We have often seen companies having a better handle on store returns, but struggle with respect to online returns. For one of our clients, while store returns remained flat at 4-5%, online returns hovered around 30-35%, going even as high as 40%.
As one of the ways to address this, we developed an algorithm to predict the online return probability, generating a real-time score during online transactions.
This score reflected the propensity of return incidence based on historical trends in transactional attributes and customer demographic parameters.
Called as Returns Propensity Score or RPS, this score was used to implement differential returns policy, sales strategies, return control measures as well as loyalty program refinements.
This multi-variate scoring algorithm was integrated with machine learning capabilities to ensure any shift in customer behaviour is incorporated in future scores.
RPS was also utilised in drafting return policy with proactive measures to curb return frequency, besides refining loyalty programs and streamlining inventory and supply chain management.
About the author:
Ashwin Malik Meshram is the Managing Director for a US-headquartered predictive analytics and artificial intelligence firm Spinnaker Analytics. He has worked closely with CEOs, CMOs and CFOs of leading corporations in North America and Europe, on diverse initiatives ranging from business transformation to machine learning. He is an IIT Bombay alumnus, and currently splits his time between Boston and Mumbai.
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