Do you know what the difference is?
There’s no shortage of advice aimed at retailers and brands trying to find a solution to reduce basket abandonment in online shops. Much of it focuses its effort on helping brands to understand what’s happening, answering questions on when people abandoned their shop, where they went and how long they were there in the first place.
In response, retailers find themselves changing their websites and brands and retailers end up spending marketing resources on retargeting campaigns, and sending out a lot of vouchers trying to get their one-time browsers back as buyers. Sometimes, this is all very well. Proactive changes to websites and remarketing can bring shoppers back. But by only asking what is happening to cause basket abandonment, brands are really only making smaller, tactical changes that will never truly solve the issue. What they really need to do is understand why basket abandonment happens, so they can make strategic decisions to address the problem in a more holistic way.
What 'why' means
The reason ‘why’ matters is that it answers questions intrinsically linked to shopper behaviour, rather than the technical faults or customer preferences that can disrupt shopping. In other words, it’s all very well knowing that customers have abandoned baskets because they don’t like the delivery charges or payment methods accepted – but addressing that isn’t going to target the wider cause of basket abandonment caused by habit, behaviour and lifestyle. Over the course of the past year, dunnhumby Beyond has analysed thousands of customer journeys – investigating the number of sessions and time period in which a shop is completed – to understand more about this issue. What this revealed is that shopping style is a huge factor in basket abandonment and a shopper’s likelihood of leaving mid-session. Out of this came four journey types (Considered, Distracted, Focussed, Leisurely). And illuminating stats, with 50% of Considered and Distracted shoppers breaking their journey. That’s useful information for brands on a top level, but where it becomes really valuable is in the insight that they’re leaving to compare prices elsewhere, heading to search and competitor websites. Not only is this insight showing what happened (broken journey, potential abandoned shop), it’s also telling the brand or retailer exactly why it happened. From this, brands can begin to make changes that address deeper issues with online basket abandonment.
How to get to why
Despite the clear value of moving from what happened to why, the challenging thing for brands can often be getting started on that journey. eCommerce generates huge amounts of data from countless different sources. Useful insight rests on bringing that together in an organised fashion, seeing things contextually and making sense of it. It’s also usually a very time-consuming process – particularly without the right partner on side who has the capability to work with vast data lakes. Increasingly, brands that are getting useful ‘why’ information out of their data are doing so through turning it into personas that flesh out the shoppers that matter most to them. As well as data about shopping patterns, these personas include lifestyle information, habits and preferences – all of which enables brands to understand even more of the behavioural traits and habits that sit behind basket abandonment. They can use these personas to answer questions that are concerning them about basket abandonment (or any other eCommerce problem, for that matter). And to hone their thinking on what developments and solutions might have the most demonstrable impact on the issue.
From what to why
Basket abandonment issues are growing as things stand. And retailers are missing out on vast sums of money that could either turn their fortunes or substantially grow their business. This is why it pays to go a bit deeper with data, raising levels of shopper understanding and the real factors that will drive people towards purchase completion. But they’ll never do that if they can only answer what…
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