Predictive Account Targeting, Prioritization, and Scoring
Categorize and rank prospect accounts by solution fit, value, and conversion likelihood.
Challenges Sales Leaders Face When Prioritizing Accounts
Challenges
- Wasted Effort on Low-Fit Accounts. Chasing poor-fit leads drains sales resources.
- Sales and Marketing Misalignment. Teams prioritize different accounts creating misalignment and friction.
- Missed Signals and Lost Opportunities. Outdated or inaccurate scoring risks missing real buyer intent.
- Manual, Static Scoring Criteria. Old point-based systems lack accuracy, insights, and nuance.
- Low Prospect Conversion and Weak Sales Productivity. Too few leads convert, driving up cost per win and reducing efficient revenue.
Solutions
- Precision Account Scoring. AI scoring highlights high-fitness accounts for better focus.
- Unified Targeting Model. A consistent, shared data model synching GTM teams on best opportunities.
- Intent-Driven Scoring. Real-time buyer intent signals boost insight and odds of conversion.
- Dynamic Predictive Modeling. Adaptive AI scoring engine evolves over time, using account data for accuracy.
- High-Propensity Filtering. Filter out low-probability leads, improve sales results and lower cost-of-sales.
Leverage AI to Prioritize Accounts with Deep Account Data
Transform account prioritization by combining technographic intelligence, firmographics, and buyer intent to identify accounts with the highest conversion potential.
Focus on Likely Buyers
Real-Time Buyer Signals
Unified Team Alignment
Granular Market Insights
Smarter Resource Use
What Are the Key Steps for Predictive Account Targeting, Prioritization, and Scoring?
The platform uses weighting and context to calculate scoring, to know exactly why accounts receive certain scores – including the option to export or connect to your CRM.
Select target accounts and layer-in signals and criteria.
- Connect your CRM and other first-party data sources, such as website activity, downloads, inquiries, to the platform.
- Enrich account and contact data with firmographics, technographics, and spend intelligence.
- Layer-in verified buyer intent signals from research and peer reviews provided by TrustRadius.
Activate and apply predictive scoring criteria.
- Activate predictive scoring and qualification models based on your historical data.
- Dynamically apply predictive scoring using ICP fit and buyer intent signals to rank and segment leads and accounts.
- For example, accounts with >$10M spend + researching “automation platforms” + scored as “Likely to Buy”.
Push leads into CRM and trigger campaigns.
- Push prioritized accounts and leads into CRM, MAP, or sales engagement platforms.
- Automatically trigger campaigns, sales plays, and content offers tailored to active intent signals.
Automated authoring and sequencing of prospecting emails.
- Track pipeline acceleration, conversion rates, and ROI by segment.
- Continuously refine ICP and predictive scoring model based on closed-won data.
Explore Other Use Cases
FAQ: ICP Account Planning and Propensity Modeling
How does HG Insights enable predictive account targeting and prioritization?
HG Insights enables predictive account targeting by combining deep account-level intelligence with AI-driven modeling to identify which companies are most likely to convert.
Instead of relying on static rules or surface-level attributes, HG analyzes firmographics, technographics, IT spend, buying-center context, historical performance, and real-time buyer intent. This allows teams to categorize and rank accounts based on solution fit, value, and likelihood to buy, ensuring GTM teams focus on the opportunities that matter most.
What makes HG Insights’ predictive scoring different from traditional lead or account scoring?
Traditional scoring models rely on fixed point systems and limited data, which quickly become outdated and opaque. HG Insights uses dynamic, AI-driven predictive models that continuously adapt based on account behavior, intent signals, and historical outcomes.
Scoring is explainable and transparent. Teams can understand why an account is ranked highly, which signals contributed to the score, and how changes in behavior affect prioritization. This builds trust across sales, marketing, and RevOps while improving conversion and productivity.
What data goes into HG Insights’ predictive scoring models?
HG’s predictive scoring models incorporate a broad range of first-, second-, and third-party data, including firmographics, technographics, IT spend, technology adoption trends, buying-center insights, and verified buyer intent signals from TrustRadius.
These inputs are enriched with historical performance data and continuously updated signals, allowing models to reflect real market conditions. The result is higher-precision scoring that captures both fit and active buying interest, not just static profile attributes.
How does predictive scoring integrate into our existing systems and workflows?
HG Insights is designed to integrate seamlessly into existing GTM stacks through native integrations and APIs. Predictive scores and prioritized account lists can be delivered directly into CRM, MAP, sales engagement platforms, data warehouses, and analytics tools.
Teams can consume HG data natively within the platform or via APIs to support custom models and workflows. Prioritized accounts and leads can automatically trigger campaigns, sales plays, routing, and reporting, ensuring predictive insights are operationalized without disrupting existing systems.
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