This article will help you start analyzing your customers’ behavior with RFM analysis.
Recency: When was the last time your customer purchased a product/service?
A high recency score means a customer has positively considered your brand for a purchase decision recently. Recency can be scored by grading on custom-built filters such as bought on the last 7 days/1 month/3 months and so on, depending on the nature of the business.
Frequency: How often did the customer purchase in a year/fixed time period?
A high-frequency score means a customer buys your brand frequently and is likely to be a loyalist of your brand. To calculate frequency, businesses need to analyze the total number of purchases completed by customers in a fixed time period. Frequency can be scored by grading on custom-built filters such as bought thrice in a year/bought once a month and so on, depending on the nature of the business.
Monetary Value: How much money has the customer spent on your brand so far?
A high monetary value score means a customer is one of the highest spending customers of your brand. Monetary value score can be graded on custom-built filters like spent more than R's 10,000/30,000/50,0000 and so on, depending on the nature of the business.
All the above criteria can be graded on a scale of 1 to 5, with 5 being the best score you could assign a customer. It is also critical to specify an appropriate range for each grade, in order to create groupings of customers with similar buying behavior.
Each customer is ranked in each of these categories on a scale of 1 to 5 (the higher the number, the better the result). The higher the customer ranking, the more likely it is that they will purchase again.
Example RFM scores:
Jane Doe = R3, F3, M2 overall score being 332 'Promising
John Smith = R4, F3, M3 overall score being 433 'Loyal'
Brenda Wilson = R2, F1, M2 overall score being 212 'At-Risk First-Timer'
Essentially, the RFM model corroborates the marketing adage that "80% of business comes from 20% of the customers. This system rates the strength of each customer and gives you the likelihood that they’ll come back or if you are at risk of losing them.
Patch makes it even easier to analyze by putting every customer into a segment where you can instantly view and compare lost customers to loyal customers and everyone in between.
This is a MUST HAVE for increasing customer retention.
- 1. Customer data is collected to create individual RFM scores
- 2. RFM scores are displayed instantly
- 3. Customers are automatically segmented into buckets such as New, At-Risk, Lost, Loyal, and more based on their RFM scores learn more
- 4. Use automations to target each bucket, for example:
- 5. Send a welcome text to all NEW customers
- 6. Send an incentive to any customer who moves to AT-RISK
- 7. Offer free loyalty cash to any customer who becomes LOST
- 8. Send a thank you card to any customer who becomes a CHAMPION
The RFM model allows businesses to gain key customer insights, through convenient data collection, and frame business strategy with those insights at the heart of every decision. The model allows the business to gain perspective on what their brand means to the existing customers, helps businesses manage customer perceptions, and also translates positive sentiment into purchase opportunities.
Businesses can recognize critical customer segments like churn-risk users, and create a bespoke marketing plan, specifically designed to retain those customers. Simultaneously, a business can also use the RFM model to maximize the potential of active customers, by creating personalized messaging and customized offerings, making them feel like high-value customers.
Personalization is one of the major benefits of RFM, as it not only allows you to target different customers with varying but equally relevant messaging, but also gives businesses the ability to recognize changing patterns of user behavior through the capture of RFM data, and move the customers to other segments if required.
Understanding the importance of RFM criteria to your business, and recognizing the importance and relevance of each, is essential to getting maximum returns from this model. This will help businesses in choosing the correct criteria, and create the right filters for segmentation.
RFM is a model based on historical data and helps forecast future behavior based on past interactions. It is essential to remember that it can be used to target existing customers only, and helps only indirectly in acquiring new customers.
The RFM model, when used in conjunction with traditional models of segmentation, can help businesses visualize new and existing customers differently, and create favorable conditions to maximize customer lifetime value. Finding the right balance between focusing on new and existing customers, along with recognizing behavioral nuances within them, will help businesses create personalized customization, leading to brand trust and loyalty.