Machine Learning & the 4 Levers to Next Generation Email Marketing From the Start

The big challenge for email marketers is to systematically improve financial performance and customer engagement.
Smart marketers are now focussing on several next-generation levers for performance improvement: the more you pull these levers, the greater the rewards. But how do these look, and how does that translate to my RFP?

Real-time access to ALL suitable data

Whether the connected consumer shops in-store, in-app, or online, or some combination, she rightly expects brands to recognise her and treat her individually and in real-time.

Google and others have written about ‘moments of engagement’ and there are lots of current opportunities for brands to leverage CRM data, merchandising data, EPOS data, Internet Of Things (IOT) data, apps/mobile data, third-party data, and other datasets. However, the volume, velocity, and variety of all this data can be a challenge for the modern marketer, which brings us to the next big lever: algorithm-based dynamic content.

RFP question:
Idea by Stefano Vetere from the Noun Project
Which of the following data types can be stored NATIVELY in the email platform?

• CRM data
• Pricing data
• Stock & availability
• Instore/EPOS data
• Loyalty data
• Social data
• App data
• Geo data
• Web behavioural data
• Fares & availability
• IoT data

Please explain how this data can be used, with practical examples based on our business objectives.

How soon is this data available for use in segmentation and targeting, after it has been transferred (via API or suchlike)?

Dynamic content using Machine Learning

Every marketer has seen the rise of machine learning over the past five years, but few realise that machine learning can be used to increase email marketing performance by more than 50%.

Self-learning algorithms are now sufficiently advanced so they can mine data and dynamically populate email campaigns with greater accuracy and speed than a human ever can. This is a big step forward for email marketers because now marketers can concentrate on strategy and optimisation, rather than the laborious task of manually building email campaigns.

For example, eBay increased their revenue per opened email by 250% in some countries and 68% globally by using machine learning to insert content into their email platform. Similarly, US retailer One Kings Lane used machine learning to generate a 6% lift in total online revenues.

RFP question:
We’d like to use algorithms to improve email personalisation and speed-to market. Do you offer an algorithm for email personalisation?

If so, please provide a non-technical overview of how it works, and how it will help us improve email performance.

Please explain the criteria the algorithm takes into account.

So how does machine learning work for email marketers?

The components of a machine learning algorithm can be grouped into two broad categories: User behaviour analysis and content analysis. By analysing a user’s behaviour in real time to track mood and compare this activity to lookalikes, much like Amazon’s “Users that viewed this product also viewed X”. You can also use business rules for content filtering and content selection.

When analysing content, machine learning will analyse similarities in site-tagged metadata. It will also use Semantic Text Analysis to build a “topic cloud” beyond what is manually tagged in the website source. Popularity of content is tracked and used to further improve relevancy of personalized content recommendations and drive user engagement.


Trigger emails

Everyone knows that the revenue per email for triggered programs is somewhere between 3X and 7X that of newsletter campaigns, however triggered programs can be difficult to set up and maintain. For this reason, most brands typically only have a few triggered campaigns in place.

Often marketers start with campaigns like cart abandons and life-cycle programs, but there is a myriad of other triggers that generate huge incremental revenues – and nowadays these types of behavioral email campaigns are much easier to manage, particularly using ‘fire and forget’ self-learning algorithm-based triggers, which can really turbo-boost your performance.

Triggers to consider include price drop alerts, new product arrivals, end of line/back in stock notifications, in-store promotions – to name but a few.

RFP question:
We see an opportunity to further enhance our triggers. Please outline the types of triggers your platform supports, and how quickly fully-personalised triggers can be deployed. (-hours/minutes/seconds).

Given what you know about our business, what types of triggers would you recommend initially?

Database growth

Potential customers come from different sources, types of advertisements, and have different customer histories and intentions… why would you treat them all the same? The best approach to increasing list size is to dynamically personalise your site to surgically tailor when and where you get opt-ins.

Intelligently serving sign-up campaigns based on visitor attributes and behavioural actions will increase your email signup rate by 3X-5X. Suggested strategies for increasing email signups include:

Timing: Ask for email before the customer leaves, or after they’ve looked at a certain series of pages.
Tailored based on source: Keep email signup message consistent with display ad clicked, don’t ask for email if the customer came from a marketing email.
Tailored based on current page: Signup request should reflect the users demonstrated interest.
Offer something in return for signup: Many retailers use a generic discount with signup, but often offering unique content can be equally effective.

RFP question:
A lot of anonymous visitors come to our website, visit one page, and then leave. Similarly, many anonymous visitors place an item in their cart and then leave.

How would you help us engage/capture email addresses for these visitors?

Begin the journey

Assessing your performance against each of these 4 factors (data, machine learning, triggers, and database growth) will help you assess your current level of competency. Rather than setting a pie-in-the-sky vision like “world-class marketing,” this approach will enable you to assess where you stand in each area, and will help you identify the gaps in your current capabilities and uncover opportunities for improvement.

When you have assessed your current capabilities, of course, the next step is to identify a roadmap for continuous improvement in each of the four areas. This roadmap will help you create better experiences for your consumers, and generate more revenues for your company.

About Peter Duffy

Peter Duffy is an email marketing and data guy. Peter accidentally helped give birth to his son on the kitchen floor. CEO of Mercanto. By knowing each shopper’s preferences across multiple sessions and transactions, Mercanto determines the best content for each individual shopper. This maximises relevance for shoppers and boosts revenue for brands.

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