Competitive And Market Analysis

Who this is for

You are an aspiring or current AI Product Manager who needs to position your product in a crowded market, understand competitors, and find a clear, defensible edge.

• [ ] PMs launching a new AI feature or product
• [ ] Founders validating product-market fit
• [ ] Analysts supporting go-to-market with data-driven insights

Prerequisites

• [ ] Basic understanding of AI product types (e.g., assistants, recommenders, classifiers)
• [ ] Comfort with user research and interviews
• [ ] Ability to read product pages, pricing plans, and public docs

Why this matters

In real AI PM work you will repeatedly be asked to:

• Size a market and decide which customer segments to prioritize.
• Map competitors, identify gaps, and select differentiators that users value.
• Evaluate whether to build, partner, or deprecate features based on traction and competitive pressure.
• Defend roadmap choices with evidence in product reviews and leadership meetings.

Concept explained simply

Competitive and market analysis is how you understand where you can win. You look at customers (jobs-to-be-done), competitors (how they solve those jobs), and your capabilities. Then you choose a position that is desirable, feasible, and hard to copy.

Core methods you will use

1. Define the job and segment. Write the job story: "When I [situation], I want to [motivation], so I can [outcome]." Segment by use case, industry, and sophistication.
2. Landscape scan. List direct competitors (same job), substitutes (manual or non-AI tools), and platform threats (features inside suites).
3. Feature-outcome mapping. Map features to outcomes users care about (speed, accuracy, compliance, cost, control).
4. Win-loss signals. Gather lightweight proof: reviews, pricing, onboarding friction, performance claims, case studies, and public roadmaps.
5. Quantify the opportunity. Estimate TAM/SAM/SOM and willingness to pay ranges; define adoption beachhead.
6. Positioning decision. Choose 1–2 differentiators and a clear who/why statement; define what you will NOT do.
7. Monitor. Set up a 30–60–90 day review: re-check competitor moves, pricing, and quality benchmarks.

Worked examples (3)

Example 1: AI Writing Assistant for Support Teams

• Job: Draft accurate, on-brand replies fast.
• Key outcomes: Response time, factual accuracy, tone control, CRM hand-off.
• Competitors: General writing tools; helpdesk suites with AI reply; macros/knowledge base.
• Gaps found: General tools struggle with customer-specific data and auditability.
• Positioning: “The assistant that cites your internal knowledge and logs outcomes to your helpdesk automatically.”
• Edge: Proprietary retrieval from internal KB + audit log + PII redaction.

Example 2: AI Fraud Detection for Marketplaces

• Job: Block bad actors without hurting good users.
• Key outcomes: Precision/recall balance, explainability, latency, cost per decision.
• Competitors: Legacy rules engines, payment processor risk tools, open-source models.
• Gaps found: Rules systems brittle; platform tools are black boxes with limited control.
• Positioning: “Adaptive risk with transparent reasons and configurable thresholds.”
• Edge: Feedback loop from chargebacks + interpretable features + on-prem option.

Example 3: AI Sales Email Personalizer

• Job: Personalize outbound at scale with relevance.
• Key outcomes: Reply rate, research time saved, CRM sync, brand safety.
• Competitors: Sequencing tools with basic AI, VA outsourcing, manual research.
• Gaps found: Hallucinations and irrelevant personalization; weak governance.
• Positioning: “On-brand personalization with verifiable references and approval flows.”
• Edge: Verified snippets from sources, template-level guardrails, and AB test loops.

