sense of familiarity and community.
In closing, I am curious to know:
- Have you heard of RFM analysis before?
- Are you already performing an RFM analysis on your eCommerce customers?
If you’re using Shopify, you can take advantage of our automated RFM software by adding the app to your store.