By Priya Shankar, Head of Product at Glue
Every PM I know has built a feature comparison spreadsheet at some point. Rows for capabilities, columns for competitors, a sea of checkmarks and X marks. Feature gap analysis is one of the most common exercises in product management, and it is also one of the most unreliable. The problem is not the framework. The problem is the data going into it, specifically on the column labeled "Us."
If you have ever completed a feature gap analysis only to learn from engineering that you were wrong about your own product's capabilities, this guide is for you. I will walk through what feature gap analysis actually is, why the traditional approach breaks down, and how grounding it in your codebase changes the outcome.
What Is Feature Gap Analysis
Feature gap analysis is the process of systematically comparing your product's capabilities against those of competitors or market expectations to identify where you fall short. It answers a deceptively simple question: what do they have that we do not?
The output is typically a matrix or table that maps features across products and highlights the gaps. Product teams use this to inform roadmap priorities, sales enablement, and strategic positioning.
Why It Matters
Feature gaps directly affect win rates, churn, and market positioning. When a prospect evaluates your product against a competitor and finds a capability missing, that gap becomes a concrete objection in the sales cycle. When an existing customer discovers that a competitor offers something they need, that gap becomes a churn risk.
The value of feature gap analysis is not just in identifying what to build. It is in understanding where you stand relative to the market so you can make informed decisions about what to prioritize, what to defer, and what to ignore entirely.
The Inputs You Need
A good feature gap analysis requires two categories of information. First, you need an accurate view of competitor capabilities. This comes from competitor research: their documentation, marketing pages, product demos, review sites, and intelligence from your sales team.
Second, you need an accurate view of your own capabilities. And this is where things break down for most teams. You can learn more about how this fits into broader competitive strategy in our competitive intelligence guide for SaaS.
Traditional Approaches (and Their Limits)
The standard approach to feature gap analysis has not changed much in a decade. Build a spreadsheet, research competitors, fill in the matrix. It works well enough when products are simple. It falls apart at scale.
The Spreadsheet Method
Most PMs start with a feature list derived from competitor marketing pages and their own product knowledge. They create a grid, mark capabilities as "Yes," "No," or "Partial," and use the output to drive roadmap conversations.
The spreadsheet method is intuitive and widely used. It is also brittle. The moment your product grows beyond what any single person can hold in their head, the accuracy of the "Us" column degrades. I have personally built feature gap matrices where I later discovered three capabilities I marked as missing were actually in the codebase.
The Survey Method
Some teams formalize internal knowledge gathering by surveying engineers. "Does our product do X? Does it handle Y?" This is better than relying on a single PM's memory, but it introduces its own problems.
Engineers answer based on their knowledge of the system, which is partial. A frontend engineer may not know about backend capabilities. A backend engineer who joined last year may not know about features built two years ago. The answers you get are only as complete as the collective memory of the people you ask.
The Documentation Method
Teams with strong documentation practices might reference internal wikis, architecture docs, or feature specs. But documentation decays. At my previous company, I estimated that roughly 40% of our internal docs were outdated at any given time. Features get refactored, APIs change, and the docs sit there reflecting a version of the system that no longer exists.
None of these traditional methods give you a reliable, current source of truth for what your product actually contains.
Code-Grounded Gap Analysis
What if instead of relying on memory, surveys, and stale documentation, you could query your codebase directly? That is the shift from traditional feature gap analysis to what I call code-grounded gap analysis.
How It Works
Code-grounded gap analysis replaces the guesswork in the "Us" column with answers derived from actual code. When you need to know whether your product handles a specific capability, you ask a system that has read and indexed your entire codebase.
For example, instead of asking an engineer, "Do we have anything like real-time notifications?" you query your codebase and get back specific answers: which files implement notification logic, which APIs are involved, what the current scope of the implementation covers.
This is the core of what Glue provides. Glue connects to your repository, indexes every file, function, and dependency, and lets you ask questions in plain language. When you are running a feature gap analysis, you can verify each capability against the actual code. For a deeper look at connecting competitive intel to code reality, see our post on competitive analysis when you can't see your own code.
