For many B2B organisations, email remains the number one means by which to maintain regular and scalable contact with prospects and customers. 

However, if you examine the typical email campaign you’ll find that – despite the promise of marketing automation – it is a laboriously manual affair.

Content strategy and creative direction is needed to devise what goes into the email; list and database management is required to decide who receives it; a campaign manager or email channel manager decides when it goes out; a demand generation exec has to report on the results; and a data strategist must pore over the campaign results to inform strategy for the next email shot. And that’s just if it is a one-off blast!

Things don’t improve much if the email is part of an ongoing ‘drip program’. Drip programs rely on preset rules (“If this X happens then do Y”, “if X does not happen, then do Z”) that are used to architect nurture campaigns and trigger communications. These programs are not particularly sophisticated and rely on a one-size-fits-all view of the buyer journey: trusting that all prospects and buyers are the same, and can be successfully corralled by your email drip logic towards a conversion goal. 

Fortunately, the advent of predictive analytics in email marketing means that a lot of these processes will soon be a thing of the past.

Enter predictive analytics

Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. Or put another way, it’s about looking at the past to try and discern the future.

You will have encountered predictive analytics if you have ever phoned a contact centre for customer service or a sales conversation. In this scenario, a script is suggested to the contact centre agent based on predictive analytics applied to your customer profile (which products have you bought, what is your income bracket, what is your lifetime customer value, and so on).

Predictive analytics takes this information and quickly works out what the likely outcome will be. A recommendation is made using predictive analytics as to what the best next step for the agent to take would be for an optimal business outcome.

Predictive analytics tools like Content Intelligence enable organisations to solve various problems by analysing the content that prospects and customers are reading, and using that to automate marketers, sales and demand generation actions on digital channels – including email.

Predictive analytics for email

To automate emails from past behaviour, predictive analytics has to do several actions simultaneously.

Firstly, predictive analytics needs to analyse all of marketing content available in an organisation’s content repository to understand what each piece of content is about (this is usually done with content metadata which tags the topics, themes, people, products, etc mentioned in the content).

Secondly, predictive analytics has to access a contact database (either in a CRM or within the email platform) to identify each contact. 

Based on the contact’s content consumption history (i.e. the content they have clicked on in previous emails), predictive analytics can build up a unique profile of the contact’s interests and automatically decide which pieces of content to send them in the next email.

Examples of predictive analytics in action is this case study of US telco C-Spire who saw a 537% increase in open rates and clickthrough increase by 92% with personalised daily newsletter that used predictive analytics.

Once you realise just how many complex operations are pulled off in real-time (analysing content, analysing a contact and then deciding which content send), you begin to see how it would just now be possible to do it with human intervention or through limited drip logic rules. To really deliver a personalised email experience that takes into account the unique buyer journey and needs – you have to use predictive analytics.


Rather than relying on drip logic we move into what Gartner calls the ‘big content’ era. Buyers expect a ‘big content’ experience; 1-2-1 email communications that understand their unique context and whims perfectly. This requires not only a deep understanding of the customer but also large volumes of categorised and available content. This is well beyond what a legion of campaign managers, CRM experts and content creators can handle – the ‘heavy-lifting’ has to be done algorithmically with the help of predictive analytics.