The post-purchase reaction phase has undergone a severe change since eCommerce began.
Ten years ago, if we bought a dozen grapes and found them bad, we just sulked.
Today, when we buy bad grapes online, we can literally say something like “the grapes are too sour” and get an exchange or a refund.
But one man’s pleasure is leading to another man’s pain.
Reverse logistics, warehousing and re-packaging cost twice the MRP of a dozen grapes, if not more.
E-commerce returns in India have been around 20-27% in the past years, and as more consumers and competitors step into the world of online shopping, the size will just widen.
This is diminishing almost a quarter of the sales profits of sellers in the D2C spectrum while burdening them with huge added costs.
But that’s the cost for sustaining, they say.
In America, where the eCommerce industry has boomed greatly, this “cost of sustaining” is approx $761 Billion worth of merchandise, that retailers are expecting to be returned this year.
So when will online selling become sustainable? Probably someday. But definitely not soon any day.
The newbie eCommerce will take years to achieve perfection, if and when marketplaces set up better processes to protect the seller as much as they benefit the consumer.
But currently, it’s the experienced modern trade engine that’s keeping the profits coming.
Not just with a lower return rate, but with retail intelligence that continuously grows the trade.
Companies no longer blindly place products in stores to drive revenue, but are using data to drive B2B order management cycles. Technology such as suggested ordering, is calculating the “perfect order” for every store in the market, by identifying buying patterns and upcoming demand.
Perfect order = the right products + the right SKUs
This lowers sales returns drastically as overstocking or understocking can be eliminated, and the shelf-life of products get optimized.
Additionally, since retailers can’t afford to get rich data to know their market, this helps them build a superior in-store inventory and make effective use of shelf space.
But that’s not the only way this retail intelligence tool is driving the growth objective.
Its AL/ML models work in tandem to increase the average drop size of orders and average product lines sold by the salesman.
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