Imagine you’re running a SaaS product with steady demand and predictable sign-ups. While growth hasn’t stalled, the revenue per customer hasn’t moved in the last quarter.
Your current price works, yet you’re unsure if it’s the right price.
You see two obvious paths to test pricing optimization.
You could increase prices and test customer willingness. Or you could lower prices to see if higher volume and faster adoption offset the drop in per-user revenue.
So instead of guessing, you run a pricing experiment.
You test a higher price for a specific segment, introduce a lower entry tier for new users, and track how each change affects conversions, churn, and expansion revenue.
Below, we show you how to build a repeatable pricing experimentation playbook. One that helps you test different pricing strategies and maximize both revenue and customer lifetime value over time.
Summarize this article for me please⭐ Featured Template
ClickUp Product Pricing Template helps you manage your entire product catalog pricing in one place, without losing track of changes, accountability, and go-live.
Use it to store every product as a task and track details like SKU, brand, product type, and variations such as size or type. It is especially handy when you are jumping between categories and need a quick peek at what’s in the catalog right now.
How to Create a Pricing Experiment Playbook: Strategies & Examples
- ⭐ Featured Template
- What Are Pricing Experiments?
- Key Components of a Pricing Experiment Playbook
- Step-by-Step Pricing Experiment Process
- Pricing Experiment Frameworks to Follow
- Examples of Successful Pricing Experiments
- Common Mistakes in Pricing Experiments
- Tools for Conducting Pricing Experimentation and Tracking
- Level Up Your Pricing Experiments With ClickUp
Summarize this article for me pleaseWhat Are Pricing Experiments?
Pricing experiments are tests where you intentionally vary prices (or discounts, bundles, etc.) for some customers and measure how behavior and business outcomes change to find better pricing.
In other words, it asks, ‘What happens to revenue, profit, and customer behavior if we charge this instead of that?’
🎯 Example: Say you sell a coding course.
- Current price (Control – Group A): $100
- Test price (Treatment – Group B): $120
Half the visitors see the current price of $100, and the other half sees the test price of $120.
After 1 week:
| Group | Price | Visitors | Purchases | Conversion | Revenue |
| A | $100 | 1,000 | 60 | 6% | $6,000 |
| B | $120 | 1,000 | 50 | 5% | $6,000 |
- At $100, more people buy (higher conversion)
- At $120, fewer people buy, but each sale is worth more
In this case, revenue is the same. If your costs are flat, you might prefer $120 (fewer students to support for the same money).
⚡ Template Archive: Free Growth Experiment Templates to Grow Your Business
Summarize this article for me pleaseKey Components of a Pricing Experiment Playbook
Your pricing experiment playbook should include the following elements, while also being aligned with your broader pricing strategy:
- Pricing objective: A clearly defined goal for the experiment, such as improving conversion, increasing ARPU, reducing churn, or validating expansion pricing
- Pricing hypothesis: A testable statement that links a specific pricing change to an expected business outcome and defines acceptable downside
- Target customer segment: The exact audience the experiment applies to, such as new users, SMBs, power users, specific geographies, or sales-led accounts
- Value metric under test: The unit customers pay for or perceive value from, such as seats, usage, features, transactions, or API calls
- Pricing lever being tested: The specific variable being changed, including price point, packaging, usage limits, billing cadence, discounts, or plan structure
- Experiment design and methodology: The testing approach used, such as A/B pricing pages, cohort-based rollouts, geo tests, or controlled feature gating
- Primary success metric: The single metric used to judge success, such as revenue per visitor, conversion rate, average deal size, or retention
- Guardrail metrics: Secondary metrics monitored to catch negative impact early, including churn, activation drop-off, support tickets, or sales cycle length
- Customer trust and exposure rules: Guidelines defining who sees the experiment, how long it runs, and how customer trust is protected through communication or grandfathering
- Decision and rollout criteria: Predefined rules that determine whether the strategic pricing change is rolled out, iterated on, or reverted. This removes bias and enables confident pricing decisions based on data
👀 Did You Know? Prices like ‘$9.99’ are a psychology hack.
