If you listen to many marketing technology vendors, then AI is the only trend that anyone seems to care about. We were back at Anticon last week at the excellent Magazine London in equally fabulous (and very unusual) warm autumn sunshine. There was a definite buzz about the potential of AI technology around the venue. It was the one feature that every marketing technology vendor in the exhibition hall was trying to sell.Â
Yet, it was clear that most marketing operations teams have far more pressing concerns than integrating cutting edge technology into the tech stack. The same old challenges around organisational alignment, funnel velocity and data quality remain top of everyone’s agenda. AI is undoubtedly exciting, but people are only implementing it when it solves one of those day-to-day challenges.
AI potential in action
Perhaps the best demonstration of AI’s potential came at the end of the day, when self-proclaimed digital anarchist Parry Malm took to the main stage to discuss the future impact of Generative AI on marketing employment. He announced in irreverent fashion that 49% of marketing tasks can be automated through AI right now. This startlingly precise conclusion is based on a ChatGPT-assisted analysis of job postings.
This claim comes from the co-founder and former CEO of a generative AI start-up, so he knows the technology better than most. There was somewhat less emphasis placed in his presentation on his Python scripting skills, which are also significantly better than most marketers, and were just as important as ChatGPT in completing his analysis. Although, it did serve as an example of one trend that was seen repeatedly throughout the day.
The power of AI vs the power of data
Data analysis is one domain where the potential of AI technology can be seen in practice right now. There was lots of discussion about the relationship between data and AI. It is now commonly recognised that effective use of AI requires good quality data. In turn, we’re seeing increasingly widespread adoption of machine learning and predictive AI within the realm of data management. Propensity models and predictive audiences are no longer the sole domain of large enterprises with in-house data science teams. Everyone is starting to use them.
The mainstream adoption of predictive AI is a response to the increasingly complex challenge of engaging B2B buyers. The expansion of the so-called dark funnel to cover up to 70% of the buyer’s journey means that sales engages with prospects far later during the customer journey than in the past. This has a significant impact on marketing performance as well as sales performance. Personalisation is seen as one key answer to this challenge, and the question of how to personalise effectively was just as important as in previous years.
A new marketing operating model
Effective personalisation requires a clear understanding of the customer. It also requires investing in the right technology and processes, as well as executing the right go-to-market strategy. Simon Daniels from Forrester was on hand to outline the relationship between all these different factors using the Forrester Operating Model for Revenue Operations. In an often-overlooked comparison, he outlined the similarities between RevOps and 80s pop music – both require synthesising multiple inputs to achieve the optimum output for the customer.
Marketers can meet those increased expectations of B2B buyers by optimising their technology, data and processes to put the customer first, but only if marketing operations can find the right blend of capabilities. If there was one key takeaway from Anticon, it is that data is now central to everything marketing does. There is intense pressure from both customers and internal stakeholders for marketers to improve their data management strategy. In time, AI will have an important role to play in meeting the much-discussed challenge of engaging the new generation of customers. For now, automation is essential, both as a technology and as a process. Manual approaches are no longer sufficient.
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