As much as retailers and brands would love them to, most customers don’t simply drop onto their site by accident. Given this, success is heavily determined by the acquisition strategy of their marketers. The problem is that many aren’t using key data points about their customers to analyze marketing effectiveness and are losing money as a result – and one key area of this is in returns.
Unfortunately, marketers are often only looking at half the picture when they report, focusing solely on how much their budget can achieve in sales without accounting for the comprehensive cost of that sale. For example, if a customer purchases six items as a result of a campaign, but returns four, that returns factor needs to make its way into the campaign reporting. And the wider data set about returns should go well beyond campaign reports and into strategic decision making.
If a retail marketer’s current customer segmentation is made without taking ecommerce returns data into account, then it’s hard to know what the real value of customers are, and segmentation and targeting decisions are thrown into question. Ultimately, this could mean that marketing effectiveness ends up well below its potential. To spend budget effectively, retail marketers need to understand the returns behaviors across their customer base and in their target segments.
How do ecommerce returns play a key role in retail marketers’ targeting and segmentation, as well as in influencing brand perception?
Following is an example to consider:
A men’s formalwear retailer is in the process of making strategic targeting decisions, and it has identified two key market segments:
Segment A consists of 35-40-year-old men who are looking to upgrade their work suits. Data from the retailer’s CRM platform tells it that members of this demographic are big spenders who are likely to buy at the top end of their range and may buy more than one suit at a time. However, they frequently make ecommerce returns, often at a rate of one or two items per purchase.
Segment B is comprised of younger men in their early 20s who may have just started working and need a sharp but affordable suit, usually from the lower end of the retailer’s product range. However, despite this lower spend, the returns data shows these consumers are actually much less likely to make a return. And better still, those that have a great experience with the business at this stage stay loyal to the brand and make more purchases in the subsequent years.
From a spend and volume perspective, Segment A looks pretty appealing. The retailer may be tempted to direct the bulk of its marketing budget at this high-spending demographic with expensive tastes. However, they are significantly more likely to make ecommerce returns, and that could drastically change their value to the business. Considering that, the younger, more cautious Segment B might be a strategically safer place to put the marketing budget. They buy, and, importantly, they keep what they buy, and they have a high propensity to become repeat customers.
The key point is that returns behavior is a crucial factor in assessing the profitability of these two segments, and so effectiveness is dependent upon having the visibility and data on which to base targeting decisions. With a digital returns platform that can flag that Segment A returns purchases at a higher rate, then the retail marketer can make a more informed decision about the value of targeting that segment.
While a retailer can work to influence return rates, it can be much harder to change brand perceptions. With visibility into why returns are happening, a merchant can identify specific improvements to make that will lead to reduced returns and, possibly more importantly, positive feelings about the brand.
For example, if sizing is a common issue for the Segment A shoppers, perhaps the retailer’s website needs improving to clarify sizing. AI tools can aggregate how a product fits differently shaped customers to offer predictions to new shoppers about which size will suit them best. If shoppers are returning specific items time after time, there may be design or manufacturing defects which can be addressed at the root.
In the end, it’s all about the impact of every transaction and subsequent action on sales data, and the ecommerce returns process is a significant part of that post-purchase experience. Fitting all of the data pieces together is the only way to get a complete picture. With comprehensive insight, retailers can make better decisions that will help keep them competitive, make the marketing significantly more effective and drive positive brand perception.
Dan Nevin is Chief Revenue Officer, Global Retail for Doddle