Your brain anchors on the first digit, so $9.99 feels as if it’s not almost the same as $10, even though it’s one cent away.
Summarize this article for me pleaseStep-by-Step Pricing Experiment Process
Below are the steps to implement an effective pricing experiment that aligns with your customer expectations:
Step 1: Research and hypothesis formation
Firstly, you need a base-level view of how your current pricing is behaving. Start by pulling a specific data set (not a full company audit). This includes:
- Conversion by plan and price points over the last few cycles
- Upgrade and downgrade patterns across tiers and key customer segments
- Churn reasons that mention price or value
- Win-loss notes from sales that talk about pricing or competitors
- Competitor price pages, pricing model structure, and packaging for comparable tiers (including whether they use usage-based pricing)
Then zoom in on the main pricing tension. If you cannot state the tension in one or two sentences, you are not ready to test it.
Some questions that will help you at this stage are:
- Where are customers clearly over-choosing one plan relative to others or specific price points?
- At what price points do we see sharp changes in conversion or churn?
- Which customer segments complain about price, and which never mention it?
- Are we obviously cheaper or more expensive than key competitors for similar value and pricing model (for example, seats vs usage-based pricing)
How ClickUp helps
You need a single source of truth to store this research. For this, use ClickUp Docs, the best AI-powered and collaborative documentation hub.
Convert the Doc into a pricing research hub with Subpages for competitor scans and experiment write-ups. You can embed usage metrics, customer acquisition cost, etc, alongside experiment results.
Within Docs, you can embed YouTube videos, Google Sheets, tables, PDFs, and more for added context.
Here’s the best part: Since Docs is AI-powered with ClickUp Brain, the benefits multiply.
You can summarize past experiments, pull out patterns that inform future pricing strategies, or even draft ideas for testing variable pricing.
👀 Did You Know? Two people can build the same Instacart cart from the same store and still see different totals. A recent investigation using 437 shoppers across four cities found that about 75% of items showed different prices, with an average gap of around 13% and some differences reaching 23%.
Step 2: Design the experiment
Now, separate the impact of price from everything else!
In other words, the offer, messaging, funnel, and experience all stay the same while you only move the number and who sees it.
You can structure that in a few ways:
A/B testing approaches
- Segment-based testing: Show different price points to different customer segments, for example, small vs mid-market accounts, or self-serve vs sales-assisted. Great when you already segment your product and want to tune price sensitivity by group
- Cohort-based testing: Launch a new price for a well-defined cohort that starts at a specific time, then compare its behavior over weeks or months to a previous cohort on the old price (useful for subscription or usage-based products where long-term patterns matter)
- Geographical price testing: Run a higher or lower price in one region while holding another region at the current price. Works well when you have clean geo splits and want to contain risk while still getting live market feedback
- New vs existing customer testing: Apply new pricing only to new customers and keep existing customers on the old structure. This avoids backlash and lets you measure how the new price affects acquisition before touching your base
Experimental controls
- Keep everything except price constant (same features, messaging, checkout flow, promotions)
- Establish a clear control group that stays on the current pricing for the full duration of the test
- Define the minimum sample size and test duration you need for a meaningful read (no one wants to overreact to early noise 😮💨)
🚀 ClickUp Advantage: Use the Table View to list all experiments each department is undertaking. Add assignees, statuses, due dates, and priorities so nothing is missed.
It behaves like a flexible spreadsheet, but each row remains a real task that you can open, comment on, and update in context.
Use the Table View to list all experiments each department is undertaking. Add assignees, statuses, due dates, and priorities so nothing is missed.
Step 3: Implement the test
At this stage, the setup should answer one question: ‘What happens for this exact user at this exact price?’
