How does RFM Analysis Help Customer Segmentation in E-commerce

I remember that until a few years ago, there were only a handful of reliable e-commerce platforms from where I used to buy only some products- mostly books. And today, there is hardly anything that cannot be bought online, and there are n number of platforms to choose from.

Global ecommerce sales have been growing at an average of 20% per year since 2014 and a double-digit percentage growth will continue through 2021.

Global ecommerce sales growth

Back home in India, number of online shoppers was expected to reach 120 million in 2018 and eventually 220 million by 2025, and the market has the potential to grow to US$ 150 billion by 2022.

These staggering numbers indicate how high the competition is going to be, not that it already isn’t.

Why is understanding customer behaviour specifically important for e-commerce marketers?

If a specific model of sunglasses that I am looking for is available in all the top ecommerce websites for almost the same price & delivery time, what would my buying decision be based on?

The relationship the brand shares with me.

Apart from many factors that forge and build the customer-brand relationship, the level of personalisation that the brand offers in terms of product recommendations, marketing messages, etc. is a very important one. Unless you as an e-commerce marketer know me well (my preferences, behaviour, and purchase patterns), you cannot strike that one-on-one personal relationship.

It is hard to craft goal-oriented targeted marketing content until you have answers to “who are my most loyal customers, who are my highest paying customers, which customers are likely to churn, who are my one-time customers, etc.” Segmentation is a prerequisite to personalisation. And personalisation, the key to better ROI.

RFM analysis, mostly an under-valued marketing tool, is a boon for e-commerce marketers like you. Here’s how.

What is RFM Analysis?

RFM analysis is a customer segmentation method. It is based on the Pareto principle, or the 80-20 rule, which says that 80% of brand’s revenues comes from 20% of customers. Based on your customers’ past transactional data, RFM analysis enables you to segment them into specific groups. You can then address each customer group separately according to their current level of engagement.

R – shows how long it’s been since a customer placed an order

F – shows how often they order from your store

M – shows the total amount they spent on your business

Based on your business dynamics, you can define a threshold for each of the three parameters. If they bought in recent past, they get higher points. If they bought many times, they get higher score. And if they spent bigger, they get more points. The combined scores give you the RFM score.

Customer segmentation based on recency, frequency and monetary value

  • High RFM customers: The customers who score the highest for recency, frequency and monetary value are your High RFM customers, your most valued ones. Advertise your high-end products and reward them for their positive buying behaviour with unique offers.Ecommerce giant Amazon rewards its loyal customers with prime-exclusive deals. This strategy has helped Amazon build its prime members subscribers base to more than 100 million!
  • Medium RFM customers: Your newest customers who have a high recency score but low frequency and low monetary value are most likely to fall in this category. With proper engagement and nurturing, these users have the potential to turn into recurring customers.
  • Low RFM customers: With low recency, low frequency and low monetary value, this segment comprises your most inactive and least engaged customers. Again, with tailored promotions, they can be moved into the medium RFM customers.

RFM Analysis and Ecommerce

RFM Analysis enables data-driven e-commerce marketers like you to get a deep-dive into the behaviour of their customers, predict their next actions, and hence enhance their customer segmentation strategies accordingly. This will, in turn, help you create marketing programs with higher response rates and conversion rates.

Imagine the almost-magical power you have with you when you know exactly:

  • Who are your customers who spend the most buying your products?
  • Who are the most loyal customers who keep coming back for a second, third, and fourth order?
  • Who are the newest customers?
  • Who are the ones you are about to lose?
  • Who are those you have already lost?

Once you have sliced your customer base into these fine segments, you can fine-tune your marketing campaigns that perfectly tailored for each category. Let us look at some major challenges of ecommerce brands, and how RFM can present a solution.

  • Preventing customer churn: ‘Need Attention’ & ‘At Risk’ segments are most likely to churn. And they are the ones you actually need to pay special attention to.Well, customer churn is not a challenge exclusive to e-commerce brands, nevertheless, it is a huge one. It is a well-known fact by now that acquiring a new customer is 5 to 25 times more expensive than retaining an existing one.On the other hand, studies by Bain & Company, along with Earl Sasser of the Harvard Business School, have shown that a 5% increase in customer retention can lead to an increase in profits between 25 and 95%. Based on the above, most e-commerce brands use win back campaigns to tackle customer churn. Timely and personalized campaigns to reconnect with these customers can help you retain them.Here is an email campaign by Nykaa that tries to entice the “At risk” customers.

    Email campaign by Nykaa that tries to entice the “At risk” customers

Related read: Win-Back Email Campaigns: 7 Best Examples to Help Re-Engage your Customers

  • Calculating & improving the customer lifetime value. While Customer Lifetime Value is an incredibly important metric, it is quite difficult to calculate. You may have customers who make small purchases every week, and there might be others who make big purchases once in 6 months. RFM analysis makes it simple to analyse the customer groups individually and determine the CLV for each. This blog explains in detail how to calculate CLV using RFM analysis.Once you have your CLV-based segments, you send your upsell and cross sell campaigns to the segments who are most likely to respond. One of the most effective campaign ideas to boost CLV and relationships is by promoting new products to loyal customers. These users are not just your customers, but also become your brand advocates.
  • Reduce marketing costs and increase ROI: Nontargeted marketing campaigns are an expensive affair. If I am anyway a loyal customer to a brand, I do not really need enticing promotional campaigns. RFM based segmentation enables you to focus on smaller segments of customers, and thus reduce costs.

RFM Analysis – the heart of your digital marketing system

To strive in the present customer-driven e-commerce industry, deep understanding of each customer’s behaviour is mandatory. This information forms the basis of every marketing and business strategy you run. And RFM analysis is the one tool that can provide you these valuable insights.

RFM analysis can actually be the heart of your whole digital marketing system, pumping life blood into every campaign across channels.

Once you have integrated RFM segmentation in your website, you can direct all your focus on designing impactful and creative content, rest assured that it is going to reach the right user. To understand how Smartech’s RFM model can aid you in your growth journey, get in touch with us today!

Ritu Poddar

Ritu Poddar

Ritu is a Technical Writer & Content Developer by profession, and a Poet & Creative Writer by passion. She works as Assistant Manager, Content Development at Netcore.

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