How do you set prices with confidence?
At a glance:
Elasticity isn’t just “how sensitive demand is to price.” For operators, it’s a tool to shape a Good/Better/Best (GBB) ladder, widen your offering “fan,” and match customers’ willingness-to-pay without confusing apples for oranges.
WHAT IT IS
Elasticity measures how quantity sold moves when price moves. But small businesses rarely sell a single, perfectly comparable widget. You’re selling relative value: options that solve the job at different levels. Organize your offers into Good/Better/Best so customers can self-sort, then use elasticity to learn where each tier should sit, how far apart they should be, and which customers want below-baseline or beyond-best variants. If your “Good” doesn’t clearly earn less than “Best,” you won’t have room to stretch the ladder.
WHY IT MATTERS
Directs product architecture: GBB clarifies feature fences and stops endless custom quoting.
Finds hidden demand: A broader “fan” of options usually captures segments you’re currently missing.
Protects margin with evidence: Elasticity by tier/segment keeps you from blanket discounting.
Aligns with real budgets: If a buyer spends $50k on a conference but nitpicks your $10k software, your positioning and tiering, not just price, are off.
CASE FILE
Spotify: Freemium + Multi-Premium Ladders to Test Elasticity
“We’ve always put subscribers on a pedestal. We’ve raised prices recently and seen very strong retention.”
– Alex Norström, Spotify Chief Business Officer, Q2 2025 Earnings Call
Setup. By 2023, Spotify had a classic streaming problem: rising content and operating costs, decent growth, but inconsistent profits. They already had the GBB model in place with tiers at Free (ad-supported), Premium Individual, and multi-account plans. The question was how far they could push price across that ladder without driving users to downgrade or churn.
Move. Spotify raised U.S. Premium prices twice between 2023 and 2024. Crucially, the GBB ladder stayed intact. Those fences made trading down painful (loss of sharing, verification hassles), while trading up felt like a deal on a per-person basis.
Outcome. Even with repeated hikes, Premium subscriptions grew ~11% in 2024 and Spotify posted its first full year of profit. That was with little disruption to plan mix; most users stayed in their chosen tier even as plan prices climbed. In elasticity terms, the “better” and “best” tiers were less price-sensitive than Spotify feared.
Lessons.
Use GBB to measure elasticity, not guess it. Spotify moved prices by 8%-22% per tier and watched where churn and downgrades actually showed up, rather than treating all subscribers as one elasticity number.
Make downgrades hurt on outcomes.
Free isn’t just fewer features, it’s worse quality. Leaving Premium should mean a worse experience, not just one less bell/whistle.
Let “best” carry the heaviest lift.
Household plans with address/eligibility fences captured high-WTP users and absorbed the steepest price jumps, protecting margin and ARPU.
CASE FILE
Goodles + Native: “Premium-First” GBB (and the Elasticity Everyone Underestimates)
“This feels like a huge unlock in terms of growing the category.”
– Goodles co-founder Jen Zeszut
Setup. In mature CPG categories, teams often map GBB as a linear climb: Start “good” (cheap), then “earn” premium later. But modern entrants keep testing (and proving) the opposite. They’re launching with a “best” option to avoid the price-war gravity of the middle. Think Native in deodorant (selling around $13 a stick while mainstream brands sell closer to $4-$5), which has bet on a real premium segment with lower price sensitivity.
Move. Instead of trying to win the same “good” buyer, premium-first brands change the axis (ingredients, identity, nutrition, design) and price like “best from day one. Native’s play was compelling enough that P&G bought the brand for $100M. And, Goodles did something similar in mac & cheese, selling a single box at nearly $3 vs. Kraft’s $1.24 at Walmart, positioning the product as “adult” and “better-for-you” and not apologizing for it.
Outcome. Premium entrants expand the category’s price brand and expose hidden elasticity. Kraft’s share fell to 39% from 45% in 2022 while Goodles has gained 6% since its 2022 debut.
Lessons.
Don’t roadmap GBB as a rite of passage. If you can credibly redefine value, launching at “best” can be the least elastic place to start, while incumbents fight over the most price-sensitive middle.