What Changes in Practice
The biggest practical change is the accuracy of your gap categorization. Instead of a binary "have it / don't have it," you can categorize with precision:
- Full implementation: The feature exists and is user-facing.
- Backend only: The logic exists but has no UI exposure.
- Partial build: Some work was done, then deprioritized.
- Adjacent capability: We have something close that could be extended.
- True gap: Nothing exists. Full build required.
This granularity is a difference-maker for roadmap planning. A "backend only" gap might take one sprint to surface to users. A "true gap" might take a quarter. Treating both the same, as traditional gap analysis does, leads to poor prioritization.
The Gap Atlas Approach
Glue's competitive gap analysis feature, called Gap Atlas, automates much of this process. You add a competitor, and Glue maps their known capabilities against what exists in your indexed codebase. The output shows where you lead, where you trail, and the nature of each gap.
This does not replace product judgment. You still need to decide which gaps matter and which ones are irrelevant to your strategy. But it gives you a far more accurate starting point than a spreadsheet built from memory.
Building a Feature Gap Workflow
Based on running this process across multiple product cycles, here is a workflow that balances rigor with practicality.
Phase 1: Competitor Feature Mapping (Week 1)
Spend focused time documenting competitor capabilities. Use their public documentation, marketing materials, demo videos, and G2/Capterra reviews. Talk to your sales team about what competitors are showing in demos. Be thorough on this side. The external view is where most teams already do reasonable work.
Phase 2: Internal Capability Verification (Week 1-2)
This is where the process diverges from tradition. Instead of relying on memory, query your codebase for every capability on the competitor list. If you are using Glue, this can happen in a single working session. Ask Glue about each capability area and document what exists.
For teams without codebase intelligence tooling, schedule dedicated sessions with engineering leads from each team to walk through the competitor feature list together. This is slower but significantly more accurate than a PM filling in the matrix alone.
Phase 3: Gap Categorization and Prioritization (Week 2)
With accurate data on both sides, categorize each gap using the five-level system described above. Then prioritize based on:
- Customer demand: How often do customers or prospects ask for this?
- Implementation effort: Is this a true gap or an exposure gap?
- Strategic alignment: Does closing this gap serve your product vision?
- Revenue impact: Can sales quantify the deal impact of this gap?
Phase 4: Ongoing Maintenance
Feature gap analysis is not a quarterly exercise and then forgotten. Set up a cadence, monthly or quarterly, to refresh both the competitor view and the internal view. With a tool like Glue that updates as your codebase evolves, the internal side stays current automatically. The competitor side still requires ongoing monitoring.
Making It Actionable
The final step is making sure your gap analysis actually influences decisions. Share the categorized gap matrix with engineering leadership so they understand the strategic context. Present it in roadmap planning so prioritization reflects real competitive dynamics.
If you want to start running feature gap analysis grounded in your actual codebase instead of tribal knowledge, try Glue to see the difference that code-level accuracy makes.
Frequently Asked Questions
What is feature gap analysis?
Feature gap analysis is the process of comparing your product's capabilities against competitors or market expectations to identify where your product falls short. It typically produces a matrix mapping features across products, highlighting gaps that inform roadmap prioritization, sales enablement, and strategic planning. The most effective feature gap analysis goes beyond simple "have it / don't have it" categorization and assesses the nature and depth of each gap.
How do you identify gaps against competitors?
Start by thoroughly documenting competitor capabilities through their public documentation, review sites, sales conversations, and product demos. Then verify your own capabilities against that list, ideally by querying your actual codebase rather than relying on team memory or outdated documentation. Categorize each gap by implementation status, distinguishing between true gaps, partial builds, and capabilities that exist but are not user-facing. This gives you a far more actionable view than a traditional binary comparison.
What tools help with feature gap analysis?
Traditional tools include spreadsheets, competitive intelligence platforms like Crayon and Klue for tracking competitors, and internal documentation wikis. For grounding your analysis in what your product actually contains, codebase intelligence platforms like Glue let you query your code directly and compare internal capabilities against competitor features. The most effective approach combines external CI tools for competitor monitoring with internal codebase visibility for accurate self-assessment.