Four Big Ways AI Can Improve Customer Experience in Online Retail

Posted by Abi Davies 29 Jun 17

customer experienceAs explored in our latest ebook, A no-nonsense guide to AI in ecommerce marketing, it’s only a matter of time before machine learning algorithms take over certain parts of an ecommerce marketer’s job.

…But not all parts—some will always need the human touch. Does that include providing a kick-ass customer experience?

Nothing can replace what human beings bring to the creative side of marketing (at least for now).

That said, it’s inevitable that artificial intelligence will have its part to play—but this is something to embrace, not fear. Because machines are created to help marketers, not replace them completely.

From predictive replenishment to taste profiling, here are four ways AI can—and will— improve your brand’s customer experience.

Personalisation

Artificial intelligence enables retailers to make their marketing messages hyper-personalised.
Here’s a closer look at how:

Creating and defining micro-segments | Machine learning algorithms can process vast sets of data and spot subtle patterns amongst customers.

Using a process known as “clustering”, an algorithm can then use the information obtained to split the data into micro-segments. Customers in each micro-segment will all have something in common; 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 the human eye.

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? As Ometria research shows, most consumers *are* bothered if a company emails them with products that don't match their personal taste.

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

Product recommendations | What makes AI-powered product recommendations different?

Whereas normal recommendation engines tend to focus on “latest” or “most popular” products, AI-powered ones use individual customer data, as well data from customers displaying similar traits, to learn the best product to put in front of each person.

predictive replenishment ecommercePredictive replenishment | This is an important point 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).

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 its got to decide the right amount of reminder messages to send (as, often, retailers send too many!).

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?
AI can also take care of the number of messages sent to a recipient, and ensure there’s no duplication.
AI and email marketingAll of this will, once again, ensure a customer is only sent the right material for them, at the right time and on the right channels.

Chatbots

Nothing will ever beat human rapport, but to write every single message to every single customer, at scale, is impossible. Which is why automation is such a godsend for ecommerce marketers.

But there’s no denying that automation can make correspondence with a brand seem a bit cold and, well, automated.

But that’s where AI is coming in and mixing things up. From Shop Direct’s “artificially intelligent conversational user interface”, Very Assistant, to Adidas’ Facebook messenger chatbot, AI is helping retailers provide a more personable service without needing human supervision.

Creates room for the human touch

As illustrated in the points above, artificial intelligence can free a marketer’s time by automating certain tasks.

This enables a marketer to invest more time in the customer journey. This could take the form of hosting in-store events or reading and responding to customer feedback. 

It's all about making a customer’s experience as smooth and straightforward as possible whilst always showing how much you care.

Topics: Ecommerce customer retention, AI in ecommerce marketing, Ecommerce customer service

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