In an increasingly fluid retail marketplace, where purchasing continues to shift from the physical to the digital, companies both big and small must look for new ways to differentiate their brands, build loyalty and keep their customers coming back for more.

Whether through personalisation, same-day delivery, or digital payment solutions such as Apple Pay, retailers are turning to new technologies as the source of this differentiation.

At the forefront of this trend is a focus on Customer Experience, with brands increasingly realising that it can be just as beneficial to change the way consumers experience a brand as it can be to change a product itself.

Unfortunately for retailers, however, many of the experiential factors that they must use to differentiate their brands are extremely subtle. As a result, it can be difficult to identify those areas of the customer’s experience that are points of frustration or cause for cart abandonment. To address this challenge, retailers are once again turning to technology – this time in the form of artificial intelligence (AI).

Research suggests that as many as 80 percent of businesses incorporate some form of AI into their organisations, with customer service being the most popular application. Yet in retail, we find that many are basic examples of AI and machine learning – either simple personalisation scripts or basic customer service chatbots. And looking ahead, retailers’ plans to incorporate AI do not yet extend much beyond robotic chat-boxes in their ecommerce stores.

Yet there are potentially hundreds of examples of retail tools and applications where AI could be used to improve the Customer Experience. Here are three such applications that we expect to come to the fore in 2018:

1. AI-Powered Personalisation

AI-Powered Personalisation involves providing visitors with specific content and offers based on the purchasing behaviours of previous customers. Sounds familiar? It’s true that these principles of personalisation have long been a staple of the ecommerce industry, but AI takes this toolset to a new level. By analysing a huge wealth of customer data, machine learning can tailor offers based on everything from weather patterns to current user moods.

Such advanced personalisation technologies already exist, but very few retailers are embracing them. In part, this is due to the silos and lack of connectivity that exists across a brand’s data sets, with the average marketing department using 12 different tools to personalise its content.

To effectively personalise content and genuinely improve customer experiences, AI requires as much information as possible about customers’ existing interactions with a brand, but retailers are faced with disjointed data sets, that are difficult for AI systems to analyse.

The key to successful personalisation is seamlessness, where websites and analytics work together to inform AI tools that will create actionable insights for developing more relevant ecommerce campaigns.

2. Behavioural analysis

While there have been plenty of retail surveys asking consumers for their views on the optimum design and flow of ecommerce sites, the reality is that such responses cannot offer the same level of insights that behavioural web data provides. Only by monitoring how consumers are actually browsing a site – whether through mouse tracking, click paths or behavioural analytics – can retailers build a true understanding of what their customers are looking for.

Such behavioural data provides invaluable insights, but it requires significant time and effort to review and analyse. This is where artificial intelligence comes in. By crunching the massive volumes of behavioural data collected across a site, AI can find the subtle points of frustration so they can be removed from or improved within the user experience.

3. Identifying hidden consumer trends/patterns

Tracking consumer behaviours through customer log-ins allows you to open up previously unseen insights. For example, if you own a pizzeria and you require customers to log in each time they make a purchase, with the right AI program, you can start to analyse predictive patterns, suggesting deals, combos and preferred toppings, ultimately resulting in a faster delivery time. By speeding up the process of ordering – and the potential delivery time – AI helps to provide a more seamless Customer Experience. While this may seem like a fairly minor improvement, in reality, these subtle AI-driven changes can be the deciding factor in whether a consumer chooses to return to your ecommerce site in future, or try a competitor instead.

As an example of such competitive advantage, consider the Arcadia Group – a collection of nine popular fashion brands. By incorporating an intelligent, AI-driven cross-selling and upselling system, Arcadia Group increased order value by 67 percent – a significant increase based on only a small change to the company’s existing processes.

While AI will never be a perfect predictor for human behaviour, it can help us to make seemingly minor – yet high impact – improvements in Customer Experience, which can ultimately mean the difference between making a sale or not. To start on this journey, retailers must first ensure that they are collecting customer data in a meaningful and joined-up way.

At the end of the day, an AI system can only be as effective as the data that it works from, it is down to retailers to build the relationships and data sets needed to feed this machine.

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