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 behaviour, product/feature mix or different marketing behaviour 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) Analyse your data to understand your key drivers
Returns broadly fall into the following areas:
- Customer behaviour
- 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 personalisation 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.
Dressipi is the global leader in fashion-specific personalisation, working with some of the world’s biggest retailers. Using a comprehensive set of Machine Learning and AI technologies alongside the largest set of product fit and style data available in the world, Dressipi enables retailers to match customers with products and experiences to influence buying behaviour at scale.
This article originally appeared on our Blog.