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 in order to provide a contextually relevant experience, with the goal of increasing retention and ROI?
And once you’ve identified these segments, how can you quickly take action on these insights?
Oracle Responsys recently introduced a new feature called RFM that will help you address these challenges. RFM stands for Recency, Frequency and Monetary analysis, and 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 RFM Dashboard
E-commerce 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 Oracle Responsys 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 the individual needs and requirements of your corresponding customers. For example, the Champions, who are your best customers with the strongest rate of high value engagement, may need to be treated with exclusive deals and privileges that make them feel special, while 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
RFM personas are generated in a three-step process:
Each customer is scored based on recency, frequency and monetary value of his/her engagement over a look-back period.
The customers are ranked and distributed in quintiles to calculate the R, F and M scores for each signal. Subsequently a weighted algorithm is applied to derive a composite R and F score.
The composite scores are used 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
Or to 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 need to 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, and then
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 help increase customer retention and drive your ROI higher.
Personalization and relevancy are key tools to marketers looking to engage and connect with your audience. You have to send the right message at the right time in the right channel. This 5-Minute Audience Targeting Primer can help.