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Machine learning is present all around us, we interact with it so often that sometimes we forget we’re even doing it. This means that it can get a bad rap, even when it’s helping to lighten the load.

We’ve said it before and we’ll say it again; the road to E-commerce success is busy and long, and it isn’t just reliant on one thing. You don’t have to be overburdened though, with the right insights you can use machine learning to give yourself more time and ease your workload.

With that in mind, here are six great ways that machine learning can help take the weight off your saddles!

Finding a Needle in a Haystack

Having a large catalogue of products can sometimes make it hard for customers to navigate your webshop. This can be overcome with a smart search function to help customers with:

  • Autocorrection - displaying shoes when a customer searches for “shoo”
  • Autopredicting - Live searching as soon as a customer begins their search string
  • Relevant Categories - If a customer is looking for shoes, categories for “trainers”, “slippers”, “sandals” etc. help to narrow the search
  • Popular Items - Placing the highest selling shoes at the top of search results as they have proven to be popular before

These functions make the search process much easier for customers that already have an idea about what they want to buy. They can also be improved over time as more customers use them.

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Tailored E-tailing

In the past, recommendations could easily become irrelevant by paying too much attention to one-off purchases. Now, recommendations are more relevant as they weed out these anomalies, and more dynamic and refined as they are updated in real-time. 

When using customer recommendations, don’t be afraid to use more than one type. Categories such as personalised recommendations, current trending products, all-time popular products or similar products will all help to display your catalogue to the fullest. But remember: label recommendations properly so your customers know what they are looking at!

Divide and Conquer

Think about you and your colleagues, are you all the same? Probably not, variation is what makes us all special, and your customer base is no different. So why treat them as one? Using consumer data, you can get insights into things such as:

  • the sorts of products your customers are most likely to be interested in
  • the frequency with which they purchase them
  • their usual spending habits

Information management can be used to segregate customers and target campaigns. For example, promoting an email campaign for a sale on sandals by reaching out only to those who’ve previously bought or shown interest in that style,  bypassing customers who tend to only buy trainers and vice versa.

Churn Out Sales, Not Your Customers

Machine learning can use customer data for churn prediction. This is a prediction of which customers you may be about to lose. With prior knowledge of which customers are losing interesting, you can take countermeasures to retain them. This can be done in a similar way as above, by segregating your audience and creating targeted campaigns.

Helpful Helpdesks 

Whether this part of your business is called customer support, customer service, customer care or another variation there is one central theme: the customer. Customer-relationship management is vital to building good rapport to attract and keep customers. 

Machine learning can help to map patterns of common customer queries, suitable responses and when to pass on a query to a manned hotline or email service. 69% of customers prefer using chatbots for quick questions, so human-computer interaction can be used to your benefit. 

There ARE Enough Hours In the Day

… or at least there are when you let machine learning lend a helping hand. By offloading tasks that are time-consuming to carry out manually, you can focus on the more important things and more importantly, take time for yourself. 

Thanks for reading! We hope you found this article useful, to find out more about how you can use machine learning to help your e-commerce site please visit:

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