Map elasticity by segment, not by tradition. Your “best” might be the fastest path to avoiding commodity rules, and your later roadmap can fill “better,” not the other way around.
Framework:
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Price elasticity of demand (E= %Δ in Quantity Demanded / %Δ in Price) captures how volume responds to price. Use it to set the distance between Good/Better/Best so upgrades trade outcomes (speed, scope, certainty), not just extra features.
Takeaway: Quantify tier-level elasticity and move spacing, not just list price.
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Customers value “quality” (e.g., speed, timeliness, resolution) differently. Intentionally “versioned” tiers let high-WTP buyers pay for more outcome while others buy baseline.
Takeaway: Build two to four real versions and let buyers self-select; don’t average to a single number.
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“Fences” (eligibility/usage/term conditions) separate segments so discounts or lower tiers aren’t just freely available to everyone.
Takeaway: Tie fences to provable cost or scarcity (think, lead times, SLAs, capacity windows) so downgrades come with a little pain.
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Because contribution margin = Price − VC, a 1% price lift can move profits more than similar changes in cost or volume (and this is common across industries).
Takeaway: Run breakeven volume math before every move; prioritize precise price tests.
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People often choose the middle when trade-offs are clear; context (e.g., time pressure) moderates the effect.
Takeaway: Make Better the “smart” choice with a clean outcome promise; avoid gimmicky or fake decoys (see Anchoring).
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Necessities (few substitutes) skew inelastic; discretionary items/abundant substitutes skew elastic, and sensitivity shifts over time.
Takeaway: Expect elasticity to differ by tier, segment, and season. Measure it where you sell.
OPERATOR CHECKLIST
◻️ We’re measuring elasticity by tier/segment (not blending the two).
◻️ We know that different tiers must produce different outcomes, not just features.
◻️ Our fences map to real cost/scarcity (SLA, lead time, access).
◻️ We run breakeven CM before moves and test spacing first.
◻️ Our middle offering passes the one-sentence clarity test.
SIGNAL TO WATCH
If most buyers cluster in Good and rarely upgrade, your Best promise isn’t compelling or spacing is too tight.
ONE QUICK ACTION
Draft a 3-tier (Good/Better/Best) that changes the outcome (speed, scope, certainty) at each step, not just the features.
COMMON TRAPS
Cosmetic tiers where Best doesn’t meaningfully change the outcome.
Averaging across unlike segments and channels to get bogus “one number” elasticity.
Moving price without a value story, upgrade path, or guardrails leads to churn and chargebacks.
Letting a new Good tier cannibalize existing customers because fences are weak.
Experiment 1:
LADDER: GOOD/BETTER/BEST
What it’s for: Draft a Good/Better/Best ladder that makes value clear, supports upsell, and gives you a sane starting point for price levels before you run deeper testing.
Who it’s for: A product/offer owner + sales/marketing lead + finance partner who need to align on packaging and price logic quickly.
What it does: Turns “we should have tiers” into a concrete ladder: what each tier promises, why customers will believe it, and how pricing should ladder up.
Use when you need…
Clarity: Makes tier differences explicit and defensible.
Speed: Produces a workable ladder in one session.
Strategic insight: Forces proof and operational reality, not just packaging theory.
Experiment 2:
SURVEY: VAN WESTENDORP
What it’s for: Get a fast, directional read on acceptable price ranges and anchors (PMC/PME/OPP/IDP) for one offer, then translate that into Good/Better/Best pricing hypotheses.
Who it’s for: A pricing/marketing lead who can recruit ~30 respondents and needs a quick price range sanity check without heavy research.
What it does: Facilitates a lightweight price sensitivity survey that yields:
Recommended range (PMC → PME)
Anchors (OPP, IDP)
Practical guidance: where Good/Better/Best might sit and what proof is required.
Use when you need…
Clarity: Converts “pricing feelings” into a defensible range and anchors.
Speed: Lightweight method that doesn’t require advanced research ops.
Strategic insight: Helps you place Better intelligently and understand what proof you need for Best.
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