Guide to Email Personalisation Systems

happy shoppers

Email personalisation – a term that has become pervasive in e-commerce marketing.  The notion of personalisation has become so widespread that it has lost most of its power, often used out of context and transformed into its own white noise. Different personalisation systems are designed with entirely different goals in mind, whether to recommend products, produce happy birthday emails, or re-engage a customer who has abandoned their shopping cart. There are three common types of email personalisation systems based on the goals they are designed to address and the process by which these goals are accomplished. Knowing how they work will allow you to make the most of them and make sure you spend the right resources to meet your business needs.

Personalisation Matrix 1

Recommender systems

Recommendation systems operate as part of a specific transaction that recommends products when the consumer is searching or as part of transactional emails. The most common example is a recommender programme that offers other similar items in different styles or from different brands if you are looking at a product.

Suggested items try to match the current purchasing intent of the consumer (currently browsed product) and therefore improve the conversion rate. The same idea is further applied to transactional email:

  1. When you put an item in your shopping cart but do not end up buying the item, you will receive an email with related recommended products.
  2. When you make a purchase, you will receive an email confirming your order with recommendations for the item that goes well with your purchase.

All these use cases serve the purpose of increasing the rate of conversion and up-selling / cross-selling.

Contextualisation systems

Contextualisation systems are designed to increase online conversion and operate in the context of an individual visit/transaction like recommendation systems.

Types of contextualisation include A/B testing (Optimizely), search and contextualisation based on a search query (Bloomreach), and adaptive content based on geography or visitor navigation (for example, London visitors will see a raincoat banner and California visitors will see a t-shirt banner).

The concept behind contextualisation is to choose the most appropriate layout to optimise the current visitor’s conversion rate. While the goals of contextualisation and recommendation systems are very similar, they accomplish these objectives using completely different means.

Customer lifecycle management systems

While recommendation and contextualisation systems are designed for conversion and upselling in the sense of an individual visit or purchase, lifecycle management systems are designed to improve engagement throughout the customer lifetime (increasing the LTV).

Usually, they work through notifications and emails based on triggers specified through business rules. Welcome emails sent on new customer signups are probably the most common triggers. Some examples of events triggers that can result in lifecycle emails include birthdays, consumer inactivity, app downloads, and transactions.

Lifecycle management programmes such as Salesforce are not typically customised for individual transactions, but work to keep consumers involved throughout the year, maximising their lifetime value.

Email personalisation: putting it together

Given the three fundamental ways of improving mail revenue, we can classify the three kinds of personalisation systems into two categories based on the goals they are intended to achieve:

  • convert/upsell (recommendation and contextualisation systems)
  • increase customer lifetime value (customer lifecycle management)

Also, we can subdivide individual use cases. When we bring it all together, we get the following matrix:

Personalization matrix 2

You will find that one of the four cells in the first diagram is absent. Personalisation online without specific intention to buy. If the customer is browsing, is it not always the customer’s intention to purchase? Not necessarily. For example, an email newsletter for e-commerce designed to engage consumers who are not necessarily looking to buy, and to pull that customer back onto the website. Mercanto is designed to deliver that ‘engage and delight’ experience directly into the body of the email. With Mercanto, the merchandise within the email is personalised and optimised for each individual shopper.

This experience is different from conventional email personalisation systems which tailors the email content based on their last click or purchase. Using a recommender platform to curate items based on the previous behaviour of the customer on line would make newsletters repetitive and boring. We do not have an article in the shopping cart; the consumer does not see a particular product; it is not a scenario of “convert/upsell.” Instead, the goal is to engage clients, get them in the shopping mode. As a result, instead of tailoring to the last click or purchase of the consumer, we need to recognise the lifetime tastes and interests of the consumer while paying attention to the real-time context such as on-sale stock patterns, seasonal transactions, and “hot” items.

There is no clear buying intention, but while you have their attention online, you need to engage the customers to buy. And as mobile commerce becomes even more prevalent, we believe the majority of user interactions will fall into this scenario. In reality, there are hard facts behind this study outlined in one of our previous posts: the bulk of mobile shopping starts either from email (27%) or from the home screen (33%).  60% of mobile transactions do not begin with the customer’s explicit intention to purchase. 

Therefore, the next generation of email personalisation systems, such as Mercanto, do not need an identifiable customer purchase intent. Instead, they make predictions of what each customer might like, based on their historical activity and real-time context.



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