Measuring the ROI of marketing

In recent years, companies have been spending millions producing as much marketing content as possible. They have been keen to create the likes of white papers, blogs and videos because they have been seen as the latest, smartest way to attract new customers.

 

But content faces a reckoning. With expected sluggish UK economic growth of barely one per cent, next year, CFOs and finance teams are now piling pressure on marketers to demonstrate exactly what ROI all this content has achieved. Marketing leaders must now make big moves towards using AI and other tech to prove the link between their output and pipeline, or potentially face cuts to team budgets and even jobs.

 

For too long, marketers have relied on basic metrics, such as page visits and report downloads to show that their content is successful. They do this out of habit and because simple measurements are easy to explain to CEOs and other senior leaders.

 

The truth, though, is that they prove very little and often only serve to feed the "Marketing Qualified Lead Industrial Complex” that so many marketers are grappling with. They demonstrate that people are interacting with marketing – sure – but they don’t show if they’ve really read it, which bits of the content they’ve focused on, for how long and what that might reveal in terms of solution-fit. They also fail to reveal which company departments or individual members of staff are engaging and if this actually leads to sales.

 

This lack of a clear path to revenue has meant companies have poured money into initiatives that aren’t working. Left unchecked, this will lead companies to reduce investment in marketing for all the wrong reasons. High-quality but hard-to-measure channels may be neglected. External messaging and innovation will be weakened. Marketing teams will find themselves marginalised by other departments that can show clearer ROI.

 

Turtl has had clients, for example, who have almost abandoned strategies because they couldn’t prove how well they were doing, only to have to rethink when better measurement and data showed that they were actually performing well.

 

Marketing budgets have already flat-lined recently, according to Gartner. Now is the time for marketers to turn to more complete and nuanced ways of proving content’s impact on revenue, if funding is not to be squeezed further and a negative feedback loop of misinformed divestments is to be avoided.

 

They must make much more use of AI and tech-driven session-level behavioural intelligence, and firmographic and intent data and analysis. These should be employed to identify people who often revisit key sections of marketing material, say, or which particular bits of a white paper attract the most interest, from whom and why. One social-media channel, such as X, may have had the most click-throughs to a blog, but LinkedIn may have attracted people who spent far longer reading it and are better aligned with the ideal customer profile or target account list. Technology must also be used to identify where people go next after looking at content, particularly when and how they interact with sales teams.

 

This will allow sales staff to be far more specific in who they target to try to secure business, rather than having to take a broader, scatter-gun approach. They can build up a hit list of accounts to pursue, or grade all accounts according to engagement, then approach the most promising and work their way down.

 

We worked with a company in the telecom sector that was able to double its win rate by using exactly these intelligence-driven strategies. Similarly, a large financial institution was able to identify which departments at certain clients were reading a lot of content in a very particular way. Its sales team used that insight to initiate conversations across the buying group on the most pertinent topics, helping seal a deal that – remarkably – covered the entire annual regional sales team target.

 

Marketers can and should make use of platforms such as HubSpot, Marketo, or even in-house platforms to uncover data. Turtl’s revenue content platform also provides excellent evidence of activity ROI.

 

AI is particularly useful because it can interpret fragmented behavioural data at far greater speed and scale than other methods. It can analyse thousands of data points in seconds, provide continuous, impartial feedback on outreach success and even identify the best times to push out content, based on when people have been engaging with it.

 

Indeed, this ability to identify specific targets will mean AI increasingly humanises, rather than dehumanises, marketing. It will allow marketers to speak to real buyers, not just personas. It will be able to create hyper-personalised messaging, proposals and content at scale, too, allowing marketing teams more time to concentrate on strategy, narrative and other aspects of their jobs that they often don’t have enough time for.

 

A recent McKinsey survey found that some two-thirds of organisations have not yet begun scaling the use of artificial intelligence across their business, marketing and sales teams are showing the greatest revenue benefits from using the new technology. These functions have seen AI can work for them—now they must use it to demonstrate how all their content delivers results.

 

If leaders use the tools available to them to bring measurement up to date, content can continue to thrive as an important part of their arsenal. Plough on with the old methods, and their content’s perceived value will fade in a way that helps no one but their competitors. 

 

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Nick Mason is CEO and co-founder of Turtl, the first Revenue Content™ Platform, which helps B2B marketers measure content’s impact on pipeline and revenue.

 

Main image courtesy of iStockPhoto.com and NicoElNino