Time Series
Historical trends and changes in technology adoption over time
What is it
Time Series data provides HISTORICAL VIEWS of technology installations — not just what a company uses today, but what they've added, removed, or changed over time. This includes install dates, removal dates, intensity trends, and technology adoption trajectories.
Coverage spans multiple years of historical snapshots, enabling trend analysis and change detection.
What problem it solves
Point-in-time data misses the story. Did they just add that product? Are they abandoning a competitor? Is their tech stack growing or shrinking? Time Series answers: "What changed, and when?" — turning static snapshots into dynamic intelligence.
What you can do with it
- New adoption alerts: Trigger outreach when a company adds a relevant product
- Churn detection: Identify when customers remove your competitor (opportunity)
- Technology trajectory: Is a company modernizing or stagnating?
- Seasonal patterns: Understand when companies typically buy in your category
- Market trend analysis: Track adoption curves for technologies over time
Real-world example
Competitive Churn Targeting
A CRM vendor monitors Time Series for accounts that recently removed a competitor's product. When Company X drops their legacy CRM (detected via Time Series), the sales team reaches out within 48 hours with a migration offer. The timing is perfect — the decision to change is already made. Win rate: 5x higher than cold outreach.
What the data looks like
Time Series Technology Changes
PRODUCT ADDITION EVENTS
| Date | Product Added | Vendor | Category | Signal |
|---|---|---|---|---|
| 2026-03-15 | Snowflake | Snowflake | Data WH | NEW_INSTALL |
| 2025-11-22 | Databricks | Databricks | ML Plat | NEW_INSTALL |
| 2025-08-10 | Terraform | HashiCorp | IaC | NEW_INSTALL |
| 2025-03-05 | Kubernetes | CNCF | Container | NEW_INSTALL |
| 2024-10-18 | AWS SageMaker | Amazon | ML Plat | NEW_INSTALL |
PRODUCT REMOVAL EVENTS
| Date | Product Removed | Vendor | Category | Signal |
|---|---|---|---|---|
| 2026-02-01 | Oracle Database (on-prem) | Oracle | Database | REMOVED |
| 2025-10-12 | Tableau | Salesforce | BI | REMOVED |
| 2025-04-20 | Jenkins | Open Src | CI/CD | REMOVED |
INTENSITY TREND ANALYSIS
- Product: Kubernetes
| Date | Intensity | Change | Trend |
|---|---|---|---|
| 2024-Q1 | 0 | N/A | Not detected |
| 2024-Q2 | 0 | N/A | Not detected |
| 2024-Q3 | 0 | N/A | Not detected |
| 2024-Q4 | 0 | N/A | Not detected |
| 2025-Q1 | 45 | +45 | ▲ New adoption |
| 2025-Q2 | 128 | +83 | ▲▲ Rapid growth |
| 2025-Q3 | 267 | +139 | ▲▲▲ Accelerating |
| 2025-Q4 | 412 | +145 | ▲▲ Strong growth |
| 2026-Q1 | 589 | +177 | ▲▲ Scaling deployment |
| 2026-Q2 | 678 | +89 | ▲ Maturing (slower growth) |
TECHNOLOGY MIGRATION PATTERN DETECTED
Old Stack → New Stack Transition
- Oracle DB (on-prem) → Snowflake (cloud data warehouse)
- Jenkins → (likely GitLab CI or GitHub Actions — check for new CI/CD signals)
- Tableau → (check for new BI tool adoption)
- Modernization Trajectory: Legacy → Cloud-Native
- Timeline: 18 months (2025-Q1 to 2026-Q2)
- Pattern: Lift-and-shift to cloud + cloud-native tooling
Key fields you get
- Product addition events: Date, product, category, intensity at addition
- Product removal events: Date, product, last detected intensity
- Intensity trends: Historical intensity scores over time (quarterly/monthly)
- Change velocity: Rate of intensity growth/decline
- Migration patterns: Old product → new product transitions
- Technology trajectory: Overall stack modernization direction
“GlobalTech is in active cloud modernization mode. They added Snowflake, Databricks, Terraform, and Kubernetes over the past 18 months, while removing Oracle on-prem and Jenkins. Kubernetes adoption is accelerating (45 → 678 intensity in 6 quarters). This company is a hot prospect for cloud-native tooling, DevOps platforms, and observability solutions. Strike while they're in migration mode — timing is perfect.”
Business impact
Increases: Higher win rates on “just removed competitor” signals
Increases: More engagement from trigger-based outreach vs. static lists
Increases: Earlier detection of expansion opportunities at growing accounts
Decreases: Lower churn risk when stack contraction is caught early
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 technographic providers only show current state. HG maintains historical snapshots enabling true trend analysis. We can show when a product was first detected, when it was removed, and how intensity has changed — critical for timing-based sales motions.
How it's derived
Time Series data is generated by maintaining historical snapshots of technographic detections. Each refresh cycle is compared against prior snapshots to identify additions, removals, and intensity changes. Historical data is preserved and queryable, enabling trend analysis across multi-year timeframes.