How We Get Our Data
DataStackGuide tracks B2B data tool adoption by analyzing real job postings. Here's how we do it, what it tells you, and where it falls short.
The Data Pipeline
We continuously collect job postings from major job boards and company career pages. Our pipeline works in four stages:
Collection
We scrape and aggregate job postings daily from multiple sources. Each posting captures the job title, company, location, compensation, description, and metadata.
Tool Detection
We scan each posting's description for mentions of 63 B2B data tools across 12 categories. This uses pattern matching with context awareness to avoid false positives (e.g., "Apollo" the tool vs. "Apollo" the mission).
Enrichment
We normalize company names, classify roles by function and seniority, categorize company stages, extract salary data, and identify remote vs. onsite roles.
Analysis
We aggregate the data into adoption rankings, co-occurrence patterns (which tools are used together), salary ranges, trend lines, and company profiles.
Current Dataset
What This Data Tells You
Adoption signals. When a company lists a tool in a job posting, it means they've committed budget, integrated the platform, and are hiring someone to use it. That's a stronger signal than a review or a case study.
Market size comparisons. By counting tool mentions across thousands of postings, we can compare relative adoption. Salesforce appearing in 1,694 postings vs. HubSpot in 432 tells you something about market penetration that no survey can.
Salary benchmarks. We extract compensation data from postings that include salary ranges. This tells you what companies pay people who use specific tools, broken down by seniority and function.
Technology stacks. Co-occurrence data shows which tools are mentioned together. If ZoomInfo and Salesforce appear in the same posting 40 times, those platforms are part of the same stack.
Company profiles. We track which companies are hiring for each tool, their stage (startup to enterprise), and geographic distribution.
Limitations & Caveats
We're transparent about what this data doesn't capture:
Large company bias
Bigger companies post more jobs. A 10,000-person company will generate more postings mentioning Salesforce than a 20-person startup, even though both use the tool. Our data skews toward mid-market and enterprise.
Not all tools appear in postings
Some tools (especially lightweight SaaS products) rarely show up in job descriptions. A company might use Clay or Warmly without ever mentioning it in a job posting. Our data underrepresents tools that aren't considered "skills."
Job postings lag adoption
There's a delay between a company adopting a tool and hiring for it. Fast-growing tools may be underrepresented in our current data but growing in the trend line.
Salary data is incomplete
Not all postings include salary ranges. In states without pay transparency laws, salary data is thinner. We only report salary figures when we have a meaningful sample size.
Category overlap
Many tools span multiple categories. ZoomInfo is enrichment, validation, intent data, and prospecting all at once. We handle this with multi-category tagging, but some categorization is inherently subjective.
Editorial Standards
Every tool page on DataStackGuide includes a disclosure footer noting our relationship with Verum and Provyx. Here's what guides our content:
- We don't accept payment for reviews, rankings, or placements
- Affiliate links don't influence our recommendations
- We apply the same editorial standards to our own products (Verum, Provyx) as to competitors
- Pricing information is verified directly from vendor websites and updated regularly
- We show our data sample sizes so you can judge the statistical significance
Data Updates
We run our data pipeline on a regular schedule. Job postings are collected continuously, and the analysis data on this site is refreshed to reflect the latest trends.
Every page shows a "Last updated" timestamp so you know how current the data is.
Questions?
If you have questions about our methodology, want to report a data error, or think we're missing a tool, email hello@datastackguide.com.