Customer behavior analytics is about understanding how your customers act across each channel and interaction point — digital or non-digital – and what influences their actions. It gives you a way to implement what I like to call, the “four rights” – talk to the right audience, through the right channel, with the right message, at the right time.
Understanding customer behavior can help your organization in more ways than you think. The entire customer lifecycle can be optimized using behavior analytics:
- Customer acquisition: Marketing will target high-value customer segments identified by behavior analytics and study behavior patterns to determine the best potential offers.
- Customer engagement: Behavior patterns will be used to generate personalized next-best, cross-sell and up-sell offers, while behavioral customer segmentation will be used for more general customer marketing offers.
- Customer retention: Behavior patterns will be used to detect possible customer churn and generate next-best retention offers.
The value of customer behavior analytics can be measured in several key metrics:
- Increased customer acquisition and conversion rates
- Lower cost of acquisition
- Larger average sale on initial purchases
- Increased number of purchases per customer
- Larger order sizes on repeat purchases
- Lower cost per sale
- Increased lifetime value of customers
- Higher customer retention/Reduced churn
- Lower cost of service
These are all metrics every organization strives to improve and can be dramatically impacted by customer behavior analytics.
There is a New Sheriff in Town
Until now, the Recency, Frequency and Monetary value (RFM) model was the mostly commonly used approach to modeling customer behavior. RFM assumes the following:
- Customers who have spent at a business recently are more likely than others to spend again
- Customers who spend more frequently at a business are more likely than others to spend again
- Customers who have spent a higher amount at a business are more likely than others to spend again
Historically companies only had purchase data at their disposal. This made RFM the only way they could model customer behavior. However, the onslaught of data from the digital age and the variety of new interaction channels is escalating the demise of a RFM.
A holistic customer behavior model that analyzes all interactions over time along with outcomes is not only more accurate and predictable but is also a necessity to compete in today’s digital world.
