Introduction: The Rise of AI in B2B Marketing
In the ever-evolving landscape of B2B marketing, two powerful forces have converged to reshape how businesses connect with their target accounts: Artificial Intelligence (AI) and Account-Based Marketing (ABM).
When discussing AI in marketing, we primarily refer to two key concepts: generative AI and Large Language Models (LLMs). A generative AI model is a subset of artificial intelligence that can create new content, including text, images, and code. LLMs are advanced AI models trained on vast amounts of text data, enabling them to understand and generate human-like text. Just like Generative AI is a subset of artificial intelligence, LLMs are a subset of Generative AI. LLMs are specialised generative AI models focused on language tasks. While generative AI encompasses a broader range of content creation (including images, audio, and code), LLMs are specifically designed for.
This fusion of ABM and AI is not just a trend; it’s a game-changing approach that is revolutionising how companies identify, engage, and convert high-value prospects. Ninety per cent of sales professionals have reported that generative AI helps them serve customers faster. Organisations leveraging AI have experienced a 50% increase in sales-qualified leads, demonstrating the technology's potential to drive significant results.
We spoke with Stephanie McCredie, Senior Director of Account-Based Marketing & Strategic Accounts for Asia Pacific, Salesforce to understand how AI is transforming Account-Based Marketing. Her insights, combined with broader industry trends, reveal six crucial areas where AI is making a substantial impact.
- Insights and Analytics
- Account Selection
- Stakeholders Profiling
- Data-Driven Customer Journeys
- Refining Messaging
- Measurement and Optimisation
Let’s explore these areas in detail.
6 Opportunities for Generative AI in ABM
Insights and Analytics
Before LLMs, an advanced AI model or a custom machine learning solution would be used to analyse large amounts of data, but doing this today is easier, faster and cheaper. For B2B marketers, this means having the ability to have a deeper understanding of customer behaviour, preferences, and engagement history without too much dependence on technical teams.
General foundational models can discern patterns and trends within customer data and predict future behaviours and preferences. This becomes even more powerful when the technology is integrated into your marketing tech stack, and you can communicate with the LLM using natural language.
In the context of ABM, AI can analyse:
- Engagement history with your brand
- Customer segmentation for one-to-few ABM campaigns
- Marketing touches and sales interactions across the customer journey
Insights into the above help marketers make more informed decisions about how to progress customers through their journey. For instance, if a prospect has attended a face-to-face event, the next touch might be a more intimate engagement than another large event.
This is something that can come up as an automatic recommendation from an AI assistant embedded in your CRM based on the notes your account executive or SDR has entered. In Salesforce's case the AI assistant in Einstein.
Similarly, LLMs can identify customer trends and opportunities to improve website performance. They can also provide deep analytics at the individual segment or campaign results. This level of insight allows marketers to refine their strategies continuously based on real-time data.
Account Selection
LLMs can analyse internal and external data to identify accounts most likely to convert, ensuring marketing efforts focus on the highest potential prospects.
From an ABM perspective, the account selection process typically involves the following:
- Analysing the total addressable market (External Data)
- Using annual results, company news and technology initiatives as triggers to determine ICP fit. (External Data)
- Considering internal data from account plans and sales team insights
- Balancing AI-driven insights with human input from sales teams
A custom LLM solution or your CRM can ingest all of the above to create a comprehensive view of each potential account and predict/create a prioritisation order for the key accounts for you target based on your ICP and objectives. Apart from such prediction capabilities built within a CRM, B2B marketers can use generative AI to create personalised content at a much faster pace and larger scale.
The use of AI in account selection is helpful, but it’s crucial to balance AI-driven insights with human input. Sales teams often have valuable insights that may not be captured in data sources, and this human element remains essential in the account selection process. The idea is to augment human efficiency with AI muscle.
Stakeholder Profiling
The next step in running an ABM campaign is stakeholder profiling and developing a buyer persona. Generative AI can help create detailed profiles of key decision-makers within target accounts, providing valuable insights into their roles, preferences, and pain points. Generative AI tools can enhance this process by automating the creation of these profiles and offering deeper analysis.
AI tools can be used to:
- Analyse CRM data to identify decision-makers and influencers
- Track marketing engagements of key stakeholders
- Gather information from external sources like LinkedIn
- Analyse language and sentiment in stakeholders’ public communications
- Identify professional connections and preferred partners
LLM's natural language processing capabilities can play a crucial role in analysing stakeholders' public communications, enabling more sophisticated sentiment analysis and a deep understanding of individual preferences. This level of profiling can be incredibly valuable for sales teams preparing for meetings with new contacts. An integrated AI assistant can provide a comprehensive view of a stakeholder’s history with the company, even if they’ve moved from another organisation.
Furthermore, sales teams can deploy specialised AI first software that can analyse a stakeholder’s social media activity, published articles, and even speaking engagements to build a comprehensive picture of their interests, challenges, and priorities. This depth of insight allows for more personalised and effective engagement and targeting strategies.
Data-Driven Customer Journeys
Deriving interpretations from Data has never been easier since the advent of large language models. Another area of Account-Based Marketing, where LLMs find application, is creating data-driven customer journeys. This point also ties back to the stakeholder profiling section, where we use the external and internal CRM data to create a buyer persona and decide the next best step in the sales cycle. Stephanie describes these as “AI-driven journey triggers,” which help determine the most relevant and timely experiences to move customers along their buying journey.
The added component here is timing, i.e., where does the current prospect lie in the buying journey?
