How to get ahead in AI email personalisation

ai in medieval times

The Dark Ages.

For anyone not really acquainted with medieval history, the Dark Ages was a time of crippling technological degradation. A period when there was no Netflix. No Spotify. No Amazon. No Sirius or Pandora.

Through the Dark Ages, consumers didn’t receive individualised product recommendations based on their unique tastes and interests. Nor did they get individualised recommendations for new TV shows, movies, or music based on what they’d already watched or listened to.

In fact, to view their favourite TV shows, people were required to “tune in” at specific times. Presumably, the television industry was creating content for all of us. Yet if we wished to watch it, we had to do so purely on their schedules.

It truly was a dark time.

Fine, so my medieval history might be considered a little off. But my root point here is that consumers prefer – and often crave – individualised experiences. And by ‘individualised experience,’ I mean an engagement or interaction with a retailer, e-commerce site, or person that leaves you feeling like your personal interests and preferences were genuinely being recognised.

Segmentation ≠ personalisation

Many marketers realise the importance of personalisation and have spent years looking to personalisation as the primary strategy to drive revenues and engagement. However, most marketers are tasked with taking their customer database, segmenting it and creating campaigns for 10, 20 or even 50 different audiences.

If these segment-based campaigns work, the customer moves along the purchasing journey, and all is well. If it doesn’t, then it’s back to the drawing board. Even with the tools available to them today, this is a labour-intensive process. What might take the marketing team a full 30 minutes to work through for one shopper, could be accomplished by Artificial intelligence  (AI) in a matter of seconds for 1 million shoppers.

AI enables the marketer to collect the onsite behaviour of each shopper and build customer taste profiles for each individual. At the same time, AI analyses all of the retailer’s products at a deep level to understand every product the way the human mind would, making connections across products, categories, and promotions. Using these two data sources, AI can create more individualised customer experiences at a 1:1 level by serving the products each person is most likely to love – and buy.

Related story: Dynamic content in the era of machine learning

What AI Personalization Tactics Do Retailers Currently Use

AI has the ability to improve many areas of CRM, says an International Data Corporation report commissioned by

In the survey, 1,028 business professionals worldwide, including 292 responders working at brands that had already adopted AI, were surveyed how they either currently use or intend to use AIEmarketer AI adoption

For example, in email marketing, 87% of AI adopters said they or intended to use or currently used AI for this purpose. And 74% of all responders—which was made up primarily of non-adopters— expressed the same intent.

Broadly, the study found that current AI users were more likely to use or intend to use AI technology for many tasks compared with all responders.

Amazon continues to set the bar for the retail industry, and Amazon has moved past predictive analytics to more sophisticated personalisation strategies based on a taste profile for each consumer. The company has recently implemented Amazon Scout, a new ML technology that learns each product’s attributes and each shopper’s tastes, and then merchandises other products based on those tastes.

Reports on Amazon’s recommendation strategy indicate they dedicate almost 70% of space on their product pages to product recommendations.

If you work in email marketing for a retail or e-commerce marketer and you’re considering investing in an AI personalisation platform, here are a few strategies for you to consider:

Engage passive consumers

For consumers that are not actively buying something, using last purchase or click can often lead to irrelevant personalisation because that data could be old, for example, if the consumer hasn’t visited your site or clicked on an email in several months. Instead of reacting to information in an effort to complete a purchase, your goal for most consumers is to inspire customers to regularly engage with your brand.

AI personalisation allows you to create personalised experiences for these passive consumers, ensuring that each consumer gets an entirely unique range of product recommendations (onsite, in-email, or in-app), based on their personal tastes, interests, and style.

This happens by using a combination of natural language processing and algorithms to firstly listen to each customer’s tastes and interests, then serve the optimal products for each consumer.  This happens automatically, so you don’t have to waste time tagging related products or on apps or widgets that suggest related products.

Read why retailers seek edge from Natural Language Processing

Boost Customer Loyalty

Using a product recommendation platform to suggest products exclusively for each consumer makes them feel valued. Valued consumers are more likely to be loyal to your brand and recommend your brand to their friends. This is important because loyal customers are the lifeblood of any retail or e-commerce business.  Consider the following:

– 25% to 40% of the total revenues of the most stable businesses in the SumAll network come from returning customers [- Sumall].

– Existing customers spend 67% more than new customers. In short, customer loyalty really pays off [-].

– A five per cent increase in customer retention can increase a company’s profitability by 75 per cent [- Bain & Company].

Cross-Sell and Up-Sell Better

Apart from being able to build a better customer experience and increase customer loyalty, product recommendation platforms can help with something that is even more critical for brand: making money. For many retail, travel, or e-commerce brand, making a sale is just the beginning. Cross-selling and up-selling are essential to maximise revenue from each shopper. According to research from PredictiveIntent, upselling on retail sites performs 20 times better than cross-selling. Fashion retailer Phase Eight does this really well, with the ‘goes with’ section on their product page:

Phase Eight


Roadmapping Your Content Marketing Strategy

For retailers and e-commerce brands looking to get ahead of the curve, there is a clear path to operationalise and scale AI within your marketing team. To help you start thinking about your roadmap, here are three steps that may help formulate your personalisation priorities and timescales:

STEP 1: Establish KPIs for AI personalisation. During this first step, think about your KPIs and what you’d like to achieve from a financial and operational perspective, for example, a revenue target, faster speed-to-market, or better customer retention.

STEP 2: AI Personalisation Pilot. In this step, you’ll be conducting a Pilot exercise and executing the initial plans you’ve prepared. You’ll learn loads from the Pilot and can carry those insights into step three.

 STEP 3: Scale. Take the frameworks established and learnings gained in the first two steps, then implement at a wider scale. Also, use this step to optimise and evolve your personalisation strategy into a world-class program that delivers significant business impact

Wrapping up

Fortunately, modern (non-Dark Age) technology allows us to take advantage of personalization like never before. For example, as marketers, we’re now able to personalise our emails, websites, and mobile apps, so the content and messaging we display is always tailored to the person engaging with it.

Having learned that relevance drives results, email marketers have continued to allocate resources towards strategies and tactics that drive more relevant messaging through segmentation and personalization.

These efforts have resulted in modest but measurable boosts in response rates over the past three years, but big opportunities exist to drive even higher engagement, and this increased focus on segmentation has come at a cost. Most email marketers today spend a disproportionate amount of time on campaign production tasks that can now be handled more efficiently by machine learning systems.

Machine learning has now reached the point where every consumer interacting with a brand can have their own unique experience. Email marketers who have begun to leverage these ML tools to launch campaigns that address consumers on an individual basis are realising they can enable better customer experiences while saving time and boosting revenues; a big win for all.

Retailers can now use machine learning to genuinely transform the way we drive relevance in email by enabling true 1:1 individualization. For many email marketers, it’s the holy grail.

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