Sorting out troublesome customers is one of the major benefits that big data offers businesses, a profitable example lies in reducing returns to online stores.
One of the banes of online retail is dealing with returns, the industry pioneers overcame objections to shopping over the web through no-questions-asked returns policies that’s trained customers into expecting they can send items back regardless of the reason.
The Frankfurt School of Finance and Management’s Christian Schulze surveyed nearly six million internet transactions and found returns are effectively costing online retailers half their profits, as The Economist reports.
Leaving that sort of money on the table is painful for any business and online retailers are trying to find ways to reduce those return costs by sacking their customers;
But this risks a backlash: rejected shoppers are likely to rush to the newspapers or social media to complain—and their gripes may turn other, more profitable customers against the firm.
Much of this comes down to Pareto’s Law, that 80% of your problems will come from just 20% of customers, and a key imperative in business is to get the troublesome, high maintenance customers buying from your competitors without being too obvious.
Identifying those troublesome customers is where Big Data comes into play, coupled with intelligent analytic tools businesses are able to identify who is more likely to return a product or dispute a bill before the sale is made.
As the Wall Street Journal reports many online retailers are exploring ways they can reduce the return rates using Big Data and analytics.
By giving buyers access to their purchasing history stores are able to suggest when a customer is buying something that isn’t appropriate or the wrong size.
The WSJ cites fashion retailer Rue La La, which lost $5 million in returns last year, as an example.
For instance, a customer who has continuously bought the same brand of dress shirts in both a small and a medium might see a note pop up saying: “Are you sure you want to order the small? The last five times you ordered both sizes, you only kept the medium,” Chief Executive Steve Davis said.
Another tactic for retailers is to discourage frequent returners from buying high margin goods through targeted vouchers and offers. One point the WSJ article makes is how differential pricing is going to be applied – if you regularly return goods then expect not to be offered the best discounts when you visit the retailer’s website.
Many returns though are the result of genuinely dissatisfied clients and this is where improving customer service kicks in, the WSJ describes how some retailers are now providing video tutorials for their products and increasingly smarter customer service can be used to avoid returns.
With the increased sophistication of customer analytics and support tools, we’ll see online retailers squeeze more profit out of their businesses as well as look after their most profitable clients.
The problem for ‘bricks and mortar’ retailers not deploying new technologies is they won’t have the tools to compete with their savvier online rivals.
A good example of legacy managers struggling in the face of chronic under investment are Australian retailers and this week the Myer department store chain had to shut down its online outlet after the system collapsed.
There is no timeline on when Myer’s website will be back up. It’s a tough time for those retailers that haven’t invested in modern system and an even tougher time for companies with legacy managers like those at Myers.
The use of big data in analysing shopping behaviours is one area where well managed retailers will out perform their poorer rivals, it’s hard to see how companies like Myer will survive in the modern era of business.