Your exec team asks how your product metrics compare to competitors. Activation rate, retention, time-to-value—do you perform better or worse than your competitive set?
You have no idea. Competitors don't publish their product analytics. Industry reports show high-level SaaS benchmarks but nothing specific to your category. You're flying blind on competitive performance.
This is the analytics benchmarking problem: the metrics that actually matter—product engagement, activation rates, feature adoption—are the metrics companies never share publicly.
But competitive benchmarking isn't impossible. It just requires indirect methods: using your own data creatively, finding proxy signals, and leveraging the limited public information competitors do reveal.
After helping five B2B companies develop competitive analytics benchmarking approaches and finding creative ways to assess competitive standing without access to private data, I've learned that effective benchmarking is less about perfect data and more about smart proxies.
Here's how to benchmark your product performance when you can't see inside competitors' analytics.
The Public Signal Method: What Competitors Accidentally Reveal
Competitors don't publish retention rates, but they reveal performance signals in other ways.
Signal 1: Employee review sites (Glassdoor, Blind)
Engineering and product employees often discuss company health in reviews. Look for mentions of:
- Customer churn issues: "We're losing customers faster than we can replace them"
- Product quality problems: "Too many bugs, customers are frustrated"
- Growth challenges: "Sales can't hit targets, product-market fit is unclear"
vs. positive signals:
- "Customers love the product, retention is strong"
- "We can't keep up with demand, scaling challenges"
- "Product metrics keep improving quarter over quarter"
These aren't precise metrics, but they indicate directional performance. If competitor employees consistently mention churn problems while your team doesn't, you're likely in better shape on retention.
Signal 2: Job postings
What roles is your competitor hiring for aggressively?
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Heavy customer success hiring → might indicate churn/retention problems
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Growth marketing roles → might indicate acquisition challenges
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Data engineering/analytics roles → might indicate they're investing in measurement infrastructure you already have
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Product managers focused on "improving activation" → explicitly reveals activation is a focus area, suggesting it needs improvement
Job descriptions reveal priorities. Priorities reveal what's not working well.
Signal 3: Product changelog and release notes
Track competitor feature releases. Look for patterns:
- Frequency of releases: Fast iteration suggests strong eng throughput, or desperate pivoting
- Types of features: Heavy focus on retention features (notifications, collaboration, integrations) suggests retention challenges
- Polish level: Lots of basic UX fixes might indicate quality debt from growing too fast
A competitor who releases onboarding improvements four quarters in a row is clearly struggling with activation, even if they don't publish their activation rate.
Signal 4: G2/Capterra review trends
Track review sentiment over time, especially specific complaints:
Month 1: "Some features are confusing" (isolated feedback)
Month 3: "Onboarding is poor" (emerging theme)
Month 6: "Takes weeks to get value" (clear pattern)
This trend reveals declining time-to-value performance, even without seeing their analytics dashboard.
The Win/Loss Data Method: Learn from Customers Who Chose
Your sales team loses deals to competitors. Your win/loss interviews reveal why.
Mine this data for comparative performance insights:
Question: "What did [Competitor] do better in evaluation?"
Answers reveal their strengths:
- "Faster time to value—we saw results in the demo"
- "Better reporting capabilities"
- "Easier to use, less training required"
- "Stronger integrations with tools we already use"
If you consistently hear "faster time to value," their activation metrics likely outperform yours.
Question: "What were your concerns about [Competitor]?"
Answers reveal their weaknesses:
- "Worried about reliability, heard about downtime issues"
- "Feature set looked comprehensive but seemed complex to actually use"
- "Seemed expensive for what we'd actually use"
- "Customer support reviews were concerning"
These concerns map to performance gaps. Reliability concerns suggest uptime metrics are problematic. Complexity concerns suggest activation or engagement problems.
Track patterns across 30+ win/loss conversations:
Create a comparison matrix:
| Competitive Strength | Frequency Mentioned |
|---|---|
| Easier onboarding | 18/30 mentions |
| Better integrations | 12/30 mentions |
| Faster support response | 8/30 mentions |
This shows where competitors genuinely outperform you, based on customer perception in active buying cycles.
The Reverse Engineering Method: Infer Metrics from Public Actions
Competitors reveal performance through strategic actions.
Action: Aggressive pricing discounts
If a competitor suddenly offers 40% off annual plans or extended free trials, they're likely struggling with conversion metrics. Healthy conversion doesn't require deep discounting.
