US tech firm RichRelevance says hyper-personalisation in shopping is the next big step for retailers as they look to create an educational online experience rather than just making it more convenient to buy things
L'Oreal's virtual make-up try-on tool is an example of how the brand is trying to bring hyper-personalisation into online shopping (Credit: L'Oreal Paris)
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Retailers like Amazon have used the past decade to make buying things online more convenient through AI-based recommendations – but now traditional brands are using hyper-personalisation to make the shopping experience truly unique to the individual. Dan Robinson reports
When it comes to retail, Amazon is the Holy Grail to which others aspire. But trying to beat the tech giant at algorithms is equal to Bing attempting to trump Google’s search engine.
Sir Charlie Mayfield, chairman of the John Lewis Partnership, conceded as much when discussing the industry’s future at an event last week.
But while iconic brands such as the one he leads may lose in price wars with the beast sculpted by Jeff Bezos just over two decades ago, they can triumph in heritage and reputation – and a new AI-based concept known as “hyper-personalisation” could even give them a technological upper hand.
Previous attempts to provide a customised shopping experience have given customers recommendations based on purchase history and what similar people have bought, but this is now all about the individual.
Rather than dividing customers into segments, they are treated uniquely by developing an understanding of why they buy things and not just what they take home – with a hyper-personalised website also becoming the web’s answer to the traditional department store shopping destination.
From today, US tech firm RichRelevance has introduced new features that harness deep learning AI tech to its Personalization Cloud software that help retailers do just this.
Chief marketing officer Michael Ni tells Compelo: “It becomes a way to not just use algorithms to remove ‘bad friction’ like Amazon does to make buying things easier, because that company has a 19% margin it reinvests into low prices and new technology.
“You aren’t going to beat it at its own game but iconic brands have history and heritage. They need to find new ways to reassert themselves and leverage what makes them special to their customers.
“This is where hyper-personalisation is really important in helping to drive retailers and brands differentiate between convenience and a good experience.
“Convenience is for Amazon, but time well spent – where it’s really worth taking a step back and enjoying the shopping experience – is the personalisation of tomorrow.”
What is hyper-personalisation in shopping?
For decades, people have been walking into a John Lewis or Barneys New York department store, wandering over to a particular clothing or beauty section and spoken to a customer assistant as they try on different styles and brands.
They’ll take feedback on board, learn a little more about a product and then buy the most suitable product at the check-out.
But with hyper-personalisation, a similar experience could be replicated on a customer’s computer, smartphone or tablet device with virtual try-on and conversational tools.
Michael explains: “We can create that department store consultation experience, where a customer goes to be a little bit more pampered, but online.
“There will be multiple conversation tools, such as a guided Q&A for skincare. You’ll be asked what your skincare concerns are and which products you’ve used today, then get product recommendations.
“We can personalise the entire journey from that point on and the placement around the website will be specific to your type of concerns and tastes.
“The products along the path will be more focused on you. If you don’t buy something, you might follow up afterwards.
“It builds a larger profile as you buy things, which suggests more products, as well as content recommendations like a video on how to apply a certain type of skincare.”
In such a scenario, Michael says this provides an opportunity for a brand to become a purveyor for how the consumer thinks about their skincare, while helping to secure a return visit that is “incredibly important” to commerce businesses.
How brands have used personalisation until now
RichRelevance, which has its headquarters in San Francisco, was set up in 2006 by a former Amazon senior executive who helped to put its personalisation platform together.
The company now serves clients in 42 countries from nine global offices, helping to drive digital growth and brand loyalty for big names such as John Lewis, Barneys New York, Not on the High Street, HP, Homebase and Wine.com.
Around the time it was founded, the retail industry was focused on moving from simply acquiring customers to browse their online catalogue to finding new ways to increase convenience.
This would often rely on demographic and psychographic models, which categorise customers into segments.
“For the past five years or so, we’ve been detecting and recognising all your purchase intent, whether you’re online or offline,” says Michael.
“What did you look at? What did you put in your cart? We’ve then been leveraging machine learning to predict what would be the next best experience.
“When you’re browsing, all the content has been personalised for you.”
Retailers have also been keen to remove any “bad friction” that might create uncertainty when deciding whether to buy a product.
This could involve introducing a promotion at the check-out or sending follow-up emails to remind them about the product.
But now companies are bringing in “good friction”, adding more content that will give people a better and more rounded experience when visiting a website.
How RichRelevance helps to create hyper-personalisation in shopping
The updated RichRelevance XEN AI platform helps retailers move towards a hyper-personalisation shopping experience by auto-discovering meaningful and numerous customer behavioural patterns.
These include ones that would have passed under the human radar and offer behaviour-based recommendations to customers, assisting marketers in using the insights across all marketing campaigns.
Michael stresses the importance of treating customers as individuals, as opposed to segments, and the software’s deep learning technology is able to identify these specific interests and intentions.
Natural language processing also helps to create a picture of the type of person a customer is by picking out certain information and analysing it within a wider context.
For example, the things a person may buy might build a picture to suggest they’re a new parent or prefer gluten-free products.
“Rather than saying people who bought these types of things also like these types of products, we can understand a lot more about why you’ve bought something to give much deeper personalised recommendations,” he says.
It’s also about creating a “local shopping experience” online that people enjoy, rather than just clicking on a website as a means to an end.
“Brands and retailers are increasingly wanting to take this concept forward beyond where Amazon is today,” says Michael.
“If you’re going to go beyond Amazon, you need to know what’s truly iconic about your brand so you can reinforce this.”
L’Oreal has been one of the major brands to pioneer this approach with tools such as a “virtual try-on” for choosing make-up.
It reported a 4% increase in converting online visitors to buyers after using such tech, while the time they spent on the website doubled.
Another example of how a retailer can take a step back to educate customers for longer-term gains could include recommending recipes for healthy eating based on a shopping list.
Michael adds: “What’s happening now is that people aren’t just buying online, they’re fundamentally shopping online – they want to be inspired and educated along the way,” he says.
“You don’t want to put anything in the way of someone buying a product but, in reality, when you’re a department store a customer assistant might say ‘I’m not sure if this is the right one for you’, and then they’ll show them something else.
“Then at the end, the customer walks away feeling more satisfied. So the brand is reasserting what makes it iconic – it’s now your tastemaker.”
Importance of hyper-personalisation in shopping
Various studies have shown how much importance consumers are increasingly attaching to a personalised shopping experience.
According to global consultancy Accenture’s 2018 Personalization Pulse Check report, 91% are more likely to shop with brands that recognise, remember and provide them with relevant offers and recommendations.
Some 48% left a company’s website and bought elsewhere because the online experience was poorly curated – up 8% on the previous year – while 83% were willing to share their data to enable a personalised experience.
Market researcher Forrester, meanwhile, rated personalisation among five “hot” tech investment priorities for retailers, alongside omnichannel, advanced data and analytics, digital store tech and AI.
This was above other trending tech and concepts like same-day delivery, chatbots, conversational commerce, and virtual and augmented reality.
Michael says: “People are trusting algorithms more than brands in some cases. Having and using someone’s data is no longer perceived as ‘creepy’ – it’s something you’re able to use to increase engagement.
“Staying relevant is the term brands are using all the time and this is a way to do that.”