The Role of AI in Personalising eCommerce Experiences

We've said it once and we'll say it again! Personalisation within your ecommerce strategy is no longer a luxury but a necessity. Consumers don't want to feel like a faceless number handing over their money to you. They want to feel seen and have every online experience directly relate to their wants and needs. But, on the other hand, they don't want to be sold to and they definitely don't want to feel like they are being watched by brands, their browsers or their devices.

Leveraging AI could transform your approach to customer engagement, enabling you to create tailored experiences that drive loyalty and revenue in subtle ways to appease the needs of your audience.

Here are some ways that you can start to use AI to amplify your personalisation strategy.

Dynamic Product Recommendations.

AI algorithms can analyse vast amounts of customer data to understand individual preferences and buying behaviours. Using AI-powered recommendation techniques can provide personalised product suggestions in real-time under the banner of "You may also like" or "People like you also bought".

Shopify offers automatic product recommendations through its Search and Discovery app (only compatible with certain themes). Shopify's AI system generates these recommendations based on products that are commonly purchased together, have a similar product description, or products in related collections. It's not possible to edit the automatically generated product recommendations, but you can add your own recommendations manually through the Search and Discovery app.

AI generated recommendations for a product should change frequently as the algorithm gathers more data around purchase patterns and to a certain extent can be left to its own devices. But a human touch should always be

Campaign Targeting and Segmentation.

Performance Max and Advantage+ campaigns are both just one big AI powered algorithm. The need for manual behavioural targeting and segmentation with these campaign types is, therefore, minimal. The key to making sure these campaign types work for you and are able to make the consumer's experience as personal as possible lies within fuelling them with enough 1st party data of existing customers and also have creative variety.

Creative variety means having a wealth of different styles of creative from brand videos, UGC, collections and carousels with a choice of primary texts that the algorithms are able to text and mix depending on what it believes will work for that particular user. The algorithm will spend money where it sees the most opportunity and when viewing native reporting you can view the split between new and existing customer segments to see which messages and creative resonate the best with each.

Personalised Email Marketing Campaigns.

Email can be one of the most effective channels when it comes to converting customers and increasing their LTV. But it can also often be the first channel to "set and forget" meaning that ongoing testing and optimisation can be overlooked. Allowing AI to do some of the heavy lifting within your email strategy, could make your campaign more effective with less effort needed from you that you might think.

Segmentation:

AI-driven segmentation is becoming more sophisticated than ever. Tools, such as Emarsys can use machine learning algorithms to identify and group subscribers with similar characteristics or behaviours, enabling more targeted email campaigns. The traditional method would be using the RFM framework but AI would be able to expand on this in a far more granular way.

Content:

Most email marketing platforms such as Hubspot and Mailchimp have generative AI or AI assist when it comes to content and content optimisation.

So when it comes to creating effective subject lines, email copy or even A/B testing images and layouts, the native AI functions within these platforms can be increasingly useful. Within Mailchimp you can even use their AI powered content optimiser to personalise the content per contact.

Automation and Behavioural Triggers:

Automated email scheduling can be incredibly useful when sending our email campaigns. Mailchimp offer a optimised send using previous open rate data, industry and geographical benchmarking to optimise the send times of your emails in order to improve open rate. List management, and even responses to common customer question are also among the most commonly used strategies utilising AI.

You are also probably already using AI to trigger emails based on specific user behaviours, such as abandoned cart reminders or re-engagement emails for inactive subscribers.

AI-Powered Chatbots and Personal Shoppers.

AI powered chatbots can help answer standard customer queries including questions about delivery, calculating shipping fees, processing returns or even checking whether items are in stock. Having a 1-1 interaction with your customers is a way of building relationship and trust and an AI chatbot is a time efficient way of meeting that need.

For fashion retail in particular, thinking about a virtual personal shopper could provide your users with the personalised experience they want without the need for a bricks and mortar store. Using that same product recommendations technique, an AI virtual assistant can pull whole outfits or products suitable based on past purchases. If your products are tagged correcting and effectively within your merchant centre, users could also ask to see a range a products based on certain parameters suited to their needs.

AI should make a marketers job easier; not by taking over completely but by being able to automate more tedious tasks or sifting through large amounts of data to find patterns a human would never spot. These insights should then be used to improve personalisation and provide a more cohesive and relatable marketing strategy to both new and returning customers.