In a nutshell:
| Area | Your set-up | Questions to ask |
| Price configuration | Define control and variant prices in flags or config, keyed to an experiment ID | Can I switch each variant on or off for the right cohort without a new release? |
| Customer-facing surfaces | Update pricing page, in-app upsell, checkout, emails, and invoices | Does the same user ever see two different prices for the same offer? |
| Billing and payments | Pass variant info into billing and payment systems | Do taxes, currency, rounding, coupons, and proration still behave correctly per variant? |
| Analytics and logging | Attach experiment ID and variant to key events and funnels | Can I trace a full path from ‘saw price X’ to ‘was charged X’ for any user in the test? |
| Safeguards and rollback | Create feature flag kill switches and a short rollback runbook | If something breaks, do we know exactly what to flip first, second, and third? |
Run a small pilot. Push real transactions through each variant, read the receipts, and scan the logs.
How ClickUp helps
Instead of manually coordinating setup across teams, you can automate the operational backbone of the test. ClickUp Automations help you:
- Trigger implementation tasks automatically when an experiment status moves to “Approved”
- Assign owners for pricing page updates, billing configuration, analytics tagging, and QA
- Enforce sequencing so billing and analytics are set before the experiment goes live
- Notify stakeholders instantly if a Rollback tag is added to the task, a tag that is titled Rollback could trigger a notification ot stakeholders
You can also codify safeguards directly into the workflow.
This video shows you how to automate your daily workflows:
⚡ Template Archive: Free ClickUp and Google Analytics Report Templates
Step 4: Analyze results
This step answers whether the pricing experiment has done its job. What does that say about how you should price going forward?
The easiest way to stay honest here is to look at the results in a few layers.
Use this as your walk-through:
| Layer | What you check | Handy tools |
| Data sanity | Traffic split, cohort balance, event fires, no weird sample ratios | Amplitude, Mixpanel, GA4 |
| Core business outcome | Revenue or profit per visitor, lift vs control, basic confidence check | BigQuery, Snowflake, Looker, Tableau, Excel |
| Behavior by segment | Differences by plan, region, channel, new vs existing customers | Segments in Amplitude or Mixpanel, SQL |
| Short-term risk signals | Refunds, downgrades, early churn, “too expensive” tickets | Zendesk, Intercom, Help Scout |
| Shape over time | Early spike or fade, weekday vs weekend patterns | Time series charts in BI or product analytics |
Step 5: Report and share learnings
If someone who was not in the room read your experiment summary a month from now, could they make the same decision you just did? If the answer is no, the test is not really finished.
Reporting is more inclined to capture a decision that can withstand time and context.
In a nutshell:
| Section | What you write | Example snippet |
| Headline | One or two sentences on the test, audience, and decision | ‘We tested raising Pro from 39 to 45 dollars for new self-serve signups. We are rolling it out in the US and the EU.’ |
| Key numbers | Primary metric plus one or two supporting metrics, with direction | ‘Profit per new customer up 6%. Conversion down 0.3 points (not significant). Refund rate flat.’ |
| Who reacted how | One short view by segment | ‘Teams under 5 seats were slightly more sensitive. Teams above 10 seats barely reacted to the new price.’ |
| Risks | Any warning signs you will keep an eye on | ‘Mild uptick in “too expensive” tickets from very small teams, but volumes are low so far.’ |
| Next move | Clear action and any follow-up experiment it unlocks | ‘Roll out to all new self-serve signups in the US and EU. Next test: steer tiny teams toward the Starter tier.’ |
A finished summary might look like this in prose:
‘We raised Pro from $39 to $45 for new self-serve signups in the US and EU. Profit per new customer improved by about 6 percent with no meaningful change in early churn, so we are rolling this out to all new self-serve customers in these regions.
Very small teams showed slightly more price sensitivity and generated a few more “too expensive” tickets, but larger teams barely reacted. We will monitor support for four more weeks and design a follow-up test that nudges very small teams toward a cheaper Starter tier instead of discounting Pro.’
