RFM Analysis allows marketers to segment customers and target them more effectively with personalized offers and coupons. It’s a great way to accelerate sales and develop customer loyalty. 

Segmentation and Targeting

The typical workflow for RFM analysis is divided into different categories:

• collection of transaction data, this includes unique customer id, 
• transaction amount, and 
• transaction data

Visits to websites that didn’t result in any sales are recorded in the case of eCommerce. Whenever data is aggregated at the customer level so it can be used, it can include a unique customer id, transaction date and amount spent by the customer.  The RFM package and the data pipeline that is used will determine the data that is being supplied. Business owners are also able to check the length of time customers are staying away from their websites in the first step.

RFM Tables

With the generation of RFM tables from the raw data that is available, data is aggregated at the customer level. The unique customer id, days since the last visit or transaction was made, the total revenue earned from all the transactions the customer made and the frequency of transactions are included in the second step. 

The RFM score for each customer is calculated by using recency, frequency, and monetary value in the third step.

In the fourth step, the RFM is used to define customer segments. Customized advertising campaigns, promotions, offers, and discounts are designed to retain and reactivate customers.

This table can be used whenever advertising strategies are being developed. The customers who fall in the lower ranks of the table can be targeted with special ads that will cause them to move up on the chart.

Important Questions Business Owners should Answer

• How many of your customers only shopped once?
• Do you know your loyal customers? 
• Which customers are the big spenders?
• Which customers are likely to stop shopping on your site?  

If you are unable to answer these questions, RFM analysis is best for you, use it to find your best customers and find ways to increase their spending.

Techniques that will Increase Customer Loyalty   

Adding a new customer to your list is 5 to 25 times more costly than retaining a loyal one. Research has shown that retaining loyal customers can lead to business growth. Many e-commerce sites have used win-back campaigns ( a marketing automation sequence that is used to nudge customers who have not shopped on your site for a while, the customer is nudged so they will start thinking about engaging with your brand again) to reduce customer churn. This strategy does not work at all times and some customers have still stayed away.

Smart marketers have found ways to ensure customer retention and avoid customer churn. They are proactive and prevent it by offering impeccable customer service. Verizon is one of the companies you can look at.

It is said that Verizon Wireless used data mining techniques and found out which customers were most likely to move on. They found a way to offer those customers a “save” offer, which was calculated for each customer, finding strategies that would be most effective. Added to that, they even figured out how likely it would be for each customer to respond to the offer. This was done to cut the churn rate among the contract customers, and this is how the churn rate was cut in half.

The right offer was mailed to those who would most likely accept it, the company reduced attrition from 2.6% to 1.3% per month and saved approximately 60% of what it would have spent normally.  

RFM Analysis and Customer Reactions

RFM analysis has been good at finding the customers who are not shopping on your site as regularly as before because it tells you how often they shop and how much they spend. You can easily detect if there is a fall-off in the amount that is being spent and you can identify customers who are ready to leave your business or those who have left for a while. You can also detect at-risk customers who are getting ready to churn and begin to pay attention in order to build eCommerce loyalty.

This data enables marketers to send personalized ad campaigns to these at-risk customers to encourage them to start shopping. You should spend money on the customers who are more likely to increase the amount of money they will spend. Now that RFM helps marketers to make decisions based on a new understanding of customer value and customer behavior.  Depending on RFM alone when marketers are developing a marketing plan is not a perfect idea. It is best to combine it with insights from customer feedback and the results from previous marketing campaigns and find the best way to communicate with customers.

Online shopping has become more popular, business owners can rake in huge profits whenever they use the right strategies to grow their businesses. There are more persons out there who enjoy shopping from the comfort of their own home, they have no problems with finding parking spaces at the malls, they spend less on fuel for their cars, they also use the time they would be spending at the malls to do something that adds more value to their lives. They are using the extra time to make good plans to increase their earnings so they can improve the quality of their lives. 

NextBee has performed RFM analysis for leading e-commerce brands and helped them reduce customer churn. Schedule a demo now to see how we can help your business grow.