If you’ve ever tried to figure out which companies are actually using certain tech—not just what they say they’re using—you know it’s a minefield. Most “market intelligence” tools are either full of holes or just give you vague charts that don’t mean much. This guide is for people who want to dig into real technology adoption patterns using Hginsights data. Whether you’re in product, marketing, or sales, you’ll walk away with a practical method for turning raw data into useful insight.
No fluff, no buzzwords—just the steps, pitfalls, and honest truth about what’s possible.
Step 1: Get Your Hands on the Right Hginsights Data
Not all Hginsights data is created equal. Depending on your subscription, you’ll get access to different levels of company, technology, and intent data. Before you dive in, make sure you actually have:
- Current tech install data: This is the backbone—lists of companies and the technologies they’re confirmed to use.
- Historical install/removal snapshots: If you’re mapping trends, you’ll need more than a static list. Some versions include quarterly or annual snapshots.
- Geography, firmographics, and hierarchy: For real insights, you’ll want to slice the data by region, industry, and company size.
Pro tip: Don’t just accept what your sales rep says. Ask for a sample export and check it yourself. Some “technology install” data is just scraped web tags or press releases, which is a far cry from verified installs.
Step 2: Define the Questions You Actually Want to Answer
Plenty of teams buy a mountain of data, then stare at it blankly. Be clear (and realistic) about what you’re trying to figure out. Examples:
- Which ERP platforms are gaining/losing market share in North America?
- How fast are companies in healthcare switching from legacy CRM tools to cloud-based ones?
- Are certain vendors being adopted faster by mid-market vs. enterprise companies?
Write these out before you start filtering or analyzing anything. It’ll keep you focused and stop you from drowning in columns.
What to ignore: Don’t try to map “all technology adoption.” That’s a recipe for confusion and noise. Pick a handful of tech vendors or categories that matter to you.
Step 3: Clean and Prep the Data (Don’t Skip This)
Hginsights data is solid, but it’s not magical. You’ll still need to do some scrubbing:
- De-duplicate companies: Watch out for holding companies, subsidiaries, and alternate spellings.
- Standardize tech names: One company’s “MSFT 365” is another’s “Microsoft Office 365.” Group aliases together.
- Time alignment: If you’re using snapshots, make sure your time periods line up. Some datasets have gaps or inconsistent intervals.
- Remove obvious junk: Sometimes, you’ll see “test” accounts, shell companies, or bizarre outliers. It’s fine to delete these.
Pro tip: Keep a log of everything you clean or change. It’ll save you when someone (usually your boss) asks, “Why does this number look different from before?”
Step 4: Map Adoption Over Time
Now for the good stuff: actually tracking who’s adopting what, and when.
If You Have Historical Snapshots
- For each time period (quarter, year, etc.), count the number of unique companies with a given technology.
- Calculate the net change: How many added, how many dropped, and what’s the total?
- Plot these numbers. You’ll see real adoption curves—spikes, plateaus, or slow burns.
If You Only Have Current Data
- Look for first seen and last seen fields, if available. Some datasets include this.
- If not, you’re limited to a point-in-time view. Be wary of drawing trend lines from this—it’s tempting, but it’s not honest.
What doesn’t work: Don’t trust web traffic or “intent” signals as proof of technology adoption. They can show interest or research, but not actual installs.
Step 5: Slice the Data by What Matters
Big adoption curves are interesting, but details are where you find real opportunities.
- By industry: Are manufacturers adopting cloud tools faster than banks? Slice and see.
- By region: U.S. vs. Europe adoption often lags by 6–12 months, depending on the tech.
- By company size: Startups usually switch faster than Fortune 500s—unless you’re talking about core infrastructure.
Build pivot tables or simple bar charts. Don’t overcomplicate it. The goal is to spot meaningful differences, not to build a dashboard no one uses.
Pro tip: Beware of tiny sample sizes. If you’re looking at “adoption among healthcare companies in Vermont with over 10,000 employees,” you’re probably looking at a handful of companies—don’t draw big conclusions.
Step 6: Visualize (But Keep It Simple)
You don’t need a PhD in data viz. Use basic line or bar charts. Excel, Google Sheets, or Tableau all work. Things to try:
- Adoption over time: Line chart, one line per technology.
- Market share by segment: Stacked bar charts work well here.
- Churn rate: Show companies dropping a tech, not just adding it.
Don’t cram all your findings into one chart. If a visualization looks messy, break it up.
What works: Simple visuals that tell a clear story. If you have to explain the axis labels for five minutes, it’s too complex.
Step 7: Test Your Findings Against Reality
It’s easy to get lost in the data and forget the real world. Before you send that deck to your boss or your client:
- Sanity check big claims: If your chart says Salesforce lost 30% market share in a year, but you haven’t seen any headlines about it, double-check your data.
- Ask for feedback: Show your findings to someone who actually talks to customers. Do your insights pass the “does this feel right?” test?
- Look for outside validation: Compare against public earnings reports, job postings, or even LinkedIn data. Hginsights is strong, but no dataset is perfect.
What to ignore: Don’t feel the need to force a “hot take.” Sometimes, the trend is flat or slow. That’s still useful to know.
Step 8: Turn Insights Into Action (or a Smarter Next Step)
Now that you’ve got real adoption trends, use them. Examples:
- For sales: Target companies just starting to adopt a competing technology—they’re in play.
- For product: Spot which sectors are lagging in adoption and ask why.
- For marketing: Build campaigns around the real rate of change, not hype.
And if you didn’t find much? That’s fine. Sometimes the real insight is that a market is steady, or slower to move than you thought.
What Works, What Doesn’t, and What to Skip
Works well: - Using historical install data to see real change, not just noise. - Slicing by relevant segments (industry, size, region). - Sanity checking your own numbers.
Doesn’t work: - Trusting intent or web data as proof of adoption. - Overcomplicating your analysis with too many variables. - Ignoring data quality issues (this will come back to bite you).
Skip it: - Any chart that looks good but you can’t explain in plain English. - Trying to boil the ocean. Focus on the few tech categories that matter.
Keep It Simple, Iterate Often
You don’t need to build a perfect market map on the first try. The best insights come from starting small, getting your hands dirty, and tweaking your approach as you go. Hginsights data is powerful, but only if you use it with a healthy dose of skepticism and a clear goal. Don’t let the hype distract you—stick to what’s real, and you’ll get way more value from your efforts.