Audiences: Artificial Intelligence and Machine Learning (AI/ML)

TAM (Total Addressable Market): Total potential demand, usually the whole category
ICP (Ideal Customer Profile): Firmographic & Technographic Attributes of your best customers
TAL (Target Account List: Data driven and Tiered-Out by (1) High Fit + (2) High Intent
Audience Segments: 1, 2, 3, and 4 …TAL broken down by (a) Industry and (b) Buyer Stage

Ideal Customer Profile (ICP) for AI and Machine Learning Audiences:

Firmographic Attributes:

Industry: Industries likely to benefit from AI/ML include Technology, Healthcare, Finance, Retail, Manufacturing, and E-commerce.
Company Size: Mid-to-large enterprises (500+ employees) that are likely to have the resources to invest in AI/ML solutions.
Revenue: Companies with annual revenues above $50M+ are better positioned to invest in these advanced technologies.

Technographic Attributes:

Current Tech Stack: Companies using analytics tools, cloud platforms (AWS, Google Cloud, Azure), or business intelligence platforms are more likely to adopt AI/ML.
Automation Usage: Organizations already adopting automation solutions or having data science teams in place.
Existing CRM and Data Infrastructure: Use of CRMs like Salesforce or marketing automation platforms like HubSpot, Marketo, or similar tools signals that they’re data-driven and ready for AI/ML solutions.

Finding Patterns:

One great source for ICP can often be the patterns in your existing customers and existing website visitors.
Based on the data you've gathered, consider asking the following questions:
- Are there particular company sizes or industries that dominate your closed-won accounts? Does anything about this data stand out or surprise you?
- Do your top-performing accounts consistently fall into certain categories? If patterns aren’t clear, it might be helpful to collaborate with an ABM partner for a deeper analysis.
- Can you identify the leading categories? What insights or trends emerge from this information?

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Target Account List (TAL) for AI/ML:

Static and Dynamic Signals:

Score a TAL based on FIT: Company size, industry, and region. For instance, AI adoption is prevalent in sectors like tech, finance, and healthcare.
Intent Signals: Use intent data to find companies that are researching AI/ML topics, attending relevant conferences, or consuming content related to AI.
Engagement Signals: Layer on signals from accounts engaging with your website and sales team.

We can use tools like IntentStream, LinkedIn Sales Navigator, or other ABM platforms to build the TAL based on these criteria. Once built, we should tier the accounts for prioritization.

Tier 1: High-fit, high-intent accounts (e.g., large enterprises already exploring AI/ML solutions).
Tier 2: Medium-fit accounts, maybe newer to AI but have data infrastructure in place.
Tier 3: Lower-fit, or smaller accounts that could still potentially benefit from AI/ML.

Do you have a partner that can help you pull this list of unknown accounts into a list?

Sub-Segmenting AI/ML Audience Examples:

Industry-Specific Campaigns: Create sub-segments for AI/ML in Finance, AI/ML in Healthcare, AI/ML in Retail, etc.
Intent-Based Segmentation: Companies showing high intent in AI topics such as predictive analytics, natural language processing (NLP), or machine learning algorithms.
Stage-Based Segmentation: Companies in early awareness stage vs. companies already in evaluation for an AI/ML vendor.

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Activation and Campaign Strategy:

Activation and Campaign Strategy

Now that we have our sub-segments, we can activate them with targeted messaging and channel-specific campaigns.

Channel: Multi-channel activation might include digital ads, content syndication, webinars, and sales outreach.
Budget Allocation: High-intent, high-fit accounts should get more budget for personalized, high-touch strategies like account-based advertising and direct sales outreach.
Offerings: Tailor offers to the audience. For example:
- Early-stage: Educational content like eBooks, whitepapers, or webinars on AI/ML trends.
- Mid-stage: Case studies and solution demos.
- Late-stage: Vendor comparison guides, ROI calculators, sales presentations.

Example:

Sub-segment: AI in Finance - high-intent + digital ads + free whitepaper download on AI/ML for Risk Management.

Right-Sizing Your TAL
Using a TAL building tool or media partner, you can right-size your TAL by determining how many accounts your marketing and sales teams can realistically target based on conversion rates, revenue goals, and available resources.

