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Writer's pictureDavid Slavich

How AI Is Transforming GTM Precision and Effectiveness in B2B



1. Background


As a go-to-market leader, you know the challenges of prioritizing finite resources on the highest-value opportunities at scale. At iCustomer, we've developed a revolutionary AI-powered Decision Intelligence platform to automate this critical prioritization with hyper-dimensional precision. This groundbreaking engine not just build a unified data model of your entire GTM across system, process, team but also puts data quality on auto-pilot while using AI to laser-focus your efforts on execution and focusing on the right buyers at the right time.


2. Building the Unified Data Model or foundation


Our AI builds a multi-faceted model of your target market, ideal customer profiles (ICPs), and buyer personas by mapping the deep relationships between:


Accounts - Full constellation of fit factors like industry, revenue, tech stack, growth trajectory, and more. 


Personas - Roles, responsibilities, seniority, goals, behavioral traits, and other psychographic attributes of key buyers.

 

Triggers  - Dynamic events like funding, hiring, product updates, past champions joining a new account, renewals, and marketing interactions signaling prime pursuit periods.


Signals - Specific people level activities your potential buyers exhibit within your funnel, channel or dark funnel i.e social, media, third party intent signals including lead form or web activity or community post or questions


Our proprietary data taxonomy defines the intricate ontology of how these account, persona, and situational elements interrelate based on empirical buying patterns.



3. The Old Model 


Whether in sales, marketing, or revenue operations, chances are you've attempted to leverage these kinds of signals in the Web 2.0 world. But that meant laborious manual data extraction and integration which was inflexible and overly dependent on static firmographic attributes - failing to capture dynamic market realities. We sucked at having - or simply failed to utilize - rich situational and timing data.  


Some rigid ICP models like BANT or MEDDIC fall short by ignoring each customer's unique nuances. They lack insight into real-time situations and signals. Fragmented sales and marketing efforts follow generalized, context-blind approaches, failing to capitalize on AI and live data. This severely limits outreach effectiveness and pipeline development.


4. AI-Powered Hyper-Precision 


Our solution automates hyper-precise prioritization through four core capabilities:


Dynamic Enrichment or Auto Tags- Algorithms proactively identify, collate, and enhance data from your tech stack, web sources, partners, and multichannel interactions on people and accounts.  


AI-Led Segmentation - Machine learning models ingest the enriched data to define your ideal customer profile with granular multi-dimensional precision.  


Intent-Driven Targeting - With an unmatched ICP understanding, AI can pinpoint prime buying situations by recognizing real-time intent signals across accounts and buyers.


Intelligent Activation - Predicted opportunities are automatically prioritized and delivered to your sales/marketing systems for laser-focused pursuit.


5. Unleash Transformative Plays


With precision targeting on auto-pilot, you can unleash powerful new GTM plays and convert more pipeline by engaging the right buyers with the right messages at the right times:


- Hyper-customized account-based plays triggered by funding, hiring, product changes, renewals, and more

- Granular buying team orchestration with unique, personalized outreach per stakeholder

- Accelerated competitive takeouts by capitalizing on emerging churn situations  

- Insight-driven expansion/adoption plays surfacing underutilized solutions to receptive customers


The future of intelligent GTM is hyper-focused, intelligently timed engagement. With iCustomer's innovative GTM Intelligence engine, you can surgically pursue the ripest opportunities and maximize conversion rates like never before.


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