4 Key Takeaways from eTail Europe 2018

The ongoing success of AI in retail and marketing has not been achieved without a big, healthy dollop of irony. Artificial intelligence is winning primarily because it’s making marketing more human – increasing customer satisfaction by enabling a more contextualised, frictionless, and personalised user experience.

That was a key theme at this year’s recent eTail Europe conference, where for three days the Loop54 team thoroughly enjoyed discussing all things e-commerce and omnichannel, learning and sharing with delegates from many different walks of retail.

One of the key takeaway messages from the conference was that while retailers in general seem to have a good overall understanding of the benefits AI can bring, far fewer are sure exactly what they need to do to start enjoying these benefits.

Our CMO, Vanessa Meyer, offered her guidance on this when she sat on the following panel: Are You Ready For AI? Taking Your First Steps.

From the Loop54 stand, we chatted to some of the world’s biggest retailers and many different individuals from across the retail sector about the ways that AI can revolutionise an e-commerce business. And of course, we attended some fantastic talks over the duration of the three-day event. Here are our four key takeaways.

1. Personalisation and User Experience are Key to Revenue and Customer Retention

In day one’s lively keynote speech, Karen Pepper of Amazon Pay stated that in 2017 alone, insufficient customisation and consumer trust cost organisations 756 billion dollars, while 73% of customers tend to switch brands if the experience is poor.

Our own proprietary research tells a similar story, with A/B tests showing our personalisation feature alone increases sales from search by 7%. It’s by learning about each individual customer over time that machine learning builds a thorough profile of the customer. The information is used to predict what a customer might be interested in next, even if it’s an area the user has never interacted with before.

2. AI Transforms Segmentation into Personalisation

The issue with traditional personalisation, e.g. loyalty point schemes, is that you never reach the point of truly knowing or interacting with a customer as an individual. Instead, customers are segmented and two ultimately different people are treated the same, since they belong to the same segment.

In his great eTail talk, Machine Learning + AI Scaling = True Personalisation, Raj Balasundaram pointed out that you should ask these four questions when considering adopting AI:

• WHO is the customer?
• WHAT content would be appropriate?
• WHEN should you reach the customer?
• HOW should you reach the customer?

A key benefit of AI is that, when implemented properly, it helps provide far more extensive answers to these questions – creating a truly personalised customer experience.

With traditional software and processes, customers are categorised into buckets, segments, and personas. But this is limiting – machine learning instead drills down to the specific individual customer level and tells you where in the lifecycle that person is, where and how they are spending, and what type of person they are.

AI transforms segmentation into personalisation, since it enables you to begin looking at segments-of-one, and it’s this powerful facet e-commerce retailers have begun to latch onto.

The applications are virtually limitless, but offering bespoke discounts is a good example that's already out there.

Rather than offer a randomised 20% off coupon to some customers, AI gives you the insight that this particular person is more likely to buy this specific product if offered this particular discount. Retailers can offer fewer discounts but get better results, as discounts are accurately matched to customer characteristics and behaviour.

3. Mobile Experiences are Key to E-Commerce Success

As chairperson for day two, Tal Ofer of Keros Digital, noted in his opening presentation:

In most world regions, mobile now accounts for more than 50% of online transactions, and in-app sales dominate.

Clearly, any e-commerce business that has not executed an adequate mobile strategy is going to fall behind. And sales are also affected in a more indirect way – having a mobile-optimised site has strong SEO benefits, since it's a key Google ranking factor.

Few that work in the field of SEO will forget the results of “Mobilegeddon”. When Google released its new mobile-friendly ranking algorithm in 2015, designed to boost the positioning of mobile-friendly pages, there were some dramatic wins and losses. While sites such as foreignaffairs.com achieved a gain in visibility of over 700%, others, for example walmartstores.com, dropped by over 30%.  

4. AI lets Marketeers Spend More Time on Core Activities

Going back to Raj Balasundaram’s talk, he said the following of the link between AI and marketing:

A true AI application should be invisible, working in the background, looking after you, not you looking after it… With AI, you can fine-tune business goals instead of trying to tackle algorithms – that’s AI's strength.

And this notion seems to have strong support within the marketing industry – in a Forrester survey, 82% of respondents agreed or strongly agreed that AI will allow marketing staff to focus on value-generation, as it automates workflows. Similarly, 79% agreed or strongly agreed AI will change the role of marketing toward more strategic work.

When AI is embedded in marketing, marketeers are freed up to spend more time using it to unify and improve customer experiences instead of having to worry about time-consuming and complex tasks such as analytical model development, data integration, and algorithmic optimisation.

Rather than leave marketeers jobless, AI actually lets them focus more on the core pillars of the discipline – as trends in customer behaviour are automatically discovered and presented in real-time, professionals can then use the data to drive new content and product innovation.

Conclusion

The ultimate goal of AI in e-commerce is to provide visitors with the best possible experience. This, in turn, drives brand loyalty and boosts revenue and sales. It helps provide the best possible answers to Raj Balasundaram’s four customer questions regarding who, what, when, and how.

For example, by mapping the relationship between products, AI lets you automatically generate a complementary list of relevant results - results that would have never been found through simple text-matching search. Related results promote product discovery and help to increase average order value.

If you'd like to start evaluating your search performance and see how machine learning can help improve customer experience, our site-search visualisation dashboard is a great place to start. It shows you key KPIs and metrics, such as sessions with/without search, revenue with/without search, top terms searched, percentage of search exits, and data filtering by traffic source and device. Get started today by downloading your free copy.