The day-to-day reality of email marketing is ‘getting stuff done’. Every time you hit ‘send’, it all just starts again, and again, and again. Each day you sit down to create campaign after campaign, like Bill Murray in Groundhog Day.
But it doesn’t have to be like this. There is a better way to do things, a smarter way to create individualized campaigns.
The answer? Machine Learning. Machine Learning allows you to deploy individualized email campaigns at scale and speed. It gives you the equivalent of a million marketers all crafting individual emails for every one of your consumers.
But rather than adding to the hype about ML, here are five elements of Machine Learning that you need for a successful email strategy:
Consumer’s lifetime interest graph
Akin to a dynamic ‘word cloud’ of each consumer’s interests, machine learning provides a multidevice cross-channel one-to-one user profile that is the weighted sum of the entire history of the consumer’s tastes and contributed data – rather than a collection of individual items with which the customer recently interacted.
This enables you to deeply understand the customer based on their ranked lifetime tastes, interests and engagement with your brand – and not just the last click or abandoned purchase.
The number one rule of email engagement – ‘surprise and delight me’. Emails must be fresh and relevant – what the consumer sees today is different from what they saw yesterday, and different from what they will see tomorrow.
There are two elements of ML-driven diversity:
- Product diversity: when sending an email with (say) 9 offers, use ML to ensure the nine are all sufficiently different from each other
- Temporal diversity: ML allows you to (automatically) ‘surprise & delight’ the consumer with diverse content, all within the context of the consumer’s interests.
This diversity capability is particularly important in email newsletters, where content needs to be relevant yet consistently fresh.
The relevance model will include the weighted interests of the specific consumer, but may also incorporate overall popular/trending products.
E-commerce and fashion brands may also wish to prioritize the newest products into their relevance model.
Marketers need to be able to apply business rules to maintain business control and avoid the ‘black box’ syndrome.
Every brand has an existing IT infrastructure, so it’s also important this type of technology doesn’t disrupt your setup, and serves as an AI-driven CMS for your current ESP.
Machine Learning gives you the power to send fresh and engaging content to every individual customer, every time, at any scale. And let’s face it, your customers have been conditioned to expect nothing less. In today’s retailing environment, the personal touch still matters, but you need to be able to deliver that personalization instantly and at scale. Machine Learning platforms don’t just lighten your workload; they help you tap into vast wells of customer data to build better relationships faster.
And as with many things, it is usually best to start small perhaps with a Pilot to prove the business case, and scale quickly. As a guideline, retailers can typically expect to see a 30%-70% uplift in email revenues.