Build a Battlecard From a Raw Competitive Signal Dataset
Objective: Given a raw 25-row signal dataset (pricing page snapshots, G2 review excerpts, job postings, a press release) for a named competitor, run it through the lesson's 4-stage CI system to produce a complete one-page battlecard.
You're the PMM at Warby Parker standing up a CI program for a direct online eyewear competitor. Sales has never had a battlecard for this rival and is currently improvising answers on live calls.
Move the raw dataset through Collection, Analysis, and Activation exactly as the lesson's stages define them, ending in a battlecard reps can open during a call.
Before you start
What you'll need
Free path (everything below is enough to finish)
Free, handles a 25-row dataset with filtering and a second analysis tab
Free, shareable, easy for sales enablement to pull into a CRM-linked doc
The process
3 steps
Step 01 of 03
The lesson's Stage 1 groups raw signals into primary sources (win/loss interviews), secondary digital sources (pricing pages, job boards, filings), community sources (G2, Reddit), and signal sources (job postings, conference abstracts).
The dataset has 6 G2 review excerpts and 4 job postings mixed together in one column. Which source category does each belong to, and does that change how much you trust it?
Procedure
- Import raw-signals-25.csv (25 rows: raw text, source URL, date)
- Tag each row's source category: primary, secondary digital, community, or signal
- Sort the sheet by category to group all 6 G2 excerpts and 4 job postings together
Category counts after sort: Secondary digital: 9 rows (pricing pages, press releases) Community: 6 rows (G2 excerpts) Signal: 4 rows (job postings) Primary: 6 rows (win/loss interview notes)
Healthy
Every row gets exactly one category and the counts are visible before analysis starts.
Unhealthy
Treating all 25 rows as equally weighted evidence regardless of source type.
What this means
Source category previews how much weight a signal deserves before you've even read it closely.
So what do I do about it?
| Symptom | Action | Effort |
|---|---|---|
| The battlecard cites a Reddit comment with the same confidence as a filed press release | Add the source category next to every citation in the battlecard draft | 5 min |
Step 02 of 03
The lesson's Stage 3 requires analysis to answer three questions in order: what changed, why did it change, and what does it mean for our positioning, pricing, or roadmap.
The 4 job postings are all for 'AR/3D Engineer' roles posted in the same month. What changed, and what's the most defensible 'why' given only this dataset?
Procedure
- For the job-posting cluster, write one line each for what changed, why (most likely explanation), and what it means for the roadmap
- Cross-reference the G2 review theme rows against the job-posting finding to see if they support or contradict it
- Flag any conclusion that rests on a single row as low-confidence
WHAT CHANGED: 4 AR/3D engineering roles posted within one month (signal source) WHY: Likely building a virtual try-on feature, not just headcount growth (matches a G2 review theme: 'wish I could try glasses on virtually') MEANS: Expect a virtual try-on launch within 1-2 quarters; do not position virtual try-on as our exclusive advantage in new battlecards
Healthy
The 'why' cites at least two independent rows, not one job posting alone.
Unhealthy
Jumping straight from 'what changed' to 'what it means,' skipping the why entirely.
What this means
Skipping the why step is how a battlecard ends up confidently wrong about a competitor's intent.
So what do I do about it?
| Symptom | Action | Effort |
|---|---|---|
| Two analysts read the same 4 job postings and reach opposite conclusions | Require every why to cite a second, independent signal row before it ships | 30 min |
Step 03 of 03
The lesson's Stage 4 defines a battlecard as a one-page document covering positioning, strengths, weaknesses, common objections, and recommended counter-moves, built to be opened and used inside a live deal.
Given the Step 2 analysis and the 6 win/loss interview rows, which single objection should sit at the top of the battlecard's 'Objections' section?
Procedure
- Count how often each objection theme appears across the 6 win/loss rows, rank the most frequent one first
- Write a one-line counter-move for the top 3 objections, each under 20 words
- Add the Step 2 job-posting finding as a single 'watch for' line, not a full paragraph
OBJECTIONS (ranked by win/loss frequency) 1. 'Their frames are $20 cheaper' (4 of 6 losses) -> Counter: Our Buy-a-Pair-Give-a-Pair story plus free adjustments for life 2. 'They have more store locations' (2 of 6 losses) -> Counter: Free home try-on removes the need for a store visit WATCH FOR: Virtual try-on launch likely within 1-2 quarters (see Analysis)
Healthy
The top objection on the card matches the most frequent reason reps actually lose deals.
Unhealthy
Leading the battlecard with the competitor's weakest, least-cited objection because it's the easiest one to write a counter for.
What this means
A battlecard's ranking order is itself a claim about what costs the most deals, rank it from evidence, not from what's easy to answer.
So what do I do about it?
| Symptom | Action | Effort |
|---|---|---|
| Reps say the battlecard doesn't address the objection they actually hear most | Re-rank the Objections section against fresh win/loss interview counts every quarter | 30 min |
Final deliverable
A tagged 25-row signal sheet, a written analysis (what/why/means) for the highest-signal finding, and a complete one-page battlecard with a ranked Objections section.
See a reference example
Lenskart competitor battlecard (excerpt) POSITIONING: Positions as the budget online-only option, no physical stores STRENGTHS: Lower base price, fast shipping WEAKNESSES: No try-before-you-buy, single-item returns only OBJECTIONS 1. 'They're cheaper' (5 of 7 losses) -> Counter: our home try-on removes the fit risk that makes cheap glasses a gamble WATCH FOR: 3 supply-chain roles posted this month, possible in-house manufacturing push
Success criteria
You're done when you can:
- Objections section is ranked by actual win/loss frequency, not by ease of writing a counter
- The job-posting-based finding cites at least one independent corroborating signal in the analysis