Set prices confidently: know your range, tiers, segments, and buyer

Frequently Asked Questions

Why is pricing strategy research important for EdTech companies??

EdTech teams make high-stakes bets every day: Which problem should we solve first? Which problem represents the greatest market opportunity? Should we even enter this new market?

Without quantitative validation, teams risk:

• Choosing based on short-term constraints or interpersonal dynamics rather than data at scale
• Pricing too low and leaving 20-30% of revenue on the table
• Pricing too high and stalling adoption before you know if the product works
• Building tier structures that confuse buyers or fail to capture willingness to pay
• Missing valuable segment-specific opportunities that only emerge when you analyze patterns at scale

Pricing research with real buyers, using methods that force trade-offs, reveals what people will actually pay when making real purchase decisions.

Market positioning and competitive differentiation 

The EdTech space is crowded and competitive. Understanding how to price your product helps you position it correctly—whether as a premium solution, an accessible option for budget-conscious schools, or something in between. Your pricing communicates value and helps distinguish you from competitors.

Understanding willingness to pay across diverse buyers 

EdTech companies often serve multiple buyer personas with very different budgets and decision-making processes—individual teachers, school administrators, district procurement officers, parents, students themselves, or in higher ed, departmental heads, IT departments, and institutional buyers. Each group has a distinct willingness to pay and budget constraints. Research helps you understand these differences and potentially create tiered offerings.

Navigating the buyer-user disconnect 

This is particularly critical in EdTech: the person paying for your product is often not the person using it. A district administrator might purchase your platform, but teachers and students are the actual users. In higher ed, institutional leaders or department chairs may sign contracts while faculty and students use the tools daily. This disconnect means you need to understand both the buyer's decision criteria (often focused on compliance, scalability, and cost-per-student) and the user's needs (typically focused on usability, engagement, and learning outcomes). Pricing research must address both audiences to ensure adoption and renewal.

Reducing churn and improving retention 

In subscription-based EdTech, getting the pricing wrong leads to high churn. This is especially challenging when budget cycles differ between K-12 (typically annual or district-level renewals) and higher ed (which may involve semester-based or multi-year contracts). Research helps you understand the perceived value at different price points and identify the threshold where customers stop renewing, particularly important given that ROI in education can be hard to quantify immediately.

Informing product development priorities 

Pricing research often reveals which features customers value most and would pay more for, helping guide your roadmap and resource allocation across both K-12 and higher ed markets.

What pricing questions will this package answer?

Every study is tailored to your decisions, but most teams need to answer a few of these:

Price Point Questions

• What price range feels right to users? Identify the floor (too cheap to trust) and ceiling (too expensive to consider).
• What price point balances adoption and revenue? Find the sweet spot where you grow without leaving money on the table.
• Does willingness to pay vary by segment? Compare teachers vs. districts, small vs. large schools, new vs. returning users.
• Can we raise prices without hurting adoption? Test whether a higher price affects purchase intent.

Packaging & Tiering Questions

• Should we offer one tier or several? Find out if good/better/best packaging increases revenue or adds confusion.
• Which features belong in each tier? See which capabilities users will pay more for and which they expect included.
• Should this feature be an add-on or part of the base product? Test whether users prefer bundling or separate purchase.
• How much more can we charge for premium? Quantify the acceptable gap between base and top tiers.

Business Model Questions

• Per-seat or site license? Learn which model buyers prefer and how it affects purchasing decisions.
• Freemium or paid from day one? See whether a free tier converts or just cannibalizes revenue.
• Annual or monthly billing? Find the discount that makes annual payment compelling.
• Who's the real buyer — teachers or districts? Align pricing to the person controlling the budget.

Competitive Positioning Questions

• How do our prices compare to alternatives? Benchmark perceived value relative to other products or the status quo.
• Do our differentiators justify a premium? Test whether unique features support higher pricing.

What will I receive from this package?

Depending on which questions you're aiming to answer, you'll receive:

• Pricing recommendations with acceptable price range and data-supported price points
• Packaging strategy with clear tier structure and feature-to-tier guidance
• Segment insights showing how willingness to pay differs by role, institution type, or region

We don't just report average willingness to pay — we show how it varies by segment. Often the most valuable insight is discovering that small districts will pay $X but large districts will pay $Y, or that new teachers price-shop while experienced teachers value time-savings differently. This lets you avoid one-size-fits-all pricing that either excludes buyers or leaves revenue uncaptured.

• Revenue scenarios comparing different pricing approaches and their expected impact on adoption and revenue
• Executive deck and 60-minute readout to walk through findings and implications
• Dataset and documentation for your internal analysis

A few notes on scope:

• Elasticity curves and revenue scenarios are provided when methods support them (e.g., Gabor-Granger, discrete choice)
• Modeling focuses on adoption × price, not full financial forecasting
• Competitive benchmarking covers 3–5 key comparables unless a deeper audit is scoped separately
• Implementation guidance is strategic (sequencing, rollout considerations, messaging framing) — we won't write your pricing page, but we'll give you the positioning framework

What happens week by week?

We know pricing decisions are urgent. This package is designed for speed without sacrificing rigor.

Week 1 — Align on What You're Deciding

Kickoff to understand what pricing questions you're trying to answer, your current or planned pricing, and your timeline.

