Imagine you’re leading a B2B tech company ready to take a revolutionary product to market. You’ve done your homework, gathered your team, and mapped out your ABM strategy. The target accounts have been selected, the campaigns are in motion, and yet… something isn’t clicking. The accounts you thought were primed and ready aren’t engaging as expected. Opportunities aren’t being created, your pipeline and revenue projections aren’t being met. It’s a familiar story, even for seasoned ABM practitioners.
So, what went wrong with your ABM program?
In the past, target account selection often meant casting a wide net. You’d identify companies based on firmographics, market segmentation, and possibly some form of intent data. However, this approach is rapidly becoming outdated in today’s data-driven world of marketing. The reality is that the rules of the revenue game have become more refined—and so must your strategy.
Welcome to ABM 101 for the evolved B2B player,
where we dive deep into how to take target account selection from guesswork to science powered by AI-first solutions that promise to elevate your ABM efforts.
Step 1: Define Your Ideal Customer Profile (ICP)
Your Ideal Customer Profile (ICP) forms the foundation of your ABM strategy. An ICP outlines the characteristics of companies that would benefit the most from your solutions and offer the highest value in return. When defining your ICP, it’s important to involve both the marketing and sales teams, as well as Customer Success. This alignment ensures that everyone is on the same page and targeting the right companies.
Consider factors like:
- What industries are your current high-value customers in?
- Can attributes of your past opportunity accounts form the basis of your ICP definition?
- What new markets should you focus on?
- Which accounts will deliver the most strategic value?
Once your ICP is clearly defined, you’re ready to move to the next phase: leveraging data.
Step 2: Leverage Firmographic and Technographic Data
With your ICP in place, start by looking at firmographic and technographic data:
Firmographic data includes attributes like company size, number of employees, industry, and growth potential. For example, a Fortune 500 company with a rising number of branches is more likely to offer significant growth opportunities.
Technographic data relates to the technology stack that a company uses. Knowing whether a target account is already using complementary or competitive technologies can help refine your outreach.
These data types help you define your target accounts with precision.
Step 3: Leverage AI-First ABM for buying signals
Here’s where AI-first ABM platforms, such as BambooBox ItWorks™, step in. AI goes beyond static data to dynamically adjust your target account list in real time based on the latest information, including buying signals.
Intent data: AI can analyze behaviours across channels to predict future actions and offer insights into which accounts are actively in-market for your solution. For example, if key decision-makers in a company are frequently searching for topics related to your service, AI-powered platforms like BambooBox ItWorks™ can pick up these signals and alert you.
Engagement data: While intent data helps you understand potential interest, engagement data helps you verify your audience’s interest in your brand or offerings similar to yours. AI-first solutions aggregate interactions across touchpoints (web visits, downloads, webinars) and provide a 360-degree view, helping you determine the likelihood of conversion.
Step 4: Apply predictive analytics
AI’s true power lies in predictive analytics, which takes your ICP, firmographics, technographics, intent data, and engagement data and uses machine learning algorithms to predict which accounts are most likely to convert. Predictive models can suggest target accounts that closely resemble your best customers by analyzing patterns from past successful deals. This helps you shorten the sales cycle and allocate resources efficiently, ensuring that you invest in accounts with the highest likelihood of success.
Step 5: Use lookalike models and multi-dimensional scoring
AI-first platforms allow you to leverage lookalike modelling. This method helps you find accounts that resemble your best existing customers. Coupled with multi-dimensional scoring, which evaluates a broader range of factors (beyond revenue and industry), AI provides a more nuanced and accurate understanding of every account’s potential. BambooBox ItWorks™ excels at this, offering sophisticated models that take your ABM strategy to the next level.
BambooBox ITWorks™: The Future of ABM
This is where BambooBox ITWorks™ comes into play. BambooBox takes the evolution of ABM to the next level, offering an AI-first platform that seamlessly integrates these advanced capabilities into your existing tech stack.
With BambooBox ITWorks™, you’re not just selecting target accounts; you’re identifying the right accounts, at the right time, with the right message. The platform’s ability to deanonymize, track engagement holistically, and provide multi-dimensional scoring ensures that your ABM strategy is not only effective but also efficient.
In the past, a tech company targeting Fortune 500 companies might have focused on all companies in the software industry with a certain revenue threshold. But with BambooBox ItWorks™, that same company can now identify which Fortune 500 companies are actively searching for solutions like theirs, understand their unique pain points, and engage them at precisely the right moment.
