It’s a well-established fact that providing a personalized user experience is key to retaining your customers for longer and getting a higher lifetime value (LTV). The best way to personalize at scale is by segmenting your customers into like-minded groups based on behavior, brand preference, purchase pattern, etc. As your business grows, segmentation is what helps you narrow down customer needs and expectations ever more finely.
But how do you decide what segments to create? Despite being followed rigorously by successful marketers, segmentation has been missing a key ingredient to unleashing its true power – real-time customer data. Moreover, traditional methods of segmentation are manual and based on hypotheses, leaving a large margin of error. RFM analysis solves this problem by automating and integrating data related to actual recency, frequency, and monetary attributes of customers into marketing decisions.
Introducing RFM Segments based on RFM modeling
RFM analysis is a proven marketing model for behavior-based customer segmentation. It has its roots in the retail industry. Customer segmentation on the basis of the analysis of recency, frequency, and monetary value of purchases goes back all the way to the mid-nineties when the model was successfully tested by retail marketers to maximize returns on printing and shipping costs of catalogs.
Applied on digital platforms, the RFM model becomes even more effective because of the ease of data integration. The model assists in grouping customers into categories such as loyal, promising, at-risk, etc. based on their purchasing behavior.
What makes MoEngage’s RFM Segments using RFM Analysis different
Data forms the foundation on which RFM’s success as a predictive segmentation tool is based. MoEngage’s Analytics suite brings in two important features to convert this data into a competitive advantage for clients:
- Make all the data work for you and not just a sample Compared to the competitors we use ALL the user data insights that are available for a product and not just a sample of it. We can get to this level of comprehensiveness because of the complete automation of scoring, data analysis, and categorizing customers into appropriate segments. This removes the two big risks of sampling: - Leaving out potentially useful data from decision making - Skewing results, thereby reducing the accuracy of the segmentation process.
- In-depth customization capabilities: With RFM Segments you can create RFM models on the go by selecting recency, frequency, and monetary parameters and view user counts in the respective categories. You can also define the time period for which you want to run the model. Plus you have the flexibility to create segments from the RFM categories and use these segments to send new campaigns.
- Higher flexibility: You can also download reports and customer data of specific categories to further analyze segments and campaigns. View customers transition from one category to another and engage them with a single click then and there. Create sub-categories of RF, RM, and FM models on the go to view user counts in these categories.
Why traditional segmentation has limited impact
One might argue that data has always been a part of segmentation. But this is only part of the story, for the following reasons:
- Customer insights and behavioral trends have been used selectively in the past, in support of hunches and hypotheses. To take the personalization of marketing to an entirely different level, integrating real-time customer behavior data into the segmentation process is a must. The more you base your marketing decisions on data, the more precisely targeted they become.
- Whenever data has been used for segmentation, it has largely been a manual process. Quality is often compromised because of the time and effort that goes into maintaining and updating the data being fed into decisions. Like all manual processes, sifting through data for segmentation is also prone to errors and oversight.
- Lastly, the lack of granularity has been a serious challenge in traditional segmentation. So, you either have the same customers falling into multiple buckets leading to inaccurate targeting. Or, a significant proportion of customers end up being left out of the buckets altogether, leading to lost opportunities. Both these scenarios lead to an inconsistent experience for customers, in turn debasing the value proposition of your product.
How MoEngage's RFM Segments work
In our systems, each parameter is assigned a value, which ranks customers on the basis of these values. For example, a customer who visited the website 1 day ago (recency), has made 10 purchases over his lifetime engagement with the website (frequency) and whose last purchase is worth $1,000 (monetary) would be assigned values of 5, 4, and 4 on the three parameters respectively. His overall RFM score would be the average of these values at 4.33.
What is unique about this process compared to earlier methods of segmenting is that all of this is system-driven. There is no manual interference, thereby reducing errors. Customers with similar scores are categorized into the same segment to enable accurate targeting by marketers.
The different segments into which you can bucket customers on the basis of their RFM score are:
- Champions – These are the most recent visitors to the website/app. They are also the most frequent and highest-spend customers.
- Loyal customers – These customers have visited recently, visited often, and made high-value purchases.
- Potential loyalists – These are recent customers who have spent a good amount of money purchasing on the website/app.
Customers falling in the buckets above typically have high RFM scores. But that doesn’t mean you can become complacent with them. You need to keep monitoring their scores and move them to appropriate buckets to ensure that you are always engaging them effectively.
Moving on to other buckets:
- Recent users – These customers have visited the website/app recently. But they are not frequent visitors, nor has their recent purchase been worth a lot of money.
- Promising – These customers haven’t visited too recently, neither are they among the most frequent or highest value shoppers. They have average scores on all parameters.
- Needs attention – These are customers who have made high-value purchases in the past. However, they haven’t visited the website/app recently.
- Price sensitive – These users have visited recently. They are also frequent but do not spend much money.
- Can’t lose them – These customers have made high-value purchases in the past. However, they do not visit the website/app anymore.
The above categories of customers are candidates for loyalty campaigns. Analyzing their behavior and spending patterns helps to design the right kind of campaigns that they will respond to.
The last two buckets consist of customers who are on the verge of churning out. It is essential to understand them and win them over before they are completely beyond reach –
- About to sleep – These customers have below-average scores on all three parameters.
- Hibernating – These are customers who visited the website/app a long time ago and didn’t spend much.
You can not only bucket customers into the above segments but also view dynamic transitions from one bucket to another.
With such high levels of data integration, automation, and customization, RFM allows you to create very nuanced segments to personalize the experience for all customers. That doesn’t mean you completely do away with traditional segmentation - that continues to be an important part of successful marketing efforts. A successful marketer intelligently combines RFM with traditional segmentation to get the best results.
Here’s what you should do next:
- Read an in-depth guide on 'RFM Segments using RFM Analysis'
- Looking for ideas on creating effective omnichannel marketing strategies? Read our beginner's guide to omnichannel marketing here.
- Watch videos, listen to our podcasts, or register for an upcoming webinar for more such information.
- If you prefer having a free demo of our products: Get in touch with us.