How to use AI for more effective online retail marketing

By: Saima Alibhai

This article explains why and how AI and machine learning can help create personalised online retail marketing messages that address shoppers' specific needs, habits, and preferences.

The idea that machines can make intelligent decisions has been around since the 1950s when the first learning programme was built. It improved at the game of checkers the more it played. Fast forward to today and machine learning and artificial intelligence (AI) are hot topics, wowing us with the potential to change our lives in many ways.

The last few years have seen us move from the realm of fiction, such as Iron Man’s JARVIS (stands for Just A Rather Very Intelligent System), the highly advanced computer system supporting Tony Stark, to shoppers becoming more and more aware of and familiar with AI services such as Amazon’s Alexa or Apple’s Siri assistant.

And for online retailers, the possibilities are particularly exciting and have a big impact on their marketing. With shoppers expecting an increasingly personalised shopping experience, AI provides brands with the potential to deliver this at a level that couldn’t be achieved so far.

Marketers have been working on tailoring messages to the individual customer for some time. With audience segmentation based on parameters such as gender, location or purchase history, we can better target content to customers. For example, a marketing message, which reflects a shopper’s browsing history on an online retailer’s website, is likely to be perceived as helpful and part of a positive brand experience.

Traditionally, personalisation has been a very time and labour intensive task, as the marketer would have to manually create separate campaigns for each customer segment.

AI lets brands deliver relevant content to shoppers at the push of a button. While true AI still only exists in science laboratories, machine learning is already being used in marketing automation platforms to make messages more relevant, personalised and less intrusive. It can analyse millions of data points about shoppers’ preferences and actions.

As shoppers interact with personalised content, the software captures even more data about their behaviours, making interactions with every individual shopper even more relevant over time. Marketing automation platforms use algorithms to analyse which items customers left in their basket, their browsing and purchasing history, and match this information with predictive modelling and the online retailer’s business rules to create hyper-personalised messages that are unique to each customer.

An example of machine learning in action

Image: Toptal

Mark is 37 years old and lives in Manchester. He shops online regularly with a particular omnichannel activewear retailer. As an email subscriber, he receives product recommendations and incentives based on his browse, cart and purchase behaviours. Almost every twelve months, Mark purchases a new pair of running shoes and recently searched the retailer’s blog for content on triathlons.

After 13 months without a purchase, Mark receives an automated email with a discount on his favourite running shoes brand. He opens the email while on a business trip to London. The next day, he receives a text message notifying him of a triathlon workshop in a nearby store of the retailer. Mark attends the workshop, uses the opportunity to try on the latest model of his favourite shoes and returns to Manchester with a brand-new pair.

The application of machine learning in marketing shows that the way we work is going through a big shift. As machines take on the heavy lifting related to segmentation and personalisation, this provides an opportunity for marketers to move from execution-focused tasks to a more strategic role within their organisation.

That means adopting the mind-set of a people manager as the use of machine learning is similar to on-boarding a new team member. Machine learning requires human expertise to direct and “teach” it how to achieve the desired business outcomes.

To ensure the campaigns reflect the changing tactics and goals of the business and continue to provide maximum engagement, marketers need to provide the marketing automation platform with all relevant information, closely review the results and make changes where required. They also need to listen to feedback from customers and add in the creativity that machines cannot deliver quite yet.

How to make machine learning a success for your brand

You need to stay in control of the marketing programme, tools, strategies and services you use. Machine learning requires your expertise to direct and “teach” it how to achieve your business outcomes. Closely review the results of your campaigns and make changes over time to achieve improvements. Also make sure you use customer data responsibly and ethically. The desire to create a more personal and interactive experience with the shopper is not an excuse for unchecked data mining.

AI can be great if you find the right balance. Research from Oracle shows that shoppers want to feel in control. Three in five shoppers (58%) have a positive attitude towards their grocer suggesting a shopping list based on purchase history, social and environmental data. But almost the same number (54%) of shoppers would consider it intrusive if their grocer automatically charged and shipped items based on that information.

Conclusion

As machine learning becomes more common, the benefits for both shoppers and online retail marketers are clear. Marketers can outsource the repetitive, time and labour intensive tasks around personalisation to their marketing automation platform, allowing them to focus on more strategic and creative aspects of the business.

Shoppers receive a truly personalised experience, being recognised as individuals rather than part of the crowd. As technology advances and shoppers’ expectations continue to rise, machine learning is set to play a vital role in creating the more personal, convenient experience shoppers crave. And offer great revenue-generation potential for commerce marketers. 

 

By: Saima Alibhai, Managing Principal Consultant, Oracle + Bronto

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