Contextual Intent
Third-party intent interpreted against the company's actual tech stack
What is it
Contextual Intent is third-party buyer intent that HG links to the technologies a company already has in place. Instead of showing only that an account is surging on a topic, HG interprets the signal based on what the company is currently running.
If the product being researched is already installed, the signal is classified as Expansion. If the company runs a competing product, it's labeled Displacement. If the company has nothing installed in that category, the signal is Whitespace.
Contextual Intent also identifies the buyer's journey stage: topic-level interest is classified as Research, while views of product, demo, or pricing pages are tagged as Evaluation.
What problem it solves
Standard intent feeds tell you an account is active on a topic and stop there. Teams are left guessing whether that activity is a renewal, a competitive threat, or a brand-new category — so reps chase noise and messaging misses.
Contextual Intent removes the guesswork by resolving each signal against verified installs, so you know what the activity actually means before anyone picks up the phone.
What you can do with it
- Prioritize accounts based on whether a signal represents expansion, displacement, or whitespace
- Match outreach to buyer journey stage by distinguishing Research from Evaluation activity
- Focus on accounts with realistic buying scenarios rather than noise
- Discover companies showing intent in categories where they have no installations
- Time outreach based on when accounts shift from high-level research to product evaluation
- Catch churn risk early: spot customers researching competitors in a category you already sell them
Real-world example
Turning a surge into a displacement play
A data platform vendor sees 47 target accounts surging on "cloud data warehouse." Rather than blasting all 47, they split the list by context: 12 already run the competitor (Displacement), 9 have nothing installed in the category (Whitespace), and 26 already own an adjacent product from the vendor (Expansion).
The Displacement accounts get a competitive teardown, Whitespace gets an education-led sequence, and Expansion goes to the account team. Same intent feed — three different plays, and each rep knows why they're calling.
Example Data
Intent activity — as it appears in the HG platform
| Account | Signal strength | Topic | Context | Buyer's journey | Location | Location strength | Datestamp |
|---|---|---|---|---|---|---|---|
| Walmart Inc. | 76 | Databricks | Expansion, Displacement | Evaluating | New York City, NY, United States | 83 | 2026-07-18 |
| Walmart Inc. | 69 | Snowflake | Displacement | Evaluating | Bentonville, AR, United States | 88 | 2026-06-27 |
| Cedarline Foods | 81 | Cloud data warehouse | Displacement | Evaluating | Chicago, IL, United States | 74 | 2026-08-05 |
| Northwind Manufacturing | 54 | Warehouse automation | Whitespace | Researching | Columbus, OH, United States | 46 | 2026-08-04 |
| Brightpath Logistics | 63 | CrowdStrike | Displacement | Researching | Dallas, TX, United States | 58 | 2026-08-02 |
| Meridian Financial | 88 | Observability platforms | Whitespace | Evaluating | Charlotte, NC, United States | 79 | 2026-08-01 |
| Context | Meaning | What to do |
|---|---|---|
| Displacement | Researching or evaluating a product where we already detect an install of a competitive product from a different vendor | Lead with a competitive teardown and migration path |
| Whitespace | Researching or evaluating a product or product category we don't detect as currently installed | Educate first — they're defining requirements, not comparing vendors |
| Complementary | Researching or evaluating a complementary product or product category | Position the fit alongside what they already run |
| Expansion | Researching or evaluating a product or category where we already detect an install from the same vendor associated with the intent signal | Route to the account team — upsell, seats, or adjacent modules |
“Same surge, six different plays. Walmart is Evaluating Databricks at signal strength 76 and Snowflake at 69 — both flagged Displacement, which is a churn signal if they bought that category from you. Cedarline is Evaluating a cloud data warehouse while running a competitor. Meridian is at 88 with nothing installed: real whitespace, moving fast. Brightpath is still Researching, so it's education, not a bake-off. Without the Context column derived from installs, all six look identical.”
Key fields you get
- Account: resolved to the HG Company ID so intent joins to every other data type
- Signal strength: 0–100 score for how strong the research activity is on that topic
- Topic: the category or specific vendor product being researched
- Context: Displacement, Whitespace, Complementary, or Expansion — derived from the company's verified installs
- Buyer's journey: Researching (topic-level interest) vs. Evaluating (product, demo, or pricing pages)
- Location and location strength: where the activity is coming from and how concentrated it is
- Datestamp: when the activity occurred, for recency and trend
Business impact
Increases: Higher meeting rates when outreach matches the real buying scenario
Decreases: Less time wasted on intent signals that go nowhere
Increases: More competitive displacement opportunities identified
Increases: Better pipeline quality from context-qualified accounts
Decreases: Lower churn risk by catching at-risk customers before renewal
Directional outcomes HG customers commonly report. Actual results vary by program and data application.
Under the hood
How it's unique to the market
Most intent tools — Bombora, bidstream providers, and other third-party feeds — show only that an account is surging on a topic, leaving teams unsure what the signal means.
HG layers each intent signal on top of a company's verified tech stack. Because we already know which products the company has installed, and which competitor products they run, we can classify the activity as expansion, competitive displacement, or true whitespace — instead of handing you an unclassified signal with no explanation. It's the industry's first solution that contextualizes buyer intent based on a company's tech stack.
How it's derived
Third-party research activity is collected across web content and publisher networks, classified to topics and products, and resolved to a company via domain and IP matching to the HG Company ID.
Each signal is then joined to that company's verified install base. The category of the researched product is compared against what's installed to assign the context type — Expansion, Displacement, or Whitespace — and the page type behind the signal sets the journey stage, Research or Evaluation.