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. 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.
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:
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.
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 “mirroring” campaigns online and in-store
Brands can essentially “mirror” a shopper’s preferences and interests both on the website and inside the physical location of the store.
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.
- Posted by Udit Agarwal
- November 25, 2017