ABM with AI and Data

Prakriti Rashi (Growth Marketing Manager at Radial Path )

In an era where AI can write content and chatbots can qualify leads, what's left for human B2B marketers to do? Quite a lot, as it turns out. B2B marketing is evolving, yes but the core principles of delivering value and building meaningful connections remain unchanged.

Precision Targeting with Intent Data

Understanding Intent Data: Intent data refers to the behavioural signals that indicate a prospect's interest in a particular product, service, or solution. These signals can be derived from a variety of sources, including website visits, content downloads, search queries, and social media interactions.

Technical Application: For effective utilisation, marketers must first integrate intent data into their Customer Relationship Management (CRM) systems or marketing automation platforms. This integration allows for real-time data processing and actionable insights.

1. Sales Applications: Intent data can be used to identify at-risk accounts by monitoring engagement levels. By setting up triggers within the CRM, sales teams can be alerted when a key account shows signs of disengagement, enabling proactive outreach. Similarly, intent data can identify upsell and cross-sell opportunities by analysing product interest patterns across existing accounts.
2. Marketing Applications: On the marketing side, intent data can enhance audience segmentation. By using machine learning algorithms, marketers can classify prospects into segments based on their behavioural data. These segments can then be targeted with personalised content, such as tailored email campaigns or dynamic website content that reflects the user’s interests and position in the buying journey.

Actionable Insight: The real value of intent data lies in its application. Marketers should focus on building automated workflows that respond to intent signals. For instance, if a prospect frequently visits a product page but hasn’t converted, a trigger can automatically send them a personalised offer or a case study that addresses their specific concerns.

AI Beyond Automation

AI-Driven Content Optimisation: AI is transforming content creation and optimisation, making it possible to scale content production without sacrificing quality. Tools like natural language processing (NLP) algorithms can analyse large datasets, such as webinars or white papers, and extract key insights to create multiple content formats.

Technical Implementation:

1. Content Atomisation: Marketers can leverage AI to atomise long-form content into smaller, more targeted pieces. For example, an AI-driven tool could identify key themes in a webinar and automatically generate blog posts, social media updates, and email snippets that align with those themes. This not only maximises the content’s reach but also ensures consistency across different platforms.
2. Predictive Analytics for Content Strategy: AI can analyse past content performance to predict which topics, formats, and distribution channels are likely to generate the most engagement in the future. This data-driven approach allows marketers to refine their content strategies continually, focusing resources on the most impactful areas.

Enhancing Chat Technology: Chatbots powered by AI are becoming essential tools in B2B marketing, providing 24/7 engagement and personalised interactions based on real-time data analysis.

Technical Implementation:

1. Integrating AI Chatbots with CRM: By integrating AI chatbots with the CRM system, businesses can create a seamless flow of information between customer interactions and sales data. For example, if a chatbot conversation reveals that a prospect is interested in a specific product feature, this information can be automatically logged in the CRM and passed on to the sales team for follow-up.
2. Real-Time Personalisation: Advanced AI chatbots use natural language understanding (NLU) to interpret user queries and provide contextually relevant responses. By analysing a user’s behaviour on the website, such as pages visited or content consumed, the chatbot can tailor its responses and suggest next steps that are most likely to convert the visitor into a lead.

Advanced Attribution Modelling:

Challenges in Attribution: Attribution modelling in B2B marketing is complex due to the typically long sales cycles and multiple touch points involved in the buying process. Traditional models like first-touch or last-touch attribution often fail to capture the full picture, leading to misinformed marketing decisions.

Technical Solutions:

1. Multi-Touch Attribution (MTA): Implementing MTA allows marketers to assign value to each touchpoint along the customer journey, providing a more accurate reflection of the marketing activities that contribute to conversions. This model requires the integration of multiple data sources, including CRM, marketing automation platforms, and ad analytics tools.
2. Algorithmic Attribution: For a more sophisticated approach, marketers can use machine learning algorithms to develop custom attribution models. These models can analyse historical data to determine the weight of each touchpoint, adjusting dynamically as more data becomes available. This approach provides a granular view of the customer journey, helping marketers optimise their strategies based on empirical evidence.