How ClickUp helps
When you’re running pricing experiments, scattered notes and static reports quickly fall apart. Pricing results are nuanced and time-bound.
A spreadsheet can show numbers, but it cannot preserve why a decision was made, how different segments reacted, or which risks were consciously accepted.
You need a single view that keeps metrics, context, and decisions tied together over time.
ClickUp Dashboards serve as your central reporting and decision log for pricing experiments. They help you:
- Compare pre- and post-test performance across cohorts
- Track primary and guardrail metrics in real time
- Monitor segment-level reactions like churn, refunds, or support tickets
- Spot early warning signals before a rollout scales
⭐ Bonus: Pair Dashboards with AI Cards to turn raw data into decision-ready summaries. Here’s how to use this combo 👇
📮 ClickUp Insight: Our AI maturity survey shows that 38% of knowledge workers don’t use AI at all, and only 12% have it fully integrated into their workflows.
This gap is often caused by disconnected tools and an AI assistant that functions as a mere surface layer. When AI isn’t embedded where tasks, docs, and discussions happen, adoption becomes slow and inconsistent. The fix? Deep context!
ClickUp Brain removes the context barrier by living inside your workspace. It is accessible via every task, doc, message, and workflow, and knows exactly what you and your team are working on.
Step 6: Scale or sunset the experiment
Every pricing experiment should end with a clear outcome. If the result does not lead to a rollout, iteration, or shutdown, the experiment has failed to do its job.
If the experiment meets its primary success metric and stays within guardrails, scale it deliberately.
If results are neutral or mixed, do not force a decision. Sunset the experiment, document the learning, and refine the hypothesis.
If the experiment clearly underperforms or triggers guardrail breaches, shut it down quickly. Revert affected customers, communicate transparently where needed, and record why the test failed. These negative results are just as valuable because they narrow future pricing options and prevent repeated mistakes.
Scaling grows revenue. Sunsetting protects trust. Both are signs of a healthy pricing experimentation practice.
⚡ Template Archive: Free Business Playbook Templates to Streamline Operations
Summarize this article for me pleasePricing Experiment Frameworks to Follow
Here are some pricing experiment frameworks you can try:
1. Price Sensitivity Testing (WTP Bands)
👀 What it tests: How much customers are willing to pay before demand drops.
🛠️ How it works: Test multiple price points across cohorts and measure conversion, revenue per visitor, and churn.
🎯 Best used when:
- You’re unsure if pricing is too low or too high
- You want to establish price ceilings and floors
2. Free-to-Paid Boundary Testing
👀 What it tests: Where to draw the line between free value and paid value.
🛠️ How it works: Move features, usage limits, or support access between free and paid tiers and track conversion.
🎯 Best used when:
- Free users engage heavily but don’t convert
- Free tier is cannibalizing revenue
⚡ Template Archive: Top Price List Templates in Word, Google Docs, and ClickUp
3. Anchoring and Decoy Pricing Framework
👀 What it tests: How context influences plan selection and perceived affordability.
🛠️ How it works: Introduce anchor or decoy plans to guide users toward a target tier.
🎯 Best used when:
- Most users cluster around one plan
- Mid-tier adoption is weak
🧠 Fun fact: In 1999, Coca-Cola reportedly explored vending machines that could charge more when the weather got hotter. It was basically ‘surge pricing’… but for soda, years before apps made it normal.
4. Value Metric Experimentation
👀 What it tests: What unit of value customers are most comfortable paying for.
🛠️ How it works: Experiment with pricing based on seats, usage, outcomes, or volume instead of a flat fee.
🎯 Best used when:
- Usage varies widely across customers
- Power users feel undercharged or overcharged
ClickUp uses a tiered packaging and anchoring strategy.
The pricing page shows multiple tiered plans (Free Forever, Unlimited, Business, Business Plus, Enterprise). Each tier has defined feature sets and usage limits that escalate with price.
This is a classic packaging and plan structure framework, where value is conveyed through plan design rather than just sticker price.
