The Power of Retail Analytics Programs
What’s trending in the south? What shares a market basket with ski pants? Who are our best customers, and how do their buying patterns differ?
We live in an age of retail analytics where retailers are amassing everything – from POS data to social media commentary to mobile app behaviors – to gain better insights into customers and predict what they may do next.
Four of the top ten 2015 technology investments and three of the top ten strategies cited by retailers in the 2015 RIS News Technology Trends Study involve using analytics to answer questions like these.
But there’s one conundrum retailers can’t seem to crack: Why do customers return products? Cue the crickets.
Apart from a few vague reason codes, few retailers really know why customers return goods. And despite analyzing every aspect of forward merchandising, returns garners surprisingly little retailer attention.
Gartner describes a maturity cycle for analytics investment:
- Descriptive analytics – what happened?
- Diagnostic analytics – why did it happen?
- Predictive analytics – what will happen?
- Prescriptive analytics – how can we make it happen?
But most retailers, even at the tier-one level, are not even at the first step when it comes to returns. If their returns analysis kept up with the rest of their retail analytics program, they would see patterns in their returns that explain why products were coming back. Imagine how much more effectively retailers could make immediate and long-term changes to their merchandise if they knew why certain products failed?
With retailers of every stripe ignoring return analytics as return rates increase, diverting even a small piece of analytics investment to returns can give retailers a leg up on their competitors.
Retailers who analyze their returns learn things others can’t about products and customers.
That contributes to a more satisfying shopping experience: customers come back, but the products don’t. That’s an analytics payoff anyone would envy.