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Pricing strategy & customer research

Pricing Feedback Survey Template

Capture pricing perception from existing customers and users — value rating, current plan, willingness to pay more, feature-set that justifies higher pricing, and competitive position. The structured input your pricing committee uses for the quarterly review your CEO has been postponing.

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Pricing Feedback Survey Template

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Who this template is for

Pricing is the highest-leverage variable in B2B SaaS — a 1 % improvement in price drives roughly 11 % more profit, versus 1 % improvement in retention (7 %) or new customer acquisition (3 %). And yet most SaaS companies leave pricing on autopilot for years, because changing pricing is scary and 'we don't have data.' This template structures the customer-side pricing input — value rating on a 5-point Likert (the most direct signal that pricing is appropriate or off-target), current plan band (which plan tier the respondent is on — Free / Starter / Pro / Enterprise), willingness to pay more (Yes / Depends on features / No, with the 'Depends' answer being the single most actionable response because it tells you what you would need to add to capture more revenue), free-text on which features would justify a higher price (the qualitative input for pricing-page repositioning), and competitive positioning rating (Much cheaper / Slightly cheaper / About the same / Slightly expensive / Much more expensive — anchored against the alternatives the customer evaluated). It is the structured input your pricing committee uses for the quarterly pricing review that your CEO has been postponing because nobody has the data, not the freeform 'what do you think of our pricing?' email that produces useless answers. Used by B2B SaaS product marketing teams, pricing strategy consultants (Simon-Kucher, ProfitWell now part of Paddle, Price Intelligently by ProfitWell), and any subscription business preparing for a pricing change, plan-tier restructuring, or annual pricing review.

From pricing question to pricing-committee-ready research in one structured survey

Survey is sent to a sampled segment of existing customers (typically with a stratified sample across plan tiers, tenure, and ARR bands to avoid survey bias) via email or in-product survey trigger. Email field captures the respondent identity (typically with an opt-out for anonymous responses if the pricing question is politically sensitive). Value rating on a 5-point Likert ('Current pricing provides good value' — Strongly Agree to Strongly Disagree) is the headline metric — the percentage of customers in 'Agree' or 'Strongly Agree' is the pricing-acceptance metric that the pricing committee tracks quarter over quarter. Current plan (Free, Starter, Pro, Enterprise — or your equivalent plan tier names) lets the analysis segment the responses by plan, which is where the actionable insights live: 'Pro customers rate value 4.2 average but Starter customers rate 2.8' tells you the Starter plan is mispriced or under-featured, not that pricing across the board is the problem. Willingness to pay more (Yes / Depends on features / No) is the upside-capture signal — the 'Depends on features' answer is the most actionable because it tells you what you would need to add to capture more revenue from this customer. The free-text 'what features would justify a higher price' captures the qualitative input that drives the pricing-page repositioning — patterns in the answers (e.g., 60 % mention SSO, 40 % mention API access, 30 % mention SLA) become the foundation for new plan-tier construction or feature-bundling decisions. Competitive positioning (Much cheaper / Slightly cheaper / About the same / Slightly expensive / Much more expensive) anchors the pricing perception against the alternatives the customer evaluated — 'About the same' is the goal state, 'Much more expensive' is a churn risk, 'Much cheaper' is value-capture opportunity (you are leaving money on the table). On submission, the responses feed the pricing-committee dashboard with the aggregate trends, per-plan segmentation, willingness-to-pay analysis, and feature-priority ranking. The patterns over 200-500 responses inform the quarterly pricing review and the eventual pricing-change decision.

What's included

Every field exists because some product marketing or pricing team has been burned by its absence — usually at the quarterly review where the team realizes they have no customer-side data on willingness to pay, or at the pricing-change post-mortem where the unexpected churn spike traces back to an obvious signal (customers on the Pro plan saying pricing was 'slightly expensive') that nobody had captured.

Subscription businesses using pricing feedback surveys

  • B2B SaaS preparing for a pricing change

    SaaS companies considering a price increase (the most common scenario — most SaaS companies under-price for 2-3 years before catching up), a plan-tier restructuring (consolidating 5 tiers down to 3 or vice versa), or a pricing model change (per-seat to usage-based, or flat-rate to value-based). The survey provides the customer-side data that the pricing committee needs to make the call. Pairs naturally with the Van Westendorp Price Sensitivity Meter (which asks 4 specific anchor questions at what prices the product becomes 'too cheap, cheap, expensive, too expensive') and Gabor-Granger (which asks the customer their willingness to pay at specific price points) — this survey complements those methodologies with the qualitative input.

