Iain Moss
Iain Moss
Posted 29 June 2021

How AI Can Improve Customer Experience in Online Retail

AI is transforming how we shop. Around 1 in 4 people in the US have a smart speaker in shop, and around 1 in 9 have used their smart speaker to shop. But more pertinently, AI is starting to change how marketers shape the customer experience. 

As the number of ecommerce channels proliferate, it becomes increasingly difficult for marketers to be able to provide a consistent experience across all channels for all their customers. Understanding where every customer is in their buying journey, what they’ve bought before, and what products or categories they are interested is basically impossible without AI.

According to KPMG, 81% of retailers say that AI is moderately to fully functional in their business, so this is something that is prevalent throughout the industry. 

But the human touch is still essential to forming a connection with customers and to make sure that a brand identity still resonates with customers. AI solutions will help marketers to do the heavy lifting and helping to identify opportunities within a retailer’s customer data. 

Here are some of the ways that AI is already transforming retail and helping brands’ customer experiences.

Creating and defining micro-segments 

Machine learning algorithms can process vast sets of data and spot subtle patterns amongst customers.

By clustering customer profiles together based on similar characteristics, algorithms can then split these clusters into micro-segments. Customers in each micro-segment will all have something in common that may not be obvious to a marketer; for example, they spend a lot of money but shop infrequently.

This enables retailers to not only tailor their marketing strategies to specific customer segments, but also to identify common traits in groups of customers that might be missed by a casual review of the data. These micro-segments can then be compared, and underperforming segments can be targeted by marketers.


Taste profiling

This AI model uses a wide range of an individual customer’s data (e.g. past purchase, recent browsing…) to predict what brand or category they’re most likely to want to see or hear about next.

Why’s this so important? We conducted a survey of consumers and 30% of them said they would stop shopping with a brand if they received irrelevant messages, and 61% said it annoyed them when retailers send communications about products they are not interested in.

Moreover, bespoke content is likely to make a customer feel special and encourage them to stick around for longer.


Personalized product recommendations

Many product recommendations offered are simply the products that that particular retailer wants to promote. These could just be products where there is a surplus, or products with a high margin, which is great for the retailer but not so good for the customers.

But AI-powered product recommendation engines can take customer history, taste profiles, as well as what products other customers bought to create an entirely unique set of recommendations for each customer, creating a much more relevant experience for customers.

Predictive replenishment 

For retailers selling replenishable items, such as makeup, skincare and food and drink. It involves using artificial intelligence to predict when a customer will be about to run out of a product, and remind them to reorder.

How does it work? An algorithm will look at a customer’s purchase history to identify patterns in their buying habits. It will also check out historical data from customers with a similar profile that have bought the same item.

Whilst this is possible without AI, resources are limited (a marketer will tend to rely on average repurchase rates, rather than individual customer data). Being able to trigger an automatic email based on a custom time frame rather than sticking religiously to a fixed time frame will increase the chances of catching the user at the right time. 

AI makes it possible to make the process far more refined, incorporating important factors like the amount of the goods purchased. It can also use the information it’s got to decide the right amount of reminder messages to send (as, often, retailers send too many!).

Predictive insights about lapsed customers

Just as every customer is different, every retailer is different, and the lifecycle of a customer looks different. Relying on standard benchmarks of how long a customer stays engaged with a brand on average is no use if it doesn’t apply to your business.

What AI can do is learn about the patterns of how your customers shop and identify when a customer or group of customers has become disengaged with your brand and prompt you to send a campaign to win them back. This could be in the form of a discount code, or a reminder of their favorite product. 

When you have a campaign that is generating results for you, you can then automate this process to anticipate when customers are about to lapse and try to prevent it.

Greater efficiency

Like a calculator can carry out complex calculations faster than a mathematician, machine-learning algorithms can process huge sets of data faster than a marketer.

This means that, thanks to AI, any form of communication between a brand and a customer will be swifter and more efficient than ever before.

An example of this could be campaign optimisation. Instead of a marketer needing to manually check in on campaign performance, and using methods such as A/B testing to try and optimise it, AI has the potential to do this for you.

The following elements of a campaign can be decided by algorithm using a feedback loop:

  • Content | Will customer A. prefer to read about [X] or [X]?
  • Send-time | What time does customer A. tend to open and click-through on emails?
  • Incentives | Do incentives even work for customer A.?
  • Cross-channel | Which channels does customer A. actually like using?

Frequency management

As retailers pursue this increasingly complex range of  AI-powered automated campaigns, it’s essential that the customer experience is optimized, ensuring that the right number of messages are sent using the best possible channels. Using AI to profile customers and understand the optimal number of sends on the channels they are most responsive to can ensure marketers keep the quality of the experience high. 


When we talk about AI, it’s important to bear in mind that a lot of the rules, limits and parameters that AI-powered systems operate in are put in by humans, and configured by them. So, rather than a robot going rogue and generating discount codes at random, and spamming your customers with messages because that generates sales, any good AI-powered solution should be built to support marketing rather than take over controls. 

By automating the regular processes marketers go through, and by identifying opportunities in the customer data of micro-segments that are likely to convert with the right messages, AI can be a help to marketers as it increases the likelihood that any given campaign or set of communications will have a positive impact on your customers. 

The sheer complexity of the modern customer journey means that AI absolutely necessary for the modern retail marketer to succeed at their job. 


To find out more about how we incorporate AI into our success model, click here

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