How To Make Your Data Work Harder To Reduce Garment Return Rates

How To Make Your Data Work Harder To Reduce Garment Return Rates

Sarah McVittie, Co-Founder of Dressipi recently participated in a panel on returns at Tech.Retail Week. In this post, she shares her highlights.

How To Make Your Data Work Harder To Reduce Garment Return Rates

It comes as no surprise that returns are expensive business for retailers, carrying a hefty hidden cost.

They can impact so many areas of operations, with the poor quality and sparsity of data held by many retailers exacerbating the issue. Everything from the lack of data on the customer (their preferences, who they are), the transactions data (often incomplete and housed in various different locations) and the product data (sparsely and inaccurately attributed) all impact how retailers may find it difficult to truly understand what is contributing to high return rates.

There is no silver bullet for solving this problem, as there are many reasons why items are returned, and each of these reasons will impact each retailer differently.

At Dressipi we’ve established that firstly, it is important to get the right data in place, and to then use it to understand the quickest and easiest way to reduce returns without impacting or reducing revenue or sales. For example, is it customer behavior, product/feature mix or different marketing behavior causing high return rates?

My latest whitepaper explores insight from a panel both myself and Vicky Brock (formerly of Clear Returns) participated in at the recent Tech.Retail Week event. The discussion revolved around how retailers can make their data work harder to reduce garment return rates.

The highlights from the panel (and in turn, the whitepaper) are as follows:

1) The main costs incurred by returns and how they impact the business

We identified 3 main areas of cost:

  • Costs of getting product back into circulation
  • Opportunity cost of not having available stock
  • Restocking costs

2) Highlighting the quantity and quality of data issue

As mentioned earlier in this post, the data held by retailers is rarely good enough (in both quantity and quality) to predict and reduce returns.

At Dressipi, we can do far more accurate propensity modelling on customer profile features and their tendency to buy and keep certain garments. This is due to the very detailed data we collect on every customer, and the taxonomy we created for every product category, (tagging every single product with up to 40-50 features).

3) Focus on the right metrics to drive up revenues and margins

Retailers typically focus on conversion and gross sales, but this can be misleading and won’t always lead to margin improvement without due consideration being given to return rates.

4) Analyze your data to understand your key drivers

Returns broadly fall into the following areas:

  • Customer behavior
  • Wrong products/features
  • Misleading press/marketing images

Both Vicky and I agreed that returns are always going to be a feature of the retail industry, but at their current levels, they are too expensive and unsustainable. The future of the industry relies on retailers using their data in a smarter way and taking action to gain real clarity as to the key metrics that can drive revenue growth alongside profit/margin growth. This, alongside one to one personalization that provides richer and more relevant customer experiences, will ensure that retail stays ahead of the curve.

To read the whitepaper in detail and to gain an insight into how you can reduce returns by up to 5% percentage points (including a quick returns analysis retailers can easily do), download it now.

Read our other analyses of return rate trends here: Fashion Returns: A Headache for Retailers and the Environment | 10 January 2022 Garment Return Rates Spike to Above Pre-Pandemic Levels | 03 September 2021 The Truth Behind Reduced Return Rates | 08 January 2021

Header Photo by Alexandra Gorn on Unsplash