Ecommerce teams need to stop wasting time and start leveraging AI

Was 2017 the year Artificial Intelligence (AI) came of age – or simply reached its hype peak?

While the future looks exciting, is AI really making a difference to the retail experience, for customers or eCommerce teams? Most UX teams remain massively frustrated. Yes, eCommerce generates loads of data – but still very little useful insight. What is the priority? Where is the actionable insight that can be used to inform the PPC strategy or maximise the experience of mobile customers?

Before companies start playing around with AI for personalisation or predictive analytics, there is a fantastic untapped source of potential insight that should be delivering incredible value. From monetising content to tailoring the experience to the needs of Generation Z or improving signposting for the Black Friday blitz, retailers need to gain insight at speed and at scale from the existing data resources.

And that is where AI and machine learning are set to play a vital role in transforming the day to day activity of eCommerce teams.

In the Dark

eCommerce teams are awash with data – yet they still wait weeks for essential insight. With today’s manual data mining tools, there is no easy way to prioritise key areas to deliver immediate improvements in both experience and revenue; there is simply not enough time to explore and exploit the available data to achieve the improvements in UX that organisations know are on offer.

too much testing today is based on intuition or best practice or what competitors are doing

The result is that too much testing today is based on intuition or best practice or what competitors are doing. It does not reflect those areas that are likely to have an impact on conversion and/or the customer experience or the fact, for example, that one in four mobile users will abandon a site if they get an error.  The only alternative is to follow those organisations throwing massive manpower at the problem, boasting of the hundreds, even thousands of tests and iterations they run.

Operating in the dark like this makes no sense. In addition to being hugely expensive, it lacks focus and is massively inefficient.  And for the UX team knowing how much more efficient and valuable they could be – if only they had a way to unlock the data – it can only lead to greater frustration.

Speed to Insight

Can AI make a difference? There is no doubt that AI and machine learning will be absolutely vital to the success of eCommerce over the next few years but it is really important to get some perspective. Over time, AI will without doubt enable organisations to become ever more savvy and unlock the value of personalisation or predictive analytics. But it is surely essential to get the basics right first.

When a content manager has no way to quickly understand the effectiveness of a new banner or a product manager cannot identify the performance of a new offer within days, let alone hours, playing around with personalisation concepts right now is missing the point. When cart abandonment rates remain incredibly high – 66.6% for fashion and a massive 82.7% on travel sites – there are clear concerns regarding the flow through a site. Is the process too long or the path to checkout filled with too much information?

What is required first is a way to gain rapid insight from the existing data resources. And that is where AI and machine learning is really working: in contrast to manual data mining techniques that can barely scratch the surface of eCommerce data, AI can transform speed to insight to deliver tangible and immediate value.

AI enables UX Fundamentals

Whether through mining the entire checkout process and then surfacing immediately both a problem and its location, or looking at different areas of the page to identify those that don’t get clicked on very often but convert well when they do, AI can provide rapid insight into the priority areas that need to be tested. No more theoretical best practice; no more copying the competition. Essentially, AI can find the issues that affect a specific website quickly – enabling organisations to focus on resolution, resolution that can drive measurable uplift.

It is this ability to move away from the generic towards business specific that will be incredibly compelling. Organisations can use AI to prioritise specific business goals and objectives – for example understanding how to capture impatient mobile customers who take just 39 seconds to decide to leave a page - 22% less time than on a desktop or tablet.

move away from the generic towards business specific

Furthermore, it can support highly intuitive goal monitoring, providing alerts if performance has fluctuated in any way. If a new offer is not performing, the team knows within hours – rather than days – and can pull/amend immediately. Similarly with landing pages – with acquisition costs spiralling, organisations need to know not only how well dedicated landing pages are performing but also where people go next. Is it effective; or would it be better to ditch the dedicated landing page and direct the traffic to the home page?

The speed with which these insights can be presented and tested can transform not only the quality of experience but also revenue. Does the business want to focus on the check-out process to ensure it is ready for Black Friday? Gain granular insight into how visitors use every aspect of a page? Or highlight the performance of content to understand how best to capture the super fickle Google Shopper visitor? Whatever the priority, AI can rapidly provide the direction and prioritisation UX teams need to test and fine tune every aspect of the eCommerce experience in a way that is simply not possible with manual data mining tools.

Out of the Dark

In eCommerce right now, getting ahead of competitors is not about personalisation or expensive brand development: it is about faster speed to insight and doing the basics right to deliver the best online experience.  And the only way that can be achieved is by getting smarter; by rapidly exploring deep eCommerce data resources to identify areas of problem and then test. This is not about more tests, or following this week’s trend. With the right speed to insight organisations can undertake far fewer tests but only those that have already been identified as having an impact on conversion and customer experience.

And this is where AI is set to be a game changer. It may have a role to play in predictive analytics and personalisation in the future, but right now AI is taking the guesswork and assumptions out of UX; it is leveraging existing data sources to rapidly identify both areas for remediation and opportunities for revenue uplift. It is providing the UX teams with the insight required to make a real difference and call time on frustration.

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