How Technology Can Streamline ABM
Successful ABM relies on mapping buying groups within enterprise accounts. In practice, this has been impossible to do accurately until now.
Pretty much every technology platform going has released a major AI update over the last 12 months. This week alone saw Integrate and Demandbase introduce new AI features. These newer releases aren't just a "me too" type text generator that few people will ever use, although ON24 have embedded one in their platform as part of a broader update. Instead, technology vendors are now introducing AI in a way that complements the strengths of their platforms.
Put together, the various AI updates are a major step towards solving one of the biggest challenges with ABM within an enterprise environment. For all the transformational promises, ABM often ends up being a re-run of existing types of campaign to existing audiences. Marketers don't use the selection of accounts in the target account list to meaningfully change the campaign content or customer experience. That's because many ABM programs ignore the contact layer when deciding on ABM targeting.
The Missing Middle
Target audiences may be selected based on firmographic profiles and customer fit, but the ultimate aim of many ABM programs is still to generate new leads at the relevant accounts. The focus on account acquisition and sales alignment means that the middle-of-funnel stages are frequently skipped. Accounts are targeted as a single entity at the top of the funnel and followed up as a single entity by sales at the bottom of the funnel. However, the middle-of-funnel nurture stage is typically still executed using a lead-based approach. That's a restriction within the standard martech stack, but it severely limits the potential of Account Based Marketing.
ABM is intended to be a land and expand methodology. The aim is to engage the entire buying team with content relevant to their persona and business needs. Marketers should be looking to round out the account profile with all members of the buying committee by capturing their details and nurturing each of them until there is activity from multiple contacts at the account. Then, when sufficient members have engaged, the entire account is passed to sales as a single entity.
Account Modeling
The trouble is that few tools are capable of actually executing such an approach at scale. Many ABM tools focus purely on the account engagement piece, delegating contact-level engagement to marketing automation platforms that aren't able to visualise the individual buying team members as a collective. That missing step of linking together the entire buying group simply isn't possible within a traditional technology stack, particularly for businesses that can sell to multiple departments within the same company.
To truly separate the different buying groups within each company as well as identify the role of each individual in the buying process, you need a clear visualisation of the corporate structure within each account. That requires accurate job title information and a data platform capable of automatically mapping the organisational hierarchy based on how buying groups are typically structured. This can be done manually, but at scale, it requires AI-based machine learning.
As such, it's no wonder that both Demandbase and Integrate have used the current wave of AI products to integrate buying group identification features into their account-based advertising platforms. The next step is to use that same information further down the funnel as part of the cross-channel nurturing program.