Rethinking Customer Loyalty in an Omni-channel World
Customer loyalty has long been measured using the RFM (recency, frequency, monetary value) framework, where spending and purchase frequency serve as proxies for loyalty. However, in today's dynamic retail landscape, characterised by omnichannel experiences and expanded customer analytics, it is essential to redefine loyalty. This article explores the limitations of traditional loyalty metrics, challenges posed by returns, the importance of customer profitability, and the potential to analyse sales and returns through an RFM lens to gain a more comprehensive understanding of customer behaviour.
The Pitfalls of Traditional Methodolgy
While spend and frequency have been widely used to gauge customer loyalty, they fail to account for factors like returns. Consider two customers with a net spend of $100 each, but one has made purchases worth $100 while the other had purchases of $150 and returns amounting to $50. Traditional metrics would classify both customers as equally loyal, but the customer with fewer returns is significantly more valuable. Returns incur shipping fees, restocking, and potential markdowns, amounting to as much as 30% of the retail price.
Unveiling Customer Profitability
A shift towards evaluating customer profitability is imperative to assess customer loyalty honestly. By analysing RFM metrics for both sales and returns, a more holistic view of customer behaviour emerges. This comprehensive approach enables businesses to take targeted actions, incentivise customers, and manage their shopping and returns behaviour more effectively. By considering both sides of the equation, companies can identify the most valuable customers and tailor their strategies accordingly.
The Power of Personalisation
Personalisation in customer loyalty extends beyond customised offers. It encompasses tailored engagement and treatment, including returns policies based on individual profiles. Businesses can craft returns policies that align with customer preferences and behaviours by leveraging customer data. For instance, customers with a history of low returns may be granted more flexible return options, enhancing their overall experience and strengthening loyalty. Personalised returns policies foster a sense of understanding, ensuring customers feel valued and acknowledged, which nurtures long-term loyalty.
Conclusion
In the ever-evolving retail landscape, traditional RFM metrics fail to capture the complete picture of customer loyalty. Relying solely on spend and frequency fails to account for the impact of returns and customer profitability. By embracing a more comprehensive approach that analyses sales and returns, businesses can gain deeper insights into customer behaviour and make informed decisions to nurture loyalty. Furthermore, personalisation, encompassing tailored engagement and returns policies, adds a crucial dimension to building lasting customer relationships in the dynamic retail world.