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Title Strategy:

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Ideal Customer Profile (ICP) for AI and Machine Learning Audiences:

Ideal Customer Profile (ICP) for AI and Machine Learning Audiences:

Ideal Customer Profile (ICP) for AI and Machine Learning Audiences:

Firmographic Attributes:

Industry: Industries likely to benefit from AI/ML include Technology, Healthcare, Finance, Retail, Manufacturing, and E-commerce.

Company Size: Mid-to-large enterprises (500+ employees) that are likely to have the resources to invest in AI/ML solutions.

Revenue: Companies with annual revenues above $50M+ are better positioned to invest in these advanced technologies.

Technographic Attributes:

Current Tech Stack: Companies using analytics tools, cloud platforms (AWS, Google Cloud, Azure), or business intelligence platforms are more likely to adopt AI/ML.

Automation Usage: Organizations already adopting automation solutions or having data science teams in place.

Existing CRM and Data Infrastructure: Use of CRMs like Salesforce or marketing automation platforms like HubSpot, Marketo, or similar tools signals that they’re data-driven and ready for AI/ML solutions.

Finding Patterns:

One great source for ICP can often be the patterns in your existing customers and existing website visitors.

Based on the data you've gathered, consider asking the following questions:

Are there particular company sizes or industries that dominate your closed-won accounts? Does anything about this data stand out or surprise you?

Do your top-performing accounts consistently fall into certain categories? If patterns aren’t clear, it might be helpful to collaborate with an ABM partner for a deeper analysis.

Can you identify the leading categories? What insights or trends emerge from this information?

Target Account List (TAL) for AI/ML:

Target Account List (TAL) for AI/ML:

Target Account List (TAL) for AI/ML:

Static and Dynamic Signals:

Score a TAL based on FIT: Company size, industry, and region. For instance, AI adoption is prevalent in sectors like tech, finance, and healthcare.

Intent Signals: Use intent data to find companies that are researching AI/ML topics, attending relevant conferences, or consuming content related to AI.

We can use tools like IntentStream, LinkedIn Sales Navigator, or other ABM platforms to build the TAL based on these criteria. Once built, we should tier the accounts for prioritization.

Tier 1: High-fit, high-intent accounts (e.g., large enterprises already exploring AI/ML solutions).

Tier 2: Medium-fit accounts, maybe newer to AI but have data infrastructure in place.

Tier 3: Lower-fit, or smaller accounts that could still potentially benefit from AI/ML.

Sub-Segmenting AI/ML Audience Examples:

Industry-Specific Campaigns: Create sub-segments for AI/ML in Finance, AI/ML in Healthcare, AI/ML in Retail, etc.

Intent-Based Segmentation: Companies showing high intent in AI topics such as predictive analytics, natural language processing (NLP), or machine learning algorithms.

Stage-Based Segmentation: Companies in early awareness stage vs. companies already in evaluation for an AI/ML vendor.

Activation and Campaign Strategy:

Activation and Campaign Strategy:

Activation and Campaign Strategy:

Activation and Campaign Strategy

Now that we have our sub-segments, we can activate them with targeted messaging and channel-specific campaigns.

Channel: Multi-channel activation might include digital ads, content syndication, webinars, and sales outreach.

Budget Allocation: High-intent, high-fit accounts should get more budget for personalized, high-touch strategies like account-based advertising and direct sales outreach.

Offerings: Tailor offers to the audience. For example:

Early-stage: Educational content like eBooks, whitepapers, or webinars on AI/ML trends.

Mid-stage: Case studies and solution demos.

Late-stage: Vendor comparison guides, ROI calculators, sales presentations.

Example:

Sub-segment: AI in Finance - high-intent + digital ads + free whitepaper download on AI/ML for Risk Management.

Right-Sizing Your TALUsing a TAL building tool or media partner, you can right-size your TAL by determining how many accounts your marketing and sales teams can realistically target based on conversion rates, revenue goals, and available resources.

Title Strategy:

WITH ROLES LIKE: -Growth Marketing -Revenue Operations -Demand Generation -Enterprise Sales -Digital Marketing -CMO -Sales Operations

Right-Sizing Your TAL
Using a TAL building tool or media partner, you can right-size your TAL by determining how many accounts your marketing and sales teams can realistically target based on conversion rates, revenue goals, and available resources.

WITH ROLES LIKE:
-Growth Marketing
-Revenue Operations
-Demand Generation
-Enterprise Sales
-Digital Marketing
-CMO
-Sales Operations