Grouping Your Data in Competitor Analysis: Weekly Winning Strategies
When analysing competitors, grouping information together can help you spot patterns. Different data points show what matters. Put related things together and separate what doesn’t fit. That’s where you find real insights.
Competitor analysis isn’t just about gathering information. It’s about understanding what it means.
It’s easy to think every data point matters equally, but that’s not true.
Top analysts at companies like Canva, Duolingo, or Notion do something smarter. They group what fits, separate what doesn’t, and ask what the data is really saying.
Here’s how to do that in a direct, practical way, without extra fluff.
Why Grouping Data is Important
Let’s say you’re tracking a competitor like ClickUp in the productivity SaaS space. You’ve pulled:
- Feature rollouts
- Pricing tiers
- Content marketing volume
- User sentiment from G2
- Employee count from LinkedIn
- Glassdoor reviews
- App store rankings
Without structure, that information is nothing more than just noise.
But if you group feature that connects with customer feedback. Or you compare employee count trends with Glassdoor reviews, you begin to move from describing to analysing. Grouping lets you:
- Identify consistent themes. “They’re pushing enterprise features but SMBs hate it”
- Detect shifts in strategy.“Their blog used to be tactical, now it’s full of thought leadership—why?”
- Catch contradictions. Their site says they care about support, but every review slams their CS team”
What should be grouped?
Group data that tells a clear story. If two or more data points are connected by goal, behaviour, or result, then see if they group together.
Examples
Product Development Group
- New feature launches
- GitHub commit volume (if public)
- Job postings for engineering/product
- Customer feedback on features (Reddit, G2)
This shows how quickly they release new features, where they focus their efforts, and what users think about those changes.
Go-to-Market Group
- Pricing changes
- Ad spend (via Meta Ad Library, SEMrush)
- Webinar frequency
- Sales hiring trends
This helps you see how aggressive they are, whether they’re targeting new customer groups, and if they focus on growing revenue or market share.
Brand & Positioning Group
- Website headlines over time (use Wayback Machine)
- SEO blog content themes
- Tone of voice in emails/social
- PR hits or mentions
This reveals how they want to be seen, what they believe matters to the market, and which competitors they follow.
Not all data can or should be grouped. This is important to understand.
This is where many analysts make mistakes. Don’t try and make patterns that are not there.
Some data stands alone because it doesn’t match the rest. That’s important.
These contradictions often hide the most valuable insights.
Glassdoor vs LinkedIn Growth
A competitor is hiring a lot, but Glassdoor reviews are pretty poor.
You don’t group that. You flag it. That contradiction could signal:
- Poor culture scaling
- Fast growth at the expense of retention
- A misalignment between public story and internal reality
How to Decide What to Group
Here’s a framework you could use to decide what to group:
1. Do these data points support the same business goal?
2. Are they happening at the same time? If one data point is from 2021 and the other is from 2025, don’t group them unless you’re looking at long-term trends.
3. Do they support or contradict each other?
4. Can you track them against a specific KPI or result? Like Like churn, CAC, LTV, release velocity, etc. Or Ad spend and sales hiring = growth
If you answer “yes” to at least three of these questions, then look to group the data.
The Real Story Is in the Tension
Your job in competitor analysis isn’t just to organise data. And it’s definitely not just to collect data. It’s to find the bigger strategic story.
If all your data fits perfectly into categories, you might be missing something. Real insights come from the tension between different data sets.
First-Hand: What We Saw With a SaaS Client in 2023
We worked with a three-year-old SaaS company in time-tracking. They were bootstrapped and making $2.8 million in annual recurring revenue. They wanted to know why they were losing mid-market deals to RescueTime and Toggl Track.
We grouped:
- RescueTime’s feature roadmap + pricing model
- Toggl’s blog themes and case studies
- Support ticket complaints from Reddit and Twitter
- Hiring velocity (they were slowing down)
We didn’t group Their user onboarding. Users were angry about the complexity on Reddit, but the company called it “streamlined.” That mismatch made it clear they weren’t listening to their users.
We advised our client to reframe their onboarding as human-first, not fast. They changed and built a zero-click setup flow with Loom tutorials, and churn dropped 11% in 3 months.
Insight that came from noticing what didn’t fit into any isolated group.
Don’t Collect Data Just for the Sake of It
Collecting every data point without judgment makes you a competitive intelligence analyst. It makes you a collector of information. Anyone can find a LinkedIn job post. That’s not intelligence. Your real value comes from recognising patterns.
- Group what aligns
- Isolate what conflicts
- Turn contradictions into hypotheses.
- Test those against customer behaviour.
Which companies do this best? It’s not always the loudest ones. It’s the ones who spot changes before everyone else does.
Final Thought: Insight Lives Where the Data Disagrees
Competitors also make changes on the fly, so their data rarely gives you a clear picture. Pay attention to where things stop making sense.
Don’t just collect data. Interpret it.
Frequently Asked Questions (FAQs): Competitor Analysis & Grouping Insights
1. What does grouping data mean in competitor analysis?
Grouping data in competitor analysis means organising related information to identify patterns, trends, and strategic direction. It helps analysts draw conclusions about a competitor’s priorities and behaviour.
2. What should not be grouped in a competitor analysis?
Data that conflicts or contradicts other information should be isolated. Contradictions often signal internal misalignment, poor strategy execution, or a market opportunity.
3. How do I analyse my competitors’ data effectively?
Start by collecting data from reliable sources. That’s product updates, customer reviews, job listings, and PR. And from talking to people. Primary research. Group what supports a single theme and flag outliers to identify strategic tension.
4. What are the best tools for grouping competitor data?
Use tools like Notion or Airtable to group datasets, Gong or Crayon for competitive intelligence, and Figma or Miro for visual mapping of data relationships.
5. Can contradictory data still provide value in competitor analysis?
Yes—contradictory data is often the most valuable. It can highlight blind spots in a competitor’s strategy or opportunities your business can exploit.
6. How do I turn grouped competitor data into strategic decisions?
Look for consistent patterns over time. Use these to form hypotheses about where your competitor is going, then pressure-test them against your customer conversations or market behaviour.
7. What is an example of effective data grouping in SaaS?
Grouping features by customer sentiment and product hiring can tell you if a SaaS competitor is investing in product-led growth or falling behind in meeting user needs.
8. Is there a template for competitor data grouping?
Yes, build categories around: Product Development, Go-to-Market, Brand & Positioning, Hiring & Culture, and Customer Sentiment. Use columns for data type, source, timestamp, and insight.
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Key Takeaways
- Grouping Your Data in Competitor Analysis helps identify patterns and insights by categorising related data points.
- Analysts should distinguish between data that aligns with business goals and that which stands alone or contradicts other information.
- Use frameworks to decide what to group and why, focusing on connections in data that reveal strategic narratives.
- Real insights emerge from examining data tensions and contradictions, highlighting potential opportunities.
- Common grouping categories include Product Development, Go-to-Market, and Brand & Positioning to effectively analyse competitors.
