The Product A/B Testing Roadmap: What to Test First, Second, and Third
The Product A/B Testing Roadmap: What to Test First, Second, and Third helps Shopify merchants move from vague intentions to a practical, ordered plan for improving product performance. If you already...
The Product A/B Testing Roadmap: What to Test First, Second, and Third helps Shopify merchants move from vague intentions to a practical, ordered plan for improving product performance. If you already know you want to run A/B tests but are unsure what to start with, follow a prioritised sequence that balances likely impact, required traffic, implementation effort, and risk. This a/b testing roadmap focuses on product-level experiments: titles, descriptions and prices. It is designed for store owners who need actionable steps to increase conversions while keeping tests reliable and repeatable.
Why follow an a/b testing roadmap?
Without a clear sequence, merchants often run low-impact experiments first or attempt technical changes that need too much traffic. A roadmap gives you a/b testing priority: what to a/b test first for fast wins, what to sequence second to expand gains, and when to tackle harder but high-value changes.
Benefits of a structured approach:
- Faster wins: low-effort, high-impact tests appear early in the programme.
- Cleaner learning: sequential testing isolates effects so you understand what caused a lift.
- Efficient use of traffic: you avoid underpowered tests that waste visitors.
- Repeatable process: teams can scale testing across categories and new product launches.
How to use this roadmap
This product testing sequence assumes you can run A/B tests on product pages and measure conversions reliably on Shopify. If you need a technical primer, see ConvertLab’s fundamentals at /convertlab/guides/ab-testing-fundamentals. Use the following rules for each experiment:
- Define one primary metric per test: typically conversion rate or revenue per visitor (RPV).
- Create a clear hypothesis: what change you expect and why.
- Estimate sample size before launching; do not stop early or peek repeatedly.
- Use segmentation: mobile vs desktop, returning vs new customers, traffic source.
- Keep a changelog: date, hypothesis, variants, results and next steps.
Prioritisation framework: ICE applied to product tests
To decide what to a/b test first, use an adapted ICE score: Impact, Confidence, Ease. Score each potential test 1 to 10 and multiply. This gives you a ranking to apply across product titles, descriptions and prices as well as other ideas.
- Impact: How much revenue or conversion lift you expect if the test wins.
- Confidence: How certain you are the change will help, based on data, user research or best practice.
- Ease: How quick the test is to implement and QA on Shopify.
Examples: a title tweak often scores high on Ease and moderate on Confidence; a price change may score high on Impact but low on Confidence and higher on risk. Use the ICE score to build the testing queue and to decide what to launch first, second and third.
The roadmap overview: first, second, third
Follow this sequence for product-level testing:
- First: product titles — quick to implement and visible in search and collections; often the best first test.
- Second: product descriptions — deeper persuasion, affects SEO and on-page conversion; requires more content work.
- Third: price experiments — highest potential impact on revenue but also highest risk and complexity; requires careful statistical planning.
First priority: Product titles
Why titles first: product