Account Based Marketing (ABM) is a powerful technique to deliver targeted and personalized engagements to customers in key accounts.  ABM Leadership Alliance’s recent research report shows that B2B marketers who adopt ABM can expect a 171% uplift in annual contract value – so perhaps it’s no wonder that according to data by Constellation Research, 92% of companies plan to implement an ABM strategy in 2017.

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However, according to the same stud, it appears that despite overwhelming evidence of the revenue growth opportunity presented by ABM, many companies aren’t entirely unconvinced of its successful execution or whether they will achieve a satisfactory return on investment. Why is this?

That old nemesis of scalability seems to be the primary cause of concern.

Meeting the Challenge of ABM at Scale With Artificial Intelligence

ABM campaigns are currently, for the most part, only feasible for targeting a few key accounts. This lack of scalability hinges on a company’s ability to direct significant time and resources towards researching and engaging customers. Whilst many marketers have adapted by building a content function within the team, all too often the collateral they produce is generic, and furthermore they have to ‘guess’ where customers are in the buying cycle – a direct contradiction to the goal of relevant, timely, hyper-personalized marketing in the ‘age of the customer’.

Artificial Intelligence is set to turn ABM on its head by
introducing hyper-personalization at scale.

Predictive analytics will feed the ABM process with never-before-seen real-time insight into customer needs and goals, pain points, market dynamics and competitor activity, sentiments and buying triggers. Millions of structured and unstructured data points will be crunched, analysed and filtered  to deliver the in-depth account knowledge needed to drive campaigns and personalised engagement opportunities, that target not just a few select accounts but hundreds or perhaps thousands, without the need to increase the size of the marketing team or create large quantities of marketing collateral.

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But more than just cutting through the white noise, reducing research time and furnishing marketers with rich customer data, machine learning algorithms will remove the guesswork when it comes to achieving relevance and timing. No longer will marketing teams have to produce generic mass broadcast communications, spamming customers, and prospects with untargeted, untimely and irrelevant messages. Instead, they will know the best time to act and the most appropriate attention-grabbing content for each individual customer – generating personalized 1:1 emails, customised website experiences and hyper-personalized brand interactions at scale with laser-targeted relevance and pinpoint timing accuracy.

What Will the Future Look Like?

As AI collides with ABM the possibilities are immense.

As time goes by and the volume of results data and precision improves, it will be possible to construct predictive models using machine learning to find patterns of event types, market challenges and future opportunities ripe for direct action, whilst at the same time correlating patterns of open rates and sharing activity across channels to recommend the most relevant content and delivery mechanism, and the likelihood the customer will want to receive it.  Step forward again and AI-powered applications may even act as a proxy, delivering content on the marketer’s behalf for the ultimate in ‘always on’ ABM.

I think it’s fair to say I have only scratched the surface of how AI could feed ABM in the future – the innovation is still very much in its infancy. But even with development at its current stage, it’s quashing fears and reticence to invest by solving the immediate challenge – the combination of ABM and Artificial Intelligence gives marketers the ability to deliver highly personalized ABM strategies at scale.

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