Lowering returns quotas: breakthrough via causal analysis.

By 2016-09-14T21:54:20+02:000000002030201609 February 1st, 2017 Allgemein

How leading returns researcher Professor Walsh discovered effective levers to lower the returns quota via causal analysis.

The extremely high returns rates in e-commerce are an unsolved problem – a problem that represents a challenge to managers due to the tight margins in online retailing. Traditional BI analysis results very often turned out to be misleading in the past. The reason is known as “spurious correlation”.

Success Drivers  therefore identified, for example, that horizontal striped clothing was sent back less often. A classic spurious correlation: horizontal stripes are found particularly on cheap products, for which the returns rate is generally lower. Under otherwise similar circumstances, horizontal striped products are sent back MORE not less. This is a finding that can only be discovered via causal analysis.

The returns whisperer

For this very reason, Professor Walsh from the University of Jena has been involved in causal analysis with his team of researchers. His various studies provide many new insights into what drives product returns.

In a study, which appeared in the Harvard Business Manager, he and his co-invetigator found that the use of penalties and obstacles does not pay off in the long run. Their study showed a clear strategic path: return rates can be reduced by providing customers with relevant product-related information and making the buying process as hassle-free as possible. Virtual try-ons, avatars and specific product evaluations from peers are very helpful in this respect. The latter is a factor with very high ROI as the investment is low.

Self learning causal analytics covers up

With the current study the research team around Gianfranco Walsh were also able to identify a key driver of customer returns. Using consumer survey data and causal analysis, they found that the customer-related reputation of an online retailer alone has a significant effect on the return rates.

This reputation is determined by the following components: customer-orientation, being a good employer, being financially sound, perceived product quality and social responsibility. Although this measure may seem very abstract, the new causal model enables very precise measurement of the effect of bad press and to quantify the effects on the margin.

“There are also many company-specific returns levers. They may be identified most successfully via self-learning causal analysis – as enabled by the NEUSREL software”, Walsh recommends.

p.s. More on NEUSREL and Universal Structural Modeling here.