How real time data adds to your RFM segmentation
RFM is one of the tried and tested methods of calculating customer value to segment an email list.
What is RFM?
- Recency - How recently did the customer purchase?
- Frequency - How often do they purchase?
- Monetary Value - How much do they spend?
Traditional RFM assumes that the previous action of a customer, in most cases transactional behavior, is a handy indicator of what they will do with regard to future purchases. Segmenting lists was essentially the job of a historian.
Adding real-time data to RFM
Naturally, this isn't the best way of judging what a customer will do. Traditional RFM follows the logic that the longer the gap between the first purchase and the second purchase, the less valuable the customer. So a long-lost customer who is on the cusp of buying - for example, they view a product multiple times in the last 48 hours but have not purchased for 6 months - is not going to be targeted by any email campaigns. You need RTD (real-time data) too.
Fresh Relevance is your Met Office for capturing real-time data and utilizing it harmoniously with your existing segmentation strategies. Don't be like Michael Fish in this famous clip from 15th October 1987, when he ignored real-time data from a caller and relied on historic data, and failed to spot a hurricane!
Real-time data adds a new dimension to RFM. You can immediately capture "leading indicators" such as browsing and carting and include them in your calculation of customer value. Marketers can segment more accurately with information that is being caught in the present.
Using a historical approach to weather isn't the most accurate way to find today's temperature or wind speed - you need a thermometer or to look out of the window. Likewise, using historical data to predict future purchases isn't going to give you optimal conversion rates. Add real-time Triggered Analytics to improve your targeting and make all your email marketing more effective.
Download the ebook for more ways to target customers with real-time data: