You, as a marketer, continue to face a data deluge with ever-increasing volumes of engagement data and (hopefully) a growing list of customers. And this reality can create a few challenges, namely:
How can you effectively find signals from this ocean of data?
How can you identify your key customer segments to provide a contextually relevant experience to increase retention and ROI?
And once you’ve identified these segments, how can you quickly take action on these insights?
Oracle Responsys Campaign Management recently introduced a new feature called RFM that will help you address these challenges. RFM stands for Recency, Frequency, and Monetary analysis. It is a customer segmentation model that hypothesizes that customers who engage or purchase more recently and frequently, and spend more, are more likely to respond positively to future promotional offers. While this may seem qualitatively obvious, RFM provides a quantitative approach to measure these attributes objectively.
Figure 1: Oracle Responsys Campaign Management RFM Dashboard
Ecommerce systems use the recency, frequency, and monetary value of purchases to build the RFM model, but in the digital marketing world, there are several other valuable customer signals such as message opens, product clicks, and conversions that can be used to enhance the RFM model.
Figure 2: Comparing RFM for e-commerce and digital marketing
Oracle Responsys leverages all of these customer signals with a unique data science approach that combines message engagement and purchase behavior to generate personas that you can analyze, target, or personalize messages to. And you can do all of this seamlessly within the platform.
RFM personas group recipients by their relative customer value in terms of engagement and purchase behavior. These personas can be used to tailor messages that meet your corresponding customers' individual needs and requirements. For example, the Champions, who are your best customers with the strongest high-value engagement rate, may need to be treated with exclusive deals and privileges that make them feel special. At the same time, you may want to target the Lost customers on social networks in an attempt to re-engage them.
Figure 3: Use the RFM personas to tailor messages
It takes a three-step process to generate RFM personas:
Score each customer based on recency, frequency, and monetary value of his/her engagement over a look-back period.
Rank and distribute customers in quintiles to calculate the R, F, and M scores for each signal. Subsequently, apply a weighted algorithm to derive a composite R and F score.
Use the composite scores to create the RFM personas.
Figure 4:Three-step process to create the RFM personas
You can easily use the RFM personas within your Oracle Responsys account to:
Route customers in the orchestration flow
Analyze campaign performance
You can use the pre-built filters created for each RFM persona to target based on the RFM persona or create your own filters if you target based on the recipient’s raw RFM scores.
Figure 5: RFM is seamlessly integrated within Oracle Responsys
Oracle Responsys makes it easy for you to:
1) Identify your customers’ engagement by using innovative data science techniques to analyze billions of customer interactions to generate the RFM personas
2) Activate these personas in various Oracle Responsys applications for targeting, personalization, and analysis.
Improve your marketing results by using RFM to send more effective messages that increase customer retention and drive your ROI higher.
Learn more about RFM scoring.
For more information about audience segmentation, please look at the Five-Minute Audience Targeting Primer.
AB Bandyopadhyay is a Principal Product Manager at the Oracle Marketing Cloud and is responsible for the analytics products at Oracle Responsys and CX Audience. He joined the Oracle Responsys team in 2013 right after business school at Cornell and since then has led the evolution of analytics at Oracle Responsys