It goes without saying that serious marketers understand the importance of “know thy customer.” Businesses that use RFM modeling for marketing can segment their customers into homogeneous groups. It is then easier to understand the traits of each group, and engage each one of them with relevant campaigns rather than segmenting on just customer age or geography.

In order to deliver relevant, personalized, and timely content to your customers you need to look beyond demographics. One of the most popular, easy-to-use, and effective segmentation methods to enable marketers to analyze customer behavior is the RFM analysis.

What Is RFM Analysis?

RFM stands for Recency, Frequency, and Monetary value, each corresponding to some key customer trait. These RFM metrics are important indicators of a customer’s behavior because frequency and monetary value affects a customer’s lifetime value, and recency affects retention, a measure of engagement.

The goal is to predict which clients are more likely to buy again in the future. RFM model is a proven marketing strategy based on customer behavior segmentation. It groups customers based on their purchase history – how recently, with what frequency and of what value did they buy.

RFM analysis helps you find answers to the following questions and more:

Who are my best customers?
Which customer has the potential to buy more?
Who has been churned out/has lapsed?
What customer can the business afford to ignore to effectively utilize budgets?
Which customer can be converted by creating value through promotions?
Which of your customers are most likely to respond to engagement campaigns?
Who are the customers who buy only discounted items?

Through RFM, you can recognize and focus on converting critical customer segments like customers on the verge lapsing or dropping to becoming engaged and active customers. You can leverage the power of RFM to utilize your marketing budgets wisely and effectively, while also increasing the overall impact of marketing on your business.

How does RFM Work?

Historically, setting up an RFM model required some technical configuration based on some metrics defined by the business.

For example, you might define a core customer as a customer who:

Shopped recently (say within a month)
Visited frequently (more than 10 visits per year), and
Spent a certain amount of money ($10,000 in the past 12 months).

Any customer who falls within that bracket will belong to your core segment.

You can define different segments and give them scores so customers would be grouped according to their RFM score. The RFM combination is important in order to optimize your targeting. You don’t want to focus only on high spenders who might be casual customers who don’t visit much or did not visit recently. Also you don’t want to ignore small spenders because they might be very regular.

However, as the name implies, the RFM model is about spend and visits but the customer universe is much more complicated than that. If your business is both online and offline then you need to setup different criteria for online customers, offline customers, and both. Furthermore, you should include customer demographics and engagement to optimize the marketing spend to target those who are engaged versus those who never respond to campaigns. You add to that product categories and NPS rating (customer feedback) and you get an idea about the different scenarios that a business would be looking at.

Sounds Complicated?

On paper (or on screen), yes, all that might sound too much for a marketing team to handle. However, you can now easily define the different customer groups that are relevant to your business then engage those groups in a personalized, relevant, and timely manner. All this made possible through AI powered segmentation along with simplified UI that is designed mainly for marketing teams.

With UrbanBuz Audience, you can create any customer segments you want based on the RFM model and beyond to really optimize your customer engagement and create exceptional customer experiences.

You can visually define your customer segments by easily combining all the different elements to fine tune each segment. You can combine demographics, with RFM, with browsing behavior, abandoned carts and more to create optimized segments.

Once created, the system would automatically keep each segment up to date and in real time. You can then engage with the right audience any time whether through our omni-channel Automated Journey product, or through our omni-channel Campaign Management product.