Just back from my last customer journey mapping workshop for this year – a big shout out to all workshop participants who trusted the process, embraced ambiguity and learned how-to journey map the end-to-end customer experience and improve their customer-centric problem solving skills.
On the topic of customer segmentation and which customers to start mapping for, we briefly discussed focusing efforts on high value customers using Customer Lifetime Value (CLV). CLV is the prediction of value a business will derive over the lifetime of the customer relationship.
Using a CLV metric to segment high value customers focuses attention on the journeys that will deliver the best return on resources and effort for the most profitable customer relationships. In my experience, when organisations endeavour to map every customer journey or every segment they end up ‘boiling the ocean’; too much data, too many fixes, too many priories and not enough resources.
I am a big fan of Dr Peter Fader, professor of marketing and co-founder of the Customer Analytics Initiative at Wharton University of Pennsylvania, and author of Customer Centricity: Focus on the Right Customers for Strategic Advantage.
In this article (link below), he says “If we can figure out the most valuable customers from a future standpoint, not historical value or profitability, come up with ways to enhance that value, extract value for shareholders, then use that understanding to find more customers like them, we can potentially make far more profits than the typical ‘one-size-fits-all’ approach that comes from trying to boost overall satisfaction ratings and keeping them all happy.”
In discussions with my consulting clients, it’s getting agreement on the metric values to calculate Customer Lifetime Value that always seems to be the hurdle in using CLV for customer segmentation purposes. Fader bases CLV calculations on three metrics: recency, frequency and monetary value. However, a brief search on Google showed that there’s many metrics and methods for calculation.
If getting agreement is the problem, there’s never a better time to start collaborating cross-functionally; getting finance and marketing working together to agree on metrics, calculation method and customer data – building the data-drive smarts to ensure prioritisation of resources and effort are focused on improving customer experiences that will deliver the greatest return on investment.