Copy-ready templates

Customer job: ____________________
Primary segment: __________________
Top outcomes (ranked): 1) ____ 2) ____ 3) ____
Direct competitors: ____, ____, ____
Substitutes/platforms: ____, ____
Our unique assets: ____, ____ (e.g., data, distribution, integrations)
Positioning statement: For [segment], we help [job] by [differentiator], unlike [alt].
Will NOT do: ______________________
    

X-axis (customer outcome): ________
Y-axis (customer outcome): ________
Plot: [Competitor A], [Competitor B], [Substitute], [Us-today], [Us-in-90d]
Key insight: ______________________
    

Population (accounts/users): ______
Usage frequency (per period): _____
Willingness-to-pay (range): _______
TAM = population × price (rough)
SAM = segment you can actually serve now
SOM = target capture in 1–2 years (conservative)
Assumption notes: ________________
    

Exercises (hands-on)

Complete Exercises 1–2 below. You can compare with the provided solutions. Use the checklist to self-review.

Exercise 1: Build a competitive landscape for your AI product

Pick a specific job-to-be-done in your domain. Create a 1-page landscape with: competitors, substitutes, outcomes, a 2x2 matrix, and your draft positioning.

• [ ] Define job story and primary segment
• [ ] List 3–5 direct competitors and 2 substitutes/platform threats
• [ ] Rank top 3 outcomes users care about
• [ ] Draw a 2x2 (choose two outcomes as axes) and place players + you
• [ ] Draft a one-sentence position and 2 things you will NOT do

Job story: When I _______, I want to _______, so I can _______.
Segment: __________ (e.g., SMB ecommerce teams)
Competitors: A, B, C; Substitutes: X (manual), Y (platform feature)
Outcomes (ranked): 1) speed 2) accuracy 3) governance
2x2: X-axis speed, Y-axis accuracy. A (med, high), B (high, low), Us (high, high).
Position: For ______, we deliver ______ better via ______. Will NOT do: ____, ____.
    

Exercise 2: Rough TAM/SAM/SOM + pricing hypothesis

Estimate opportunity size and an initial price band. Keep assumptions visible.

• [ ] Count reachable customers (from a directory or logical estimate)
• [ ] Choose a price metric (seat, usage, volume tier)
• [ ] Calculate TAM, define SAM, set SOM for 1–2 years
• [ ] Write 2–3 assumptions to validate with users

Population: 50k teams; ICP SAM: 8k; initial SOM: 2% of SAM = 160 teams.
Price metric: per-seat + usage tier; band: $20–$40/seat/mo + $0.50/1000 events.
TAM (rough): 50k × $5k ARR avg = $250M. SAM: 8k × $6k ARR = $48M. SOM yr2: 160 × $8k = $1.28M.
Assumptions: seat counts per team; willingness to pay if accuracy > 95%; integration cost acceptable.
    

Common mistakes and how to self-check

• Over-focusing on features, not outcomes. Fix: Always map feature → customer outcome → metric.
• Ignoring substitutes and platforms. Fix: Include manual workflows and suite add-ons in the landscape.
• Vague positioning. Fix: Name the segment, job, and differentiator explicitly; add what you will NOT do.
• Hand-wavey sizing. Fix: Show assumptions and ranges; recalc with conservative numbers.
• Static analysis. Fix: Calendar a 30–60–90 review; re-check pricing and quality benchmarks.

Practical projects

• [ ] Create a competitor battlecard set (1 page each): strengths, weaknesses, traps to set, traps to avoid.
• [ ] Build a live 2x2 dashboard for your product area; update monthly with notable moves.
• [ ] Run five 20-minute customer calls to validate top outcomes, then update your positioning.

Learning path

1. Start with one job-to-be-done and one segment.
2. Do a 1-hour landscape scan; draft the 2x2 and outcome ranks.
3. Validate with 3 customers; refine positioning and pricing hypothesis.
4. Publish battlecards and a 60-day monitoring plan.

Next steps

• [ ] Finish Exercises 1–2 and save your templates.
• [ ] Take the Quick Test to check understanding. Progress is saved for logged-in users; the test is available to everyone.
• [ ] Share your positioning statement with a peer for critique.

Mini challenge

In 5 sentences, position your product against the strongest substitute, not the nearest competitor. Name the job, the outcome, your edge, and the trade-off you accept.