The amount of behavioural and demographic data available for individual online shoppers has increased to staggering levels.

Coinciding with the increase in data, the use of artificial intelligence (AI) and machine learning techniques has become more mainstream as businesses discover the value of insights gained by these advanced techniques.

With more data available than ever before, retail marketers have been given the opportunity to harness this data using machine learning algorithms that describe and predict shopper behaviour in real time.

These insights can be used to drive both automated email campaigns and interactive website experiences that are relevant and personalised, leading to the creation and conversion of lifelong customers that drive increased value.

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Thanks to AI-driven campaigns, marketers are no longer required to manually sort through massive amounts of data in order to discover behaviours and send personalised messaging that increase shopper loyalty.

Retailers who successfully scale and automate AI-driven experiences for their customers are already seeing significant results, including higher overall revenue and higher customer lifetime value.

Using AI-driven approaches armed with predictive marketing algorithms, brands like Finish Line, Chicos, eBags, and Value City Furniture are blending physical and digital shopping experiences to convert customers into omnichannel shoppers with a 30% higher lifetime value than single channel customers.

These brands have been able to leverage a customer’s real-time online and offline transactional and behavioural data to automate individually personalised email campaigns that increase overall order values.

Here are three ways brands can use AI-driven campaigns to aid in personalising email and web marketing efforts:

1. Using ‘abandonment causality’ campaigns with customer service

By harnessing customer data, Value City Furniture is able to recognise when customers abandon items in their cart or leave before completing their checkout, which happens for a number of reasons.

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To combat these types of abandonment, they send personalised email messaging to assist the shopper with the reason they left the site, whether it be shipping costs or a broken promotional code.

Using abandonment causality, Value City Furniture has seen a 283% increase in revenue.

2. Using “mirroring” campaigns online and in-store

After consumers interact with your brand either online or in-store, retailers should personalise future interactions across all channels.

Brands can essentially “mirror” a shopper’s preferences and interests both on the website and inside the physical location of the store.

For example, if a customer purchases a specific brand of athletic shoes at a physical Finish Line location, the next time they visit Finish Line’s website, they could be shown athletic gear of the same brand.

Using mirroring campaigns, Finish Line has seen a 2.5x increase in website effectiveness.

3. Using ‘model based’ campaigns to identify propensity to purchase and disengaging customers

When a customer returns to a retailer after previously interacting with specific brands or purchasing from the site, retailers can use machine learning algorithms to understand the shoppers habits and target them based on prior interactions.

For example, if a shopper normally makes their purchase one week after adding items to their cart, retailers should not send a discount to compel them to buy since their predicted propensity to purchase is high.

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Using these types of campaigns to target shoppers based on browse history or past purchases, retailers can see around a 12% click to conversion – as high as shopping cart abandonment messages.

Data-driven campaigns like these allow retail marketers to increase customer retention rates while sending fewer, more meaningful, personalised messages.

The use of multi-channel behavioural and transactional data, coupled with AI-driven insights, provides brands with more individualised and relevant shopping experience that improve the shoppers’ experience and, ultimately, ROI for the retailer.

Sourced by Dean Abbott is co-founder and chief data scientist at SmarterHQ