4 AI Use Cases to Optimize B2B Lead Management

4 AI Use Cases to Optimize B2B Lead Management

As recent headlines suggest, there is promise for the use of artificial intelligence (AI) within marketing, but many leaders still struggle with how and when to embed AI and machine learning (ML) into their workflows.

At present, marketers have access to a range of AI/ML models that have been incorporated into marketing automation platforms (MAPs) and account based marketing (ABM) platforms – and one would think this is positive news as 73% of B2B marketing organizations have installed some type of marketing automation platform.

Yet, 63% of them estimate utilizing less than two-thirds of its capabilities.

Concurrently, B2B CMOs rank demand generation as the top way that marketing can add unique value to organizational strategy, but many organizations struggle to convert top-of-funnel lead generation performance to later-stage commercial outcomes.

Clearly, marketing organizations don’t always know which AI/ML workflows to embed within their campaigns and programs. And this is a real challenge, especially amidst strong economic headwinds: Marketers must be prepared to show results from new martech investments, because in the end, what CEOs and boards really want is growth, not fancy AI.

To more effectively close pipelines and not lose momentum in this evolving space, get started with the following four AI/ML use cases to reduce overall costs and drive growth against a backdrop of economic uncertainty.

Identify Prospects Through Predictive Lead Scoring

Although MAPs and salesforce automation systems excel at capturing leads, they can also create problems for marketing leaders.

Enter: Predictive lead scoring, which uses available data sources for helping qualify leads more accurately. Some ABM platforms and MAPs can layer in data with how closely your leads match your ideal customer profile, third-party intent data, as well as engagement data.

Collectively, this data is used for finding leads that fit your existing target persona. Most predictive lead scoring algorithms analyze data from past customers and current prospects to predict future outcomes.

Look for insights from your MAP to estimate the influence of lead characteristics on revenue and guide lead scoring. Some ABMs identify signals on an account level, versus aggregating individual prospects’ scores within an account, so that marketing organizations can more scalably  see where members of a buying committee might be expressing implicit and explicit signs of interest.

Once leads are scored, make sure to discuss how higher priority leads will be surfaced with your demand generation team via any combination of CRM fields, alerts and dashboards/reports. Start small by finding a champion within the sales team to pilot the initial rollout of leads elevated by the AI program you put in place.

Align Sales and Marketing with Lead Disruption

Despite vast sums spent on campaigns, events and collateral, digital marketing leaders can’t deliver results if the wrong rep is matched with the most perfect prospect. Ensuring alignment pays off: sales and marketing organizations that prioritize alignment are nearly 3x more likely to exceed new customer acquisition targets.

Intelligent lead distribution supersedes tired techniques like territory assignments or other business rules hard wired into a CRM. Instead of relying on a fixed algorithm that might just be dividing up leads based on territory assignments, AI is enabling leads to be distributed by their likelihood to convert with a specific rep.

This workflow could predict who to assign a lead to based on the sales person’s expertise, pipeline load or other parameters as well as based on the lead’s preferences, background and history of interactions.

After all, B2B organizations that unify commercial strategies and leverage multithreaded commercial engagements will realize revenue growth that outperforms their competition by 50%.

Tailor the Customer Journey with Predictive Content

Digital marketing leaders often struggle with identifying the right content to send or display. This is true with anonymous users for whom there’s no engagement data, along with known prospects and customers that have visited a company website multiple times.

Some vendors meet the need for 1:1 marketing through the personalization of web experiences or email using data and AI models. Generally, when there’s more data available for a given customer, such as engagement data and CRM data, AI models for delivering content will perform better.

Regardless of what AI workflow you employ for personalizing content, leverage the output of those workflows for building targeted narratives that complement and underpin your brand story and address customer needs. Use the insights from your AI/ML analytics for assessing the impact of micronarratives (i.e., pivotal moments in the customer journey) and create actionable content that targets customer needs and pain points at each moment in the customer journey.

Hit the Timing and Channel Sweet Spot

Even with martech running smoothly, marketers are competing with a myriad of other brands, each generating their own blizzard of messaging. It’s not only easy to get lost, it’s hard to stand out in the noise without depleting your budget. Some AI/ML-backed capabilities, like send time optimization and inferred channel preference, identify the timing and marketing channel most likely to perform best with a specific person. They also autonomously individualize the channel/timing of that message across a large number of audience members.

As one example, if a person opts into both emails and text messages, the lead would receive only a single message in a single channel, to maximize engagement, with a fallback waiting to deploy in other channels. As another, marketers can optimize open rates by incorporating an AI function in a MAP into their campaign that would send an email without necessarily knowing when a person or group is most likely to open that email.

In the end, AI is not a panacea. Commercial offerings may be good enough and getting better, but they still require investments in people and processes to drive performance improvements. Ensure your sales team is fully engaged and participating in the AI/ML workflow process, that you have good insight into your vendors’ capabilities and retrain your models regularly to achieve clear and profitable results.

Jeff Goldberg is a senior director analyst in the Gartner Marketing Practice. He is focused on how marketing teams can integrate marketing technology to maximize ROI. Join Jeff and his colleagues to learn more about B2B demand generation at the 2023 Gartner Marketing Symposium/Xpo.


  • Jeff Goldberg

    Jeff Goldberg is a senior director Analyst in the Gartner Marketing Practice. He is focused on how marketing teams can integrate marketing technology to maximize ROI.

    View all posts

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