Then a 90-minute pricing workshop where we:

• Prioritize your 2–3 most urgent questions — You can't test everything in one study. We'll help you focus on the decisions that matter most right now (e.g., "What should we charge?" and "Should we have tiers?" vs. trying to also answer freemium strategy, discount levels, and international pricing).
• Map competitors and positioning — We'll audit what alternatives cost and how you want to differentiate. If you don't know competitive pricing yet, we can research it as part of this workshop.
• Define pricing hypotheses — We'll turn your instincts into testable statements. If you come in saying "we have no idea," we'll challenge assumptions ("Why do you think teachers will pay that?"), examine your value proposition, and help you articulate hypotheses worth testing.
• Identify your target segments and buyer roles — Elementary vs. secondary? Teachers vs. administrators? Small districts vs. large? We'll map who needs to be tested and whether different segments might have different willingness to pay.
• Choose the right research method for your needs — Van Westendorp for price ranges, Gabor-Granger for elasticity, conjoint for tier design. We'll explain the tradeoffs and recommend 1-2 methods that answer your specific questions without over-engineering the study.

The workshop is collaborative but structured. You'll leave Week 1 with clarity on what you're testing, why it matters, and confidence that the study will drive action, not just generate "interesting" data.

Weeks 2–3 — Field the Survey and Analyze

We handle survey setup, quality checks, and fielding — either to your list or through our recruiting partners.

If you have your own list: Typical field time is 7–10 days for 200-300 responses.

If we recruit through panels: Allow 10–14 days, especially for district administrators or other niche buyers. Panel recruitment adds cost (quoted separately) and 1-2 weeks, but it's sometimes necessary for new markets or hard-to-reach audiences.

Sample size: Most methods need 200–300 responses for solid confidence. Conjoint analysis needs the higher end. If your audience is smaller (50-100 people), we'll adapt the method or combine quantitative with brief qualitative pricing interviews to give you directional guidance.

While data collects, we prepare the analysis framework: segment cuts, price sensitivity curves, and tier appeal models.

Once responses are in, we run the statistical analysis and translate findings into strategy. We're not just calculating averages — we're identifying which features drive premium tier appeal, finding segment-specific pricing opportunities, and modeling revenue outcomes under different scenarios.

Week 4–6 — Get Recommendations You Can Use

We deliver a clear, data-driven pricing strategy with:

• Recommended price points and ranges with supporting data and confidence intervals
• Tier structure and feature allocation (if testing tiers) — which features belong where and why
• Segment-specific insights — how willingness to pay differs by role, organization size, or experience level
• Revenue scenarios — modeled adoption and revenue impact at different price points
• Implementation guidance — sequencing recommendations (who to price for first), rollout considerations (grandfather existing customers? offer upgrade path?), and messaging alignment (how to talk about tiers without cannibalizing or confusing). We won't write your pricing page, but we'll give you the strategic framing.

60-minute readout session where we walk through findings, discuss implications, and answer questions. You'll leave with evidence, clarity, and a defensible plan you can take to finance, sales, and leadership.

When should I use pricing research?

Before launch — to set prices confidently

Many launches fail on pricing—too conservative or too aggressive. Pricing mistakes compound: start too low and you'll struggle to raise prices later without alienating early adopters. Start too high and you'll miss adoption targets before you even know if the product works.

Before changing prices — to reduce churn risk

Existing customers anchor to your current price. Testing helps you understand which price increases they'll accept vs. which will trigger churn or competitive shopping. Some segments may tolerate 20% increases while others defect at 10%.

When adding tiers — to decide what belongs where

Tier structure is tricky — too many tiers confuse buyers, too few could leave money on the table. You need to know which features drive willingness to pay for premium vs. which users expect in the base product, and what price delta between tiers feels fair.

When entering new markets — to adjust for different budgets and expectations

Higher ed vs. K-12, international vs. domestic, small districts vs. large — willingness to pay can vary dramatically. What works in one market may be completely wrong for another.

Who is the Pricing Strategy Research Package right for?

The Pricing Strategy Research Package is the right fit if:

✅   You're preparing to launch and need to set initial pricing with confidence
✅   You're considering a price change and want to reduce churn risk
✅   You're adding tiers and need data on feature allocation and price gaps
✅   You're entering a new market with different budgets and buyer expectations

This package may not be the right fit if:

❌   You haven't validated product-market fit yet (consider our Problem Validation Survey Package)
❌   You need ongoing pricing optimization support (consider our Research & Design Partner engagement)
❌   Your pricing is constrained by external factors (grants, institutional mandates) that data can't change

What research methods do you use?

Different pricing questions require different methods. We select the right one (or two) during your workshop.

Van Westendorp Price Sensitivity

Find the acceptable price range and optimal point for a single product.

Users answer four questions about price thresholds (too cheap, cheap, expensive, too expensive). We map their responses to identify where prices feel right vs. too low or too high.

Use this for: Single-tier pricing decisions, establishing initial price points, understanding price ceilings.

Gabor-Granger Price Testing

See how demand changes at different prices to identify the revenue-maximizing point.

Users see a specific price and indicate purchase likelihood. We show different prices to different groups and measure how demand shifts across the price curve.

Use this for: Testing 2-4 specific price points, measuring price elasticity, understanding revenue impact of price changes.

Conjoint Analysis

Understand how users trade off features and price — ideal for tier design.

Users make trade-off decisions between product configurations with different features at different prices. Statistical modeling reveals the value users place on each feature and optimal feature-price combinations.

Use this for: Tier structure decisions, feature prioritization, understanding which capabilities justify premium pricing.

MaxDiff

Rank features by importance to guide what belongs in each tier.

Users repeatedly choose the most and least important features from sets. We derive preference scores showing feature hierarchy.

Use this for: Feature prioritization before building tier structure, understanding must-haves vs. nice-to-haves.

Discrete Choice Modeling

Simulate real-world choices between tiers or bundles to predict adoption.

Users choose between realistic product configurations (like they'd actually shop). We model preferences to forecast behavior under different scenarios.

Use this for: Multi-tier products with add-ons, competitive scenarios, bundle optimization.