How B2B marketers can make the most of AI

Generative artificial intelligence and other fast-developing technologies have much to offer the profession, but there are cultural hurdles to surmount in the race to find uses for these powerful tools 

B2B marketers in an office talking about AI

While leaps in generative artificial intelligence have wowed much of the business world over the past 12 months or so, specialists in B2B marketing have been profiting from the power of AI and advanced data analytics for years. 

Nonetheless, GenAI represents huge potential value to a profession that’s keen to exploit this fast-moving tech. The possibilities are verging on bewildering, according to Eric Gregoire, senior vice-president and global head of digital at pharma giant Bayer’s Consumer Health division.

“There’s never been a better time to be a marketer, because there is so much you can do with this emerging technology,” he says. “But you may also get frozen by where to start.”

AI reveals customer insights

Kevin Iaquinto, CMO at etail software developer CommerceHub, believes that the use of AI and data-driven solutions have already improved B2B marketing significantly in three areas, enabling “hyper-personalisation at scale, better targeting and better creative”.

His view aligns with the conclusions of research published by McKinsey in May. This found a significant increase in the use of GenAI for lead identification, leading to better targeting and personalised outreach. 

Steve Reis, a senior partner at McKinsey and co-author of the research report, says that AI is helping to answer one of marketing’s fundamental questions: who’s the customer? This is particularly hard to answer in the B2B space, because each key purchasing decision in a business is typically made by several stakeholders, few of whom will have exactly the same priorities. 

The growing capacity of data-gathering and advances in real-time analysis promise B2B marketers a much better idea of who the main decision-makers are likely to be and what matters most to them. This should in turn enable them to personalise their marketing communications.

You can learn a lot from what didn’t work

AI and data analytics can be applied at various stages of the sales funnel, the popular marketing model that charts the progress of each potential customer from awareness (learning of a product’s existence) all the way down to action (buying it). As an example of a lower funnel application – that is, the point at which someone is about to make a purchase – Bayer partnered with Google to analyse navigation data supplied by visitors to its own website. The aim was to identify high-value customers based on behavioural traits, such as the time they stayed on each page, to learn who among them were seriously considering a purchase. Such insights enabled Bayer’s marketing team to target those individuals with personalised messages, leading to a double-digit percentage growth in its sales conversion rate.

“The way we do things has changed dramatically,” says Gregoire, who notes that it wasn’t long ago that marketers would base their campaigns on intuition or “what you think is best for the customer. Now you actually know what’s happening in real time. You can make smarter decisions, test, learn and transform.”

But he warns that making the best use of advanced tech solutions is no easy undertaking.

Preparing the marketing team to adopt AI solutions

The challenge here for many marketing teams lies in moving from recognising the value of AI and data-driven solutions to implementing them, according to Gregoire. In the first two years of its AI adoption, Bayer focused on building effective partnerships with tech companies and adding new skills to the team, incorporating AI gradually into the mix. 

Iaquinto believes that marketing teams must first develop the right skills to fill the growing need for “prompt engineers, data analysts and marketing automations”.

Many CMOs have found it hard to persuade every member of their marketing and sales teams of the benefits of AI – and Iaquinto reckons it’s almost impossible to realise its full potential without your department’s total support. No matter what great leads you provide, “you won’t see the growth in the pipeline if sales teams aren’t committed to using it”, he stresses.

Every step in adopting these solutions must be clearly communicated across the entire function. It’s a matter of building a common understanding and then setting clear objectives and measurable key performance indicators.

One of the main barriers to adopting AI is the underlying feeling of replacement anxiety among the sales force, according to Reis. It’s an understandable sentiment, given that 20% of sales tasks could be automated, although these are largely administrative in nature. In fact, rather than causing redundancies in sales, AI could instead help the function to become more strategic and creative. 

It seems that the sensational emergence of ChatGPT over the past year has gone some way to softening such resistance. The advanced chatbot has made AI accessible to people who aren’t technologically minded and its sheer usability has been winning many sceptics over – even those in sales.

The need to smash silos and experiment without fear of failure

For the sales and marketing function to make the most of AI’s power, it must work more closely with IT and data specialists in the organisation, according to Gregoire. This should help marketing managers to adopt a “test and learn” approach when trying out new applications for the technology, he adds. 

This means accepting the inevitable risk that any given experiment will fail. 

“You can learn a lot from what didn’t work,” says Gregoire, who reports that unsuccessful experiments are actually celebrated at Bayer. 

This is not about getting jubilant when things don’t go as expected, of course. Rather, it’s about seeking insights from failures instead of brushing them under the carpet; explicitly acknowledging that testing new tech is not without its downsides; and actively encouraging people to engage in the sort of calculated risk-taking that fuels successful innovation.

Iaquinto notes that many of the most exciting use cases for AI have yet to enter the mainstream. “We’re in the 10th minute of a 90-minute match,” he says. 

Marketing chiefs who’ve been slow to grasp the benefits of AI’s recent advances therefore still have time to try to catch up with the early adopters. But, while they’re focusing on the technology and all its potential uses, they would be wise not to ignore the cultural considerations of applying it.