  • PLG SaaS analyzing free-to-paid conversion

    Product-led growth SaaS (Notion, Linear, Figma, Postman, Retool, Cal.com, PostHog, Posthog, dbt) where the question is not 'should we change the headline price' but 'what would convert the free users to paid?'. The survey of Free-plan users captures their value perception, willingness to pay, and the features they would pay for — which is the input the product team uses to identify the gating-feature decision for the Pro tier. The patterns are different from paid-customer pricing research — free users are often willing to pay for features they have not used, which the pricing team needs to discount.

  • Subscription consumer products and DTC brands

    Subscription consumer products (Calm, Headspace, Spotify, Netflix subscription tiers, Audible, Skillshare, MasterClass, Babbel, Duolingo Plus) and DTC subscription brands (Dollar Shave Club, Birchbox, HelloFresh, BarkBox) running annual pricing reviews. The survey captures the consumer-side pricing perception across tier (typically Free trial / Monthly / Annual / Family Plan) and the competitive positioning against the alternatives. Pricing changes in consumer subscription are public and visible (Netflix and Disney+ price increases generate news cycles), so the data needs to be robust before the announcement.

  • Open source dual-license and open core companies

    Open source companies running dual-license or open-core models (Elastic, MongoDB, HashiCorp, GitLab, Sentry, PostHog, Cal.com, n8n, Supabase, Strapi) where the question is 'which features should be paid vs. free?'. The survey of self-hosted open-source users captures the willingness to pay, feature priorities, and the perception of paid-tier value. The patterns inform the open-core boundary decision — which features stay open-source and which features move to paid — which is one of the highest-stakes decisions in the open-core business model (Redis Labs, MongoDB, Elastic all faced backlash when they moved features behind paid licenses).

  • Brazilian SaaS pricing research

    Brazilian B2B SaaS (RD Station, Conta Azul, Pipefy, Vindi, Hotmart Business, Bling, Tray, Nuvemshop) facing the unique Brazilian-pricing reality where SaaS pricing in BRL (Reais) is often substantially lower than the USD equivalent (typically 30-50 % discount versus the US dollar headline) due to PPP (purchasing power parity), USD/BRL exchange volatility, and Brazilian buyer expectations. The survey captures the value perception in the Brazilian context, the willingness to pay in BRL terms, and the competitive positioning against the local SaaS ecosystem (Resultados Digitais, Hotmart, Conta Azul) rather than the US comparison.

  • Spanish-market SaaS and European pricing strategy

    Spanish-market and European SaaS facing the EU-specific pricing question of how to price across the EU (which is one market for VAT but multiple markets for purchasing power — Germany / France / Spain / Italy / Poland have meaningfully different SaaS pricing expectations). The survey captures the country-specific pricing perception and informs the geographic pricing strategy (single EU price vs. country-specific pricing vs. tiered geo-pricing). Tools commonly used by Spanish SaaS for pricing research: Typeform (Spanish-built, used worldwide), Survicate, Refiner, Sprig, Hotjar Feedback.

Tailor it to your pricing methodology

Every pricing review has its own specific questions. Configure the Likert scale to match your team's preferred scale (5-point or 7-point are most common; 5-point is more interpretable, 7-point captures more nuance). Configure the plan-tier field with your actual plan names — most SaaS uses Free / Starter / Pro / Enterprise; some use Hobby / Pro / Team / Business / Enterprise; PLG companies often use Free / Pro / Team; consumer subscriptions use Free trial / Monthly / Annual / Family. Add the tenure field (months as customer) — pricing perception changes meaningfully over the customer lifecycle, with new customers being more price-sensitive than tenured customers. Add the company size or ARR-band field for B2B SaaS — willingness to pay correlates with company size, and segmenting by ARR band reveals the under-monetized customer segment. Add the Van Westendorp four-question block if you are running formal pricing research: 'At what price would the product be too expensive (you would not consider buying)?', 'At what price would the product be priced so low you would question quality?', 'At what price would the product be expensive but you would still consider buying?', 'At what price would the product be a bargain?'. The four answers produce the Price Sensitivity Meter chart that pricing strategists use to identify the optimal price range. Add the Gabor-Granger price-ladder questions if you are running formal pricing-elasticity research: 'Would you pay $X per month for the product?' iterating up or down based on the answer. Configure the routing — high-value enterprise responses go to the pricing committee directly; low-tier responses aggregate for the dashboard; 'much more expensive' responses route to the customer success retention queue (these are churn-risk customers). For Brazilian SaaS, configure the BRL pricing reference and capture the exchange-rate-sensitivity if you bill in USD. For European SaaS, configure the country-specific pricing reference and capture the VAT-inclusive vs VAT-exclusive perception (B2B customers think in net price; B2C in gross price).