Marketers can create dynamic, personalised customer journeys based on real-time behaviour and historical data by implementing generative AI.
Key opportunities here include:
- Real-time analysis of customer behaviour (B2B Intent Data and Intent Intelligence)
- Incorporation of historical data and business-specific datasets
- Dynamic adjustment of planned journeys based on new interactions
- Integration of sales and marketing touchpoints
All of the above can be achieved by building internal bespoke solutions or accessed through CRM and marketing automation platforms, which offer a feature set that includes them.
Refining Messaging
This opportunity is the lowest-hanging fruit when looking for efficiencies in your sales and marketing efforts for your ABM program.
Marketers globally are already leveraging AI tools for various purposes, including designing event invitations, developing themes, and implementing company-specific tools that incorporate brand voice and solution details.
Striking a balance between AI-generated content and human creativity and personalisation becomes an important call out. The best approach is one where marketers use AI as a powerful starting point for content creation and idea generation at scale while still relying on human expertise to review, edit, and refine the outputs. By doing so, marketers can add nuance, creativity, and personal touches that AI alone cannot replicate.
Enhanced Measurement and Optimisation
The last opportunity to use generative AI tools is related to performance and metrics measurement. LLMs can analyse campaign performance in real-time using predictive analytics and provide insights for ongoing optimisation and improvement.
Integrated AI analytics tools used with existing marketing automation platforms and CRM systems can optimise content distribution, streamline campaign management, and ensure seamless collaboration between AI-driven insights and existing marketing processes.
The key use cases here are as follows:
- Analysing which touchpoints have the strongest conversion to revenue
- Identifying the most effective offers and engagements
- Monitoring pipeline generation and progression
- Calculating return on investment (ROI) for different marketing activities
The focus is on metrics, full-funnel conversion rates, and ROI. AI can help identify which touchpoints and offers drive the highest conversions and the best ROI, allowing marketers to refine their strategies for optimal results continuously.
Implementing AI in Your ABM Strategy: Best Practices
So far, we have discussed the key areas within Account-Based Marketing where generative AI technology can be utilised. However, the million-dollar question still remains: How can a B2B marketer get started with the adoption of AI in their organisation beyond content creation? Let's answer this question for you.
1. Start With Clear Goals
Define specific objectives for incorporating AI into your ABM efforts. This could include improving account targeting, personalizing content at scale, or predicting customer behaviour. Having clear goals will help guide your AI implementation and measure its success.
2. Ensure Data Quality
AI models are only as good as the data they have access to. Invest time in cleaning, organising, and maintaining your customer and account data. Implement processes to continuously validate and update data to ensure AI insights remain accurate and relevant.
3. Choose the Right Tools
Select AI-powered ABM platforms that integrate seamlessly with your existing tech stack. Look for solutions that offer features aligned with your goals, such as predictive analytics, automated personalisation, or intent data analysis.
4. Train Your Team
Ensure your marketing and sales teams understand how to leverage AI insights effectively. Provide training on interpreting AI-generated recommendations and using them to inform strategies. Foster a data-driven culture where teams feel comfortable working alongside AI tools.
5. Maintain a Human Touch
While AI can provide valuable insights and automate certain tasks, don't lose sight of the importance of human creativity and relationship-building. Use AI to inform and enhance your strategies, but allow human judgment to guide final decisions and maintain authentic connections with target accounts.
6. Start Small and Scale
Begin by implementing AI in one area of your ABM strategy, such as account selection or content personalization. As you gain experience and see results, gradually expand AI usage to other aspects of your program.
7. Continuously Monitor and Refine
Regularly assess the performance of your AI-driven ABM initiatives. Use A/B testing to compare AI-powered approaches with traditional methods. Be prepared to adjust your strategies based on results and feedback.
8. Focus on Actionable Insights
Configure your AI tools to provide actionable recommendations rather than just raw data. Ensure insights are presented in a way that enables your team to quickly understand and act upon them in their ABM campaigns.
By following these best practices, you can effectively leverage AI to enhance your ABM strategy, improving targeting precision, personalisation, and overall campaign performance while maintaining the critical human elements of B2B marketing.
Conclusion: Balancing AI and Human Creativity in ABM
This article has been fascinating to write. It is all about AI in ABM, but you might have seen a recurring theme sprinkled across all sections. Traditional marketing methods often struggle with achieving greater personalisation at scale, which is addressed by the use of AI, but human creativity and strategic thinking remain essential in ABM.
Several areas where human input is crucial include:
- Creative design and messaging
- Strategic thinking and brainstorming
- Collaboration with other organisations
- Ensuring authenticity and personalisation in communications
- Interpreting and acting on AI-generated insights
Leveraging generative AI in ABM can personalise content for individual accounts, gain insights from extensive datasets, streamline content creation processes, and predict future behaviours and preferences, but the key is to use AI to augment human intelligence, not replace it.
AI can provide data-driven insights and automate repetitive tasks, allowing marketers to focus on strategy and creativity and build genuine relationships with target accounts. As we look to the future, one thing is clear: AI will continue to play an increasingly important role in shaping the landscape of B2B marketing. By embracing this technology and learning how to leverage it effectively, marketers can stay ahead of the curve and drive unprecedented results for their organisations.
And just to reiterate, the key lies in viewing AI not as artificial intelligence but as "augmenting intelligence" – a powerful tool that enhances and amplifies human expertise in the complex world of Account-Based Marketing. If you are looking for guidance on your next ABM campaign. Contact us below.
