Precision vision

How, then, to make intelligent recommendations to customers interacting with the brand through digital channels? Above all, it requires data, specifically three broad types.

The first is data about the individual and their preferences. “Intelligent recommendations typically employ collaborative filtering and content filtering approaches,” explains Gary Arnold, data strategy director at Kin + Carta.

“Collaborative filtering means looking at what people like your customer have done – what have they liked or bought, for example. Content filtering means looking at the content a person is interested in and recommending similar content.” 

Both approaches require organisations to understand more about individual customers and build customer profiles. Can you identify a customer no matter which channel they choose to engage with the brand through? What do you know about similar customers? What can you infer, even if you don’t know, particularly about what customers think and feel? The second kind of data needed for intelligent recommendations is operational. 

To be useful, recommendations must draw on information such as stock levels and availability. After all, there is no point recommending a product that is not in stock or that can’t be delivered. 

“That’s another whole chunk of data that you should be trying to understand: for example, availability, pricing strategies, how you can get in front of them, and how you supply to the customer,” says Arnold.

A third crucial source of data is the outcome of previous recommendations. How did the customer respond to a particular recommendation – did they take it up or at least consider it, or was it rejected out of hand? Which recommendations worked well – and why?

This data can be fed back into the recommendations to optimise results, but this will take time, particularly for organisations with less mature data collection and management capabilities. 

“The challenge is building detailed profiles of customers, centralising data rather than having it stuck in siloes,” warns Browning. “Once you have that, there are tools and methodologies to help you capitalise on the fact you know who your customers are and that you can segment them.”

More organisations are now recruiting specialists with the skills to build this sort of architecture, says Deepali Vyas, global head of the fintech, payments and crypto practice at executive search consultant Korn Ferry. 

“Everything has become so customer- centric; whether you’re delivering a product or a service or software, you need personalised distribution,” she says. “So, with that, we’re looking at a lot of predictive analytics, customer behaviour analytics and so on. The customer needs to feel like that business knows exactly what they want, how they’re going to go about delivering it and even predicting what they need.” 

These are often industry-agnostic disciplines, Vyas points out, and organisations can learn from their peers in different sectors. “We recently helped a leading financial services firm hire a chief artificial intelligence (AI) and data analytics officer and they recruited from a technology company,” she says. “They wanted a fresh set of eyes on something they weren’t doing well enough.” 

This is not to say that businesses seeking to capitalise on intelligent recommendations need to adopt the most advanced techniques from day one. Indeed, it makes sense to start with less demanding approaches. For example, rules-based recommendations make simple suggestions according to an agreed rule – if a customer bought new tyres two years ago, for example, perhaps now is the moment to recommend a replacement. 

This, in turn, can lead to more sophisticated techniques, such as harnessing AI to make smarter suggestions. Deep learning, for example, can be applied to large customer behaviour datasets to identify opportunities for recommendations that might not be obvious to human experts, explains Lionel Touati, Google’s head of partner engineering for EMEA South.

“It allows you to create much smaller segmentations,” he says, and therefore make fine-grained recommendations based on the precise behavioural characteristics of individual customers. 

Furthermore, Touati says, AI-powered recommendations can be optimised for commercial KPIs, such as order value or the number of items purchased. This automates the feedback loop between the recommendations and their outcome. “You have this continuous improvement which makes the system much better,” says Touati.

Businesses can also be smart about how they collect data. They may opt to ask customers questions over time, for example, rather than overwhelming them with multiple requests in one go. Tapping into other data sources – in a responsible and compliant fashion – can also be rewarding; social media, for example, is a rich vein of behavioural and attitudinal data.

“Don’t be afraid to experiment,” concludes Browning. “Be creative rather than getting trapped in a formula – think about all the interesting ways that you can influence a customer journey and create a different kind of outcome. That’s ultimately what you’re looking for: a differentiated experience that drives long-term loyalty.”

“Don't be afraid to experiment. Be creative rather than getting trapped in a formula.”

• Start simple – but get started. Customers expect online experiences to be personalised and those that don't look dated. But a little intelligence can go a long way: if nothing else, define your high-value customers and find out what works for them. 
  
  
• Focus on where the issues are. Choose, say, three key areas where performance needs to improve – conversions and transactions are obvious places to start – and build hypotheses that can be tested in each one via experimentation. Once validated, build these hypotheses into the proposition. 
  
  
• Work towards a centre of excellence. Build around personalisation, with recommendations as a key component, expanding use cases and creating repeatable processes and technologies to address those use cases.

This article originally appeared in Thread, Edition 3. Thread is Kin + Carta’s quarterly magazine that cuts through the complexity of digital transformation. Making sustainable change real, achievable and attainable.