Pricing feedback FAQs

Van Westendorp and Gabor-Granger are formal pricing-research methodologies that produce specific quantitative outputs. Van Westendorp asks four anchor questions (too cheap, cheap, expensive, too expensive) and plots the intersection curves to identify the 'optimal price point' and the 'range of acceptable prices.' Gabor-Granger asks the respondent's willingness to pay at specific price points iteratively, producing a demand curve. Both are well-validated for quantitative pricing research but require a structured methodology to execute correctly. This survey is the qualitative complement — it captures the why behind willingness to pay, the feature priorities that drive value perception, and the competitive positioning that anchors the perception. Most pricing research programs run all three: Van Westendorp for the quantitative price-range anchoring, Gabor-Granger for the demand-curve fitting, and this kind of survey for the qualitative input that informs the messaging and feature-bundling decisions. Tools used: ProfitWell Recur (formal pricing research with both Van Westendorp and Gabor-Granger built in, now part of Paddle), Simon-Kucher (consulting firm for high-stakes pricing research), Pricing.com (newer offering), and standalone form tools like this one for the qualitative layer.
Both modes have value and the right answer depends on what you are trying to learn. Anonymous mode (no email captured, or email captured but stripped before analysis) produces more candid answers — customers are more willing to admit 'I think your pricing is too expensive' when they are not worried about getting a follow-up sales call. Attributable mode lets the customer success team route low-value-perception responses to retention outreach, which is the right move if you are running pricing research as a churn-prevention exercise. The compromise that most product marketing teams use: capture email but separate the analysis from the customer-success routing. The analysis dashboard sees aggregate trends without individual attribution; the customer success team gets a separate alert for 'much more expensive' or 'Strongly Disagree' responses with the email for retention outreach. This respects the customer's expectation that the survey is confidential while letting the org act on the high-signal individual responses.
Statistically, 200-500 responses across a representative sample of your customer base is the typical threshold for reliable per-plan and per-segment analysis. Under 100 responses, the per-plan segmentation is too noisy to draw conclusions (the Enterprise tier may have only 10-20 customers responding, which is not enough). For overall headline metrics (the percentage who rate value 'Agree' or 'Strongly Agree'), even 50-100 responses gives a directional read. For B2B SaaS with fewer than 100 customers total (early-stage), the survey is more about identifying outlier signals than statistical inference — even 20 responses with 5 'Strongly Disagree' on value is a meaningful signal worth investigating. The other dimension to watch: response bias. The customers who respond are typically more engaged than average, which means the survey overrepresents satisfied customers. Adjust the interpretation accordingly — the actual customer base is probably less satisfied than the survey suggests. For pricing decisions specifically, weight the responses by ARR (high-ARR customers' willingness to pay matters more than low-ARR customers') to avoid the 'mathematically correct but commercially wrong' decision.
On submission, the workflow can update the customer record in your customer success platform with the pricing perception data attached. For Gainsight, ChurnZero, Catalyst, Vitally (customer success platforms), the responses feed the customer health score with pricing perception as a contributing factor — customers with low value-perception or 'much more expensive' ratings get flagged as churn risk and routed to the appropriate CSM playbook. For Amplitude, Mixpanel, Heap, PostHog (product analytics), the response gets attached as a user property so subsequent product behavior can be analyzed in context (do customers who said 'much more expensive' actually use the product less, which would confirm the churn risk?). For ProfitWell now Paddle (subscription analytics specifically), the survey data feeds the pricing dashboard with the qualitative complement to the quantitative MRR and churn analytics. For Salesforce, HubSpot, Pipedrive, RD Station CRM, Holded (CRM), the response is attached to the contact or account record. For Stripe, Chargebee, Recurly, Vindi (Brazilian), Iugu, Asaas (Brazilian) — the subscription billing platforms — the response can be cross-referenced with the customer's actual usage and renewal behavior. The patterns over 6-12 months inform the eventual pricing-change decision with both quantitative usage data and qualitative customer perception data.
The five most common mistakes: (1) Surveying only the loud customers — the responders self-select into customers who care enough to respond, which biases the data toward engaged users and away from at-risk silent customers; mitigation: in-product survey triggers across the user base rather than email-only. (2) Asking 'do you think we should raise prices?' — customers will always say no to higher prices, so the question is useless; the correct frame is 'would you pay more for X feature?' which captures the value-feature link. (3) Running the survey once and never again — pricing perception drifts over time as competition changes and feature gaps emerge, so the survey should be quarterly or at minimum annual. (4) Ignoring the segmentation — the average value rating is misleading if Pro customers rate 4.5 and Starter customers rate 2.0; the segmented data is where the actionable signal lives. (5) Acting on the qualitative input without quantitative cross-check — if 30 % of respondents say 'I would pay more for SSO,' that is a strong signal but not proof; the pricing team should run a Van Westendorp or Gabor-Granger study on the SSO feature specifically before pricing it. The qualitative input identifies the hypothesis; the quantitative methodology tests it. Companies that skip the testing step end up with pricing changes that work in theory but produce unexpected churn in practice.

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