Artificial Intelligence has been helping and empowering marketers for many years with its ability to predict and forecast buyer behaviour, but Generative AI (GenAI) has the potential to change the game. While it is early days for GenAI, it has already started showing great promise in a few industries and functions — and marketing is one of them. GenAI and Large Language Models (LLMs), as the names suggest, are creative and cognitive technologies that help ideation, messaging, text and visual content creation and personalisation, which are traditionally the core competencies of marketers.
Adoption of AI in ABM to date
One specific area of B2B marketing that has already benefited enormously from the application of AI, and which could be further enhanced with the support of GenAI, is account-based marketing. Defined as ‘treating individual accounts as markets in their own right’, the power of account-based marketing (ABM) lies in the development and personalisation of propositions, content and experiences for individual stakeholders in key accounts, based on a deep understanding of their specific context.
ABM has created a conundrum for B2B companies. It is a resource-intensive exercise, and a change to the typical approach to marketing investment decisions, which are usually concerned with minimising costs and headcounts as part of an organisation’s sales, general and administrative overheads. And yet it delivers the highest ROI of any B2B marketing approach, and so businesses predictably want to scale it across more accounts — but without adding more resources.
ABM platforms have emerged to model propensity to buy and actual buying signals from key stakeholders and serve up digital content through their buying journey in an attempt to help scale ABM. These tools have been largely adopted by ABM-ers, but they aren’t enough. They can’t provide a 360 view of the customer, don’t allow for deep personalisation, nor deliver offline or in-person experiences. Yet.
The art of the possible
We’re used to thinking about technology in terms of a roadmap, and this is a useful way to consider how to harness the power of GenAI in ABM. Looking at a three-year time horizon (Figure 1), the first year’s focus will be on laying down the foundational elements for GenAI to do more with less. The second year moves on to integrating GenAI technologies into ABM workflows and strategies, then the third year focuses on optimising AI-enabled ABM initiatives and scaling implementation across all customer-facing teams.