By offering a Free Forever plan with significant capabilities alongside paid tiers, ClickUp also uses anchoring—the free option and lower tiers make the higher-value plans feel more compelling when compared side by side.
Summarize this article for me pleaseExamples of Successful Pricing Experiments
Let’s see how different companies have implemented pricing playbook experiments: 🌸
1. Uber surge pricing (Matching supply and demand in real time)
Uber’s surge pricing (a dynamic multiplier applied during high demand) was originally rolled out as an experiment to see if price signals could rebalance drivers and riders.
Internal analysis showed that higher prices during peak periods led some riders to wait while simultaneously attracting more drivers into the surge area. This, in turn, reduced wait times and increased the number of completed trips during busy events.
2. Netflix’s new pricing segment (Low-tier mobile-only plan)
Netflix began testing a lower-priced subscription plan in India and other Asian markets. The mobile-only plan starts at $2.80/month.
The catch is that you could only stream on one device. The goal here was to make it more accessible in a price-sensitive market. And it seems to have worked pretty well! Netflix said the results were strong enough that they might bring similar low-cost plans to other countries.
3. The Economist’s three-tier offer (Decoy pricing to push a premium tier)
The Economist famously ran a three-tier subscription offer:
- Web only: $59
- Print only: $125 (the ‘decoy’)
- Web plus print: $125
When Dan Ariely tested this structure with students, adding the ‘print only for $125’ decoy shifted people toward the premium bundle. The share choosing the $125 web plus print offer jumped from 68% to 84%.
Revenue per 100 customers rose because many more people picked the higher-priced option without changing the actual top price.
Summarize this article for me pleaseCommon Mistakes in Pricing Experiments
Here are some common pitfalls you must be aware of:
❌ Insufficient sample size
Running experiments with too few customers leads to unreliable results. You might conclude a price change was successful or unsuccessful when the outcome was just a random variation.
✅ Fix: In a pricing tool, calculate the required sample size before launching using statistical power analysis.
Aim for at least 80% statistical power and account for your baseline conversion rate, expected effect size, and desired confidence level (typically 95%). Use online sample size calculators or consult with a data analyst to determine the minimum number of customers needed in each group.
❌ Not accounting for cannibalization
A new pricing tier might show strong adoption, but you’re not measuring how many customers downgraded from higher tiers or would have chosen more expensive options.
✅ Fix: Measure net revenue impact across all tiers, not just adoption of the new option.
Calculate whether the new pricing structure increases total revenue or just shifts customers around. Consider cohort analysis to see natural upgrade/downgrade patterns.
❌ Not segmenting properly
Treating all customers the same misses important differences in how various groups respond to pricing. New customers, power users, different industries, or geographic markets may react very differently.
✅ Fix: Define key customer segments upfront (new vs. returning, SMB vs. enterprise, geography, usage level) and ensure each segment has sufficient sample size. Analyze results both overall and by segment. This shows whether a price change works for everyone or just specific groups.
❌ Selection bias in test groups
Non-randomized groups mean you’re measuring differences between customer types rather than the effect of your pricing change.
✅ Fix: Use proper randomization to assign customers to control and treatment groups. Validate that groups in your A/B tests are balanced across key characteristics (demographics, past purchase behavior, acquisition channel).
For existing customers, consider matched pair designs where similar customers are split between groups.
Summarize this article for me pleaseTools for Conducting Pricing Experimentation and Tracking
To run pricing experiments reliably, you need tools that can control exposure, record what each user saw, and connect that to what they ultimately paid. They include:
1. ClickUp (Best for running pricing experiments end-to-end in one converged AI-powered workspace)
Ask a teammate to walk you through the last pricing experiment and see if they pull a trail of documents, BI dashboard, and mail threads. Heaven forbid they do, and you’d know how broken the system is!
ClickUp steps in as a Converged AI Workspace to pull that entire loop into one system to end work sprawl for good. Research and hypotheses sit in version-controlled Docs, execution runs through Tasks, and performance shows up on Dashboards. And to connect all of it, you have the world’s most contextual and ambient work AI sitting atop, removing the lengths of context switching permanently.