Action: Pivot to new messaging/positioning
If a competitor who emphasized "ease of use" suddenly shifts to emphasizing "enterprise security," their original positioning likely wasn't converting well with their target market.
Action: Product bundling or package restructuring
Unbundling features into separate products suggests engagement with all-in-one package was low. Rebundling separately sold products suggests customers wouldn't pay for individual pieces.
Action: Leadership changes
New Head of Product or VP of Engineering often indicates performance problems previous leadership couldn't solve. Check LinkedIn and press releases for executive departures at competitors.
Each action is a signal. Cluster multiple signals to infer likely performance gaps.
The Proxy Metric Method: Find Correlates You Can Measure
You can't measure competitor activation rates directly, but you can measure proxy signals that correlate with activation.
Proxy: Trial signup to activation time windows
Track how long it takes from when someone signs up for competitor trials to when they go silent or post about activation success.
Monitor Twitter, LinkedIn, product forums for posts like: "Day 3 with [Competitor], finally got first dashboard working!" This reveals ~3 day activation timeline.
Compare to your own: If you activate users in <1 day and competitors take 3-5 days, you have comparative advantage on time-to-value.
Proxy: Community activity levels
Active user communities indicate engagement. Measure:
- Posts per week in competitor Slack/Discord communities
- Forum activity (questions asked, answers provided)
- User-generated content (templates, tutorials, integrations)
A thriving community suggests strong engagement metrics. A ghost-town community suggests low active user counts or weak engagement.
Proxy: Support volume indicators
Monitor competitor support channels (Twitter support accounts, public help forums):
- Response time (fast responses suggest manageable volume)
- Question types (lots of basic questions suggest onboarding problems)
- Escalation patterns (lots of "DMing you" responses suggest serious issues)
High public support volume with slow responses suggests scaling challenges or quality problems.
The Customer Research Method: Ask Users Who've Used Both
Some of your customers previously used competitors. Some prospects are evaluating both products simultaneously. These people have comparative insights.
Survey question: "On a scale of 1-10, how quickly did you reach value with [Competitor] vs. [Your Product]?"
Answers reveal perceived time-to-value comparison, even if you don't know exact metrics.
Survey question: "Which product was easier to use for [specific workflow]?"
Repeated patterns across many users indicate real UX/usability advantages or disadvantages.
Interview question: "Walk me through your first week with [Competitor]. How did that compare to your first week with us?"
Narrative comparisons reveal where experiences diverge. If users consistently say "Competitor took 3 weeks to get first value, you took 2 days," you have strong comparative data point.
The Analyst Report Method: Industry Benchmarks with Context
Gartner, Forrester, and category-specific research firms publish benchmark data. It's usually aggregate (all tools in category) rather than competitor-specific, but still useful.
Use benchmarks to assess your standing:
Industry benchmark: 35% of SaaS trial users activate within 7 days
Your performance: 58% activate within 7 days
Inference: You're well above industry average. Unless your competitors are also above-average, you likely lead on activation.
Use benchmarks to contextualize gaps:
Industry benchmark: 70% annual gross retention for B2B SaaS
Your performance: 68% annual gross retention
Inference: You're near average. Competitors are likely in similar range unless they're clear outliers.
Validate benchmarks with your segment:
Not all benchmarks apply to your specific segment. Enterprise SaaS has different benchmarks than SMB. Confirm the benchmark actually represents your category.
The Internal Baseline Method: Track Your Improvement Velocity
If you can't benchmark against competitors directly, benchmark against yourself over time.
Quarterly metrics tracking:
Q1 2024: Activation rate 42%, 6-month retention 68%
Q2 2024: Activation rate 46%, 6-month retention 71%
Q3 2024: Activation rate 51%, 6-month retention 74%
Q4 2024: Activation rate 53%, 6-month retention 76%
You're improving 3-5 percentage points quarterly on key metrics. Even without competitor data, this trend suggests strong execution.
If competitors are static or declining while you're improving, you're gaining ground even if you don't know their exact numbers.
Communicate improvement velocity to stakeholders:
"We can't see competitor dashboards, but we can see our own trajectory. We've improved activation 11 points and retention 8 points over the past year. That rate of improvement—if sustained—will move us from industry average to top quartile within 18 months based on published benchmarks."
This reframes benchmarking from "how do we compare right now" to "at our improvement rate, where will we be in 12 months?"
You'll never have perfect competitive analytics visibility. But by combining public signals, win/loss insights, proxy metrics, and customer research, you can build directionally accurate competitive benchmarking that informs strategy without requiring access to competitor dashboards.