As much as we’d love them to, most customers don’t simply drop onto your site by accident, so your success is heavily determined by the acquisition strategy of your marketers. The problem is that many retailers aren’t using key data points about their customers to analyse marketing effectiveness and are losing money as a result.
The missing jigsaw pieces
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 had better 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 your current customer segmentation is made without taking returns data into account, you don’t know what the real value of your customer is, and your segmentation and targeting decisions are immediately thrown into question. Ultimately, this could mean that your marketing effectiveness ends up well below its potential. To spend marketing budget effectively, you need to understand the returns behaviors across your customer base and in your target segments.
How returns plays a role in targeting and segmentation
Let’s look at a quick example – you’re a men’s formalwear retailer in the process of making strategic targeting decisions. You’ve identified two key market segments:
Segment A consists of 35-40 year old men who are looking to upgrade their work suits. Data from your CRM platform tells you that this demographic are big spenders who are likely to buy at the top end of your range and may buy more than one suit at a time. However, they frequently make returns, often at a rate of one or two items per purchase.
Segment B is made up of younger men – in their early 20s – who may have just started their working life and are in need of a sharp but affordable suit, usually from the lower end of your range. However, despite this lower spend, your returns data shows that they are actually much less likely to make a return. And better still, those that have a great experience with you at this stage stay loyal to your brand and make more purchases in the following years.
If you look at spend and volume, Segment A looks pretty appealing. You might be tempted to direct the lion’s share of your marketing budget at this high-spending demographic with expensive tastes. Yet they are significantly more likely to make returns, and that could drastically change their value to your business. Factoring that in, the younger, more cautious Segment B might be a strategically safer place to put your marketing budget. After all, they buy, but vitally they keep what they buy, and they have a high propensity to become repeat customers. That said, perhaps the older Segment A should still be your focus – it all depends on the numbers themselves: what percentage of items they return, how much returns cost you to process, and so on.
The point is that returns behavior is a crucial factor in assessing the profitability of these two segments, and so your effectiveness is dependent on having the visibility and data to base targeting decisions upon. If you’ve got a digital returns platform which can flag that segment A returns purchases at a higher rate, then you can make a more informed decision about the value of targeting that segment.
Return rates aren’t set in stone, but brand perceptions can be
You don’t necessarily have to accept that Segment A shoppers will always necessarily return a high proportion of their purchases. The rate at which customers return items isn’t set in stone and can be influenced. Once again, the vital question is one of visibility. If retailers are able to capture and aggregate the reasons why returns are happening, they can identify the concrete improvements to make that will lead to reduced returns.
For example, if sizing is a common issue for our Segment A shoppers, perhaps the website needs improving to clarify sizing. AI tools can aggregate how a product fits differently shaped customers to offer predictions to new shopper 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.
While return rates can be reduced in this way to great effect, improving the profitability and lifetime value of customers, there are limits to how far this approach can be taken. Making returns unnecessarily hard or treating customers with suspicion can result in more financial damage than it saves. Returns are costly, but upsetting customers is almost always more costly in the long run.
In the end, it’s all about the impact of every transaction and subsequent action on your sales data, and the returns process is a huge part of that post-purchase experience. Fitting the data jigsaw together using all of the pieces is the only way to get a clear picture. Once you’re seeing clearly, you can make better decisions that will keep you competitive and make your marketing significantly more effective in the long-term.
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