Let’s now take a look at it:
Give every pricing experiment an owner and a path
ClickUp Tasks helps you turn a pricing idea into a concrete concept ready to be shipped. Simply put, each experiment becomes a task with assignees, status, priority, and dates to steer clear of any confusion about who moves it forward and when.
Under each task, you can also add subtasks like ‘set up pricing in the billing system’ or ‘analyze results.’ And if you want better personalization, use ClickUp Custom Fields for all relevant factors you want to count in, like experiment ID, price variant, or target segment.
To keep an eye on progress, use the Tasks comment section to loop in assigned teammates and have open conversations about relevant experimentation topics.
Use built-in AI to design, run, and learn from every pricing experiment
ClickUp Brain can act like a pricing ‘copilot’ that turns jumbled data into testable ideas. It scans your existing docs, tasks, notes, and even meeting summaries to surface recurring pricing pain points (like ‘too expensive for small teams’ or ‘confusing tiers’) and turn them into hypotheses.
From there, use it to sketch out experiment structures (which segments to target, what variants to test, and which metrics to watch) based on the context it pulls from your workspace.
After the experiments end, it can draft customer-facing FAQs, pricing page copy, or internal sales enablement based on the chosen pricing, all in your brand voice.
Over time, since everything is stored in ClickUp, Brain becomes a searchable record of past pricing tests. That means you can ask, ‘What happened last time we tried X?’ and avoid reinventing the wheel every time.
Turn conversation into pricing insight with ClickUp’s AI desktop companion
ClickUp Brain MAX, the most reliable AI desktop companion, can pull context from your hypotheses, experiment tasks, dashboards, and even external sources. You can toggle between external AI models such as ChatGPT, Claude, and Gemini—depending on the task at hand.
Ask, ‘What pricing experiments have we run on the Pro plan in the last six months?’ and BrainGPT can outline the full pricing experiment framework, key results, and decisions in one summary.
With Connected Search, when you connect each app to ClickUp, it allows you to find information across external apps.
Example: Find every doc where customer success mentioned small teams pushing back on price. It will search across ClickUp and connected devices using Connected Search. You have the exact evidence to run pricing experiments.
BrainMAX’s Talk to Text makes this even smoother! That means, sales or CS can dictate notes right after a call, and Brain MAX links that insight to the right experiment or turns it into a fresh hypothesis to test.
Deploy Super Agents as your AI coworkers for pricing experiments
ClickUp Super Agents act like autonomous AI teammates embedded directly inside your workspace. They have access to the data from your Workspace and connected apps that you give to them.
They only build memory if you enable it in the individual Agent’s profile.
📌 Example of how Super Agents support pricing experimentation.
You can configure Super Agents to:
- Monitor live experiments and flag when guardrail metrics like churn or refunds cross thresholds
- Track experiment timelines and notify owners when analysis or decisions are overdue
- Compare current results with past pricing tests and surface historical context automatically
- Suggest next steps, such as scaling, iterating, or sunsetting, based on predefined rules
Build your first Super Agent with ClickUp 👇
ClickUp best features
- Estimate experiment effort: Use ClickUp Time Estimates to size each pricing test, so you can balance impact vs. effort and avoid overloading the team with more experiments than the quarter can handle
- Capture ideas and feedback: Collect new pricing ideas or customer reactions through ClickUp Forms, turning every submission into a task with fields like target segment, proposed price points, and expected impact
- Design pricing models visually: Use ClickUp Whiteboards to sketch pricing ladders, customer segments, and upgrade paths, then convert sticky notes and shapes into tasks so brainstorming flows straight into execution
- View experiments from every angle: Switch between ClickUp Views like List, Board, Calendar, and Table to organize experiments by stage, owner, launch date, or region, without losing the underlying data or context
ClickUp limitations
- The depth of features in ClickUp can overwhelm first-time users
ClickUp pricing
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Best for many large teams
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ClickUp Brain is a no Brainer. One AI to manage your work, at a fraction of the cost.
