The retail market has been growing steadily over years with increasing vibrancy and dynamism. Due to high competition in the market today, almost every retailer (online and offline) has a policy around products return and exchange to ease out the purchase and enhance customer experience. This gives the customer a free hand to buy any product he or she likes and return or exchange it later for a reason. Both, the customer and the retailer benefit from this policy, as it allows customers to buy anything they like without much thought; on the other hand, it gives high volume of sales to retailers. A return policy is however not a win-win situation for retailers. They have to bear huge loss on product returns that eat up their profit margin. Every customer expects a return/exchange option and the retailer has to essentially offer this to survive in the market.
Retail returns get worse if any deceptive interest is involved. Fraud return is the bane in the return process. This puts a substantial dent on the retailer’s margin. Generally, retailers are ready to receive genuine returns but they are concerned about fraud returns.
Fraud returns can be executed in multiple ways, for instance, returning a product damaged at the customer’s end, returning used products, or replacing authentic products with duplicate/stolen products and initiating return. Survey reports indicate that more than 90% of retailers have experienced fraud returns in the past years, costing them nearly $5 million for every $1 billion in revenue.
Retailers try to counter fraud returns with strategies like accepting return on valid invoice only, specifying a time window for return of damaged products, attaching a non-reusable tag with fashion products, among others. Reimbursements are given to customers through store credits to be redeemed for a purchase at the store. However, all such measures do not help retailers effectively deal with the problem of fraud claims.
How can analytics help
Fraud returns can be dropped significantly with analytics-enabled solutions. Analytics can be leveraged to
- Identify customers with frequent returns record and impose stringent return conditions
- Identify customers with a history of fraud returns and tag tighter return conditions
- Identify frequently returned products and make relevant return policy for these
- Identify frequently returned products and dealers associated with these products and take required action
An apt analytics enabled solution (predictive model) for fraud returns will indicate each return as Approve, Decline or Alert (Figure 1 shows the flow of the return process). Here, ‘Approve’ indicates negligible chances of fraud associated with the return. ‘Decline’ indicates a fraud return and ‘Alert’ indicates moderate chances of fraud. In the case of Alert, store managers verify the claim, product, receipt and the concerned dealer.