Sign up for our newsletter - Navigating the horizon of business technology​
Technology / Data

Using predictive analytics to fix B2B content marketing

The world of analytics is often one in which solutions are in search of a problem. We are prone to obsessing over analytics because it is the new shiny thing. Rather we should make sure the analytics we use actually solves a business need. Fortunately (or unfortunately), B2B organisations are finding themselves with a business need that could absolutely do with a dose of analytics: unused content.

According to PQ Media’s Global Content Marketing Forecast 2015-19, $145bn was spent on content marketing in 2014 with over $76bn apportioned to B2B content marketing. Over the same period, analyst firm SiriusDecisions found that in the average B2B organization "at least 60% of content goes unused. Put that together with the figures from PQ Media and you arrive at the eye-watering figure of $50B in wasted marketing content, globally.

Wasted content isn’t the only issue B2B organisations are contending with. There is an engagement problem, too. The latest report from the Content Marketing Institute reveals that engagement is the biggest challenge for content marketers in 2015: 54 percent of B2B marketers say they are "challenged" or "very challenged" in this area.

So where does analytics fit into this?

White papers from our partners

The are three principal types of analytics: descriptive, which uses business intelligence and data mining to ask: "What has happened?"; predictive, which uses statistical models and forecasts to ask: "What could happen?"; and prescriptive, which uses optimization and simulation to ask: "What should we do?". As I explained before, each of these have their uses, but it’s predictive analytics that I wish to posit as a solution to B2B organisation’s content marketing woes.

Predictive analytics are great to use for scenario planning – providing a range of outcomes to choose from and plan against. With these options you can work through to a natural conclusion to predict things like what sales will be like next year, or how many visitors you’ll get to your website. The model is designed to give you answers – or at least best guesses – to your question.

More importantly, predictive analytics tools such as Content Intelligence allow B2B organisations to solve various problems by analysing the content that prospects and customers are reading, and using that to inform marketers, salespeople and demand generation execs of what to write, send or say next.

Predictive analytics for content marketing

At the risk of predictive analytics for content marketing sounding too conceptual, here are some examples of how it can solve B2B content problems:

– Content wastage

Predictive analytics will identify the pieces of content that will be most engaging to your prospects and customers, and make recommendations as to which pieces you should send to them. This saves your sales team the time of having to wade through a central database and having to learn about all the content that is available at their disposal.

– Prevent churn from your marketing funnel

Predictive analytics can identify leads that are likely to churn and take action using your content. One example of this is personalization: rather than uniformly showing visitors to your site the same content each time, predictive analytics will examine each visitor’s content consumption history and display the most engaging content to retain their interest.

– Improve Marketing-to-Sales handover

The Marketing-to-Sales lead handover is a crucial point in lead management. It’s also the site where most revenue is lost – for a variety of reasons. Predictive analytics solves a variety of issues during the lead handover point, not least because it enables salespeople to prioritise leads. Predictive analytics also enables marketers to pass on a large amount of lead intelligence (such as their emerging needs and interests) to salespeople for a more informed sales discussion.

B2B content marketing is currently in something of a rut. There are known problems with engagement, wastage and demand generation in general. Fortunately, by looking to predictive analytics as a solution, B2B organisations have a real chance to rectify these issues.
This article is from the CBROnline archive: some formatting and images may not be present.