ClickUp ratings and reviews
- G2: 4.7/5 (10,800+ reviews)
- Capterra: 4.6/5 (4,500+ reviews)
What are real-life users saying about ClickUp?
A G2 reviewer says,
2. Chargebee (Best for testing and rolling out subscription pricing directly at the billing layer)
Chargebee is a subscription billing and revenue management platform built for recurring revenue businesses. It supports multiple pricing models (flat, tiered, volume, usage-based, and hybrid) and lets you spin up new plans, coupons, trials, and regional price variants.
Teams use Chargebee at the billing layer to A/B test pricing and packaging, compare cohort-level revenue impact, and roll successful changes out quickly, often in under an hour, while keeping invoicing, taxes, and revenue recognition consistent.
Chargebee best features
- Create, clone, and modify plans, add-ons, and currencies in Chargebee’s product catalog to roll out new price points or entire pricing models
- Create versioned prices for the same plan (for example, Pro at $49 and Pro at $59) with effective dates and regional overrides to introduce new levels without breaking existing SKUs
- Grandfather existing subscribers on their current pricing while selectively migrating chosen cohorts using filters like MRR band, geography, or contract term
Chargebee limitations
- Checkout and subscription management on mobile can feel disjointed, with flows that break or behave inconsistently across devices
- Reporting is often unreliable, making it hard to trust subscription and revenue analytics without extra verification
Chargebee pricing
- Starter: Free (on the first $250K of cumulative billing, then 0.75% on billing)
- Performance: $7,188/year for up to $100K billing per month
- Enterprise: Custom pricing
Chargebee ratings and reviews
- G2: 4.4/5 (900+ reviews)
- Capterra: 4.3/5 (90+ reviews)
What are real-life users saying about Chargebee?
A Capterra reviewer says,
3. Recurly (Best for iterating subscription plan versions with lifecycle and retention controls)
Recurly is a subscription management platform where you can spin up new plan versions at different dollar amounts or billing intervals, keep legacy plans live for existing customers, and route only specific segments onto the new pricing.
Its revenue and churn analytics then enable you to compare MRR, upgrades, downgrades, and cancellations by plan version, allowing each pricing experiment to be judged on its actual impact on subscription and retention.
Recurly best features
- Manage subscription lifecycle with Recurly Subscriptions to test new plans, prices, and billing terms while proration, renewals, and dunning are handled consistently in the background
- Sell and experiment on Shopify with Recurly Commerce to launch subscription products, tweak offers, and compare how different price points or bundles perform at checkout
- Drive upsells and retention with Recurly Engage to trigger targeted upgrade, win-back, and discount campaigns based on subscriber behavior at each pricing tier
Recurly limitations
- Handling price localization and tax-inclusive versus tax-exclusive options can feel cumbersome when you are trying to move quickly on new pricing
- Setting up structured pricing A/B tests is not straightforward, so a lot of experimentation logic still has to live outside the tool
Recurly pricing
- Custom pricing
Recurly ratings and reviews
- G2: 4/5 (200+ reviews)
- Capterra: 4.6/5 (60+ reviews)
What are real-life users saying about Recurly?
A Trustradius reviewer says,
Summarize this article for me pleaseLevel Up Your Pricing Experiments With ClickUp
A pricing experiment playbook on its own is just theory.
The real shift surfaces when you have a place where every step from the research and hypotheses to the metrics and final call lives together.
And that’s what ClickUp promises (plus delivers). It gives you one Converged AI Workspace where pricing ideas start in Docs, turn into owned work in Tasks, show their impact on Dashboards, and get stitched together by ClickUp Brain and Brain MAX into a living memory of everything you have tried.
No hunting through random decks, no guessing what happened last time you changed a price.
Everything you need to stay organized and get work done.
