Basket abandonment costs UK retailers billions of pounds each year – attracting a plethora of solutions that have hitherto struggled to dent the problem. In 2018, Barclaycard estimated the cost at £18bn and the figure is only likely to have increased in the last three years.
This year, Statista found that in the UK, basket abandonment rates are as high as 85% for shoppers using their mobiles and 77% for consumers using their tablets and computers.
This article will examine how as the big browser companies axe third-party cookies out of privacy concerns, retail sites and online brands need to employ new approaches to basket abandonment. The most effective is the use of behavioural analytics, enabling personalised real-time interventions during consumer website visits. Driven by AI, this approach uses first-party cookie data that remains compliant with privacy legislation.
Failing to recognise reasons behind high abandonment rates
Until the advent of behavioural analytics, however, the effort to reduce horrendous abandonment statistics has focused on reducing friction within the customer journey. Researchers have highlighted the stages where consumers lose interest, such as requirements for account-creation or form-filling, or revelation of inflexible returns policies or delivery methods. Pages that are slow to load, repeated requests to enter payment data or perception of high shipping costs can also be significant deterrents.
This has helped remove many pain points, but a more fundamental problem remains, which is that retailers continue to use one-size-fits-all solutions that treat all shoppers alike. These solutions fail to recognise the differences between consumers and are unable to identify the triggers most likely to prompt individuals to go through with a purchase. They use undifferentiated offers that either fail because they are irrelevant or waste discounts on shoppers who would have made a purchase anyway.
Segment and interact in real-time
Almost all online retailers would be better advised to integrate behavioural analytics with their website systems to segment consumers accurately and interact with them in real-time. Using AI to extract insights from first-party cookie data generated solely from activity on its own website, a brand can build a picture of individual consumers within 50 milliseconds of arrival. Brands can use hundreds of attributes covering many aspects of dwell-time, speed of movement and page views, as well as interest in specific products. Historical data about what customers did on previous visits helps create a detailed pattern of behaviour and preferences.
Using behavioural analytics, a telecoms provider will know the right price-points that work for a previous customer who is exploring broadband plans and data bundles, enabling an effective intervention with the right level of offer. Having built a baseline of what the average conversion journey looks like, retailers can identify factors that indicate a specific web visitor will not follow through and make a purchase. Instead of deploying discounts and offers right away, retailers can hold them back for the right moment. For example, a site’s first-time visitor who has two items in their basket but is moving the mouse towards closing the page might be offered an instant discount to trigger an immediate purchase.
Unlike third-party data from sources that are often obscure and have opaque processes of consent, behavioural analytics of the type discussed here rely solely on a website’s own first-party cookies. These do not track visitors across the web and are transparent about consent. In return consumers gain the outcome they want more quickly and easily.
It is not a technique of deception, misleading consumers into making choices that are not choices at all. Nor does it make crass assumptions based on age or other profile information. It is personalised, respects multiple layers of identity and is compliant with data protection legislation such as GDPR.
Personalisation and preparation
Personalisation is vital, but another underestimated element in the reduction of basket abandonment is preparation – moving previous customers or website visitors along the purchase funnel before they arrive on the website. This requires the ability to use the consumer’s current behaviour and past history (if available) to accurately identify a persuasive alternative. This must be combined with the ability to put the recommendation in front of consumers, either on a website or multichannel, via email and SMS.
Communication with the user must have maximum relevance and be set at the right level of frequency to avoid creepiness or annoyance. Of course, some areas of online commerce lend themselves very readily to this kind of marketing. Vehicle dealerships, for example, have registration anniversaries and MOT dates they can use in emails and SMS messages without seeming intrusive. What makes this work is knowing the customer from their previous visits and having the ability to encourage them back without handing over too much margin.
The role of first-party cookie data
Behavioural analytics will become essential for personalisation and effective intervention once Google follows Apple and Firefox in banning third-party cookies out of concerns for consumer privacy. Third-party cookie data will no longer be available. In the meantime, continued use of one-size-fits-all solutions will undermine the ability of brands and retailers to reduce basket abandonment through failure to segment accurately and interact in real-time.
Organisations using these solutions will remain unable to use genuinely personalised offers and promotions that make the difference. Rather than annoying consumers with pop-ups and discounts for products they do not want, the deployment of behavioural analytics is far more persuasive and effective – especially for consumers close to the final stages of their purchasing journey.