Key Considerations: While setting up advanced attribution models, it’s crucial to regularly validate and recalibrate the models against actual business outcomes. This ensures that the attribution data remains aligned with the overall marketing and sales objectives.

Bridging Sales and Marketing: Data-Driven Alignment

Shared Dashboards and Data Transparency: The integration of sales and marketing efforts is critical for achieving business goals. Shared dashboards facilitate this alignment by providing both teams with real-time access to key metrics.

Technical Setup:

1. Unified Data Warehousing: By consolidating data from disparate sources into a unified data warehouse, organisations can create a single source of truth. This ensures that both sales and marketing teams are working with the same data, reducing discrepancies and fostering collaboration.
2. Custom Reporting Dashboards: Using business intelligence (BI) tools, marketers can create custom dashboards that track KPIs relevant to both sales and marketing. For instance, a dashboard might include metrics such as lead velocity, conversion rates, and customer acquisition costs. These dashboards should be updated in real-time and accessible to both teams to ensure continuous alignment.

Collaborative Analysis: Regular joint meetings to analyse dashboard data are essential. During these sessions, both teams can discuss performance trends, identify areas for improvement, and adjust strategies accordingly. This ongoing collaboration ensures that both sales and marketing are aligned in their efforts to drive revenue.

Enabling Self-Service in B2B Buying

The Shift to Self-Service: B2B buyers are increasingly seeking out self-service options that allow them to research and evaluate products independently before engaging with sales teams.

Technical Implementation:

1. Interactive Content: Implementing interactive tools like product configurators, ROI calculators, and virtual demos can provide prospects with the information they need to make informed decisions. These tools should be integrated into the company’s website and accessible without requiring extensive lead capture forms.
2. Content Management Systems (CMS) Integration: To support self-service initiatives, marketers should ensure that their CMS is capable of delivering personalised content experiences. By leveraging data from intent signals and CRM integrations, the CMS can dynamically display content that aligns with the user’s interests and stage in the buying process.

Gating Strategies: Reevaluate the need for gating all content. While some high-value assets may still require lead capture, consider offering other content ungated to facilitate the self-service experience. This approach can reduce friction in the buyer’s journey, increasing the likelihood of conversion.

Optimising Account-Based Marketing (ABM) Segmentation

Data-Driven Segmentation: Effective ABM relies on precise segmentation, allowing marketers to tailor their approach to the specific needs and characteristics of each account.

Technical Implementation:

1. Data Enrichment: Before segmenting accounts, it’s important to enrich your data with external sources. This could include firmographic data, technographic data, and intent data. By integrating this enriched data into your CRM, you can create more accurate and actionable segments.
2. Advanced Segmentation Techniques: Utilise clustering algorithms and predictive modelling to identify commonalities among accounts. These techniques can reveal hidden patterns and group similar accounts into cohorts that can be targeted with specific marketing campaigns.

Personalisation at Scale: For top-tier accounts, a one-to-one marketing approach is necessary, involving highly customised content and interactions. For other segments, focus on creating content that addresses common pain points or objectives shared by multiple accounts. This approach allows for personalisation at scale, maximising the impact of your ABM efforts without overwhelming resources.

Want to know more on how to maximise your ABM strategy? Read our blog: Marketing That Works: ABM for Digital Infrastructure

Striking the right balance in effectively utilising AI and data for ABM content can be challenging for marketers. Yet, by leveraging the right AI tools to generate content and data-driven strategies, marketers can further optimise and tailor their content to deliver value for their target audience.

Need help finding your perfect ABM strategy? Say hello at?[email protected]?or send us a message on our website:?https://www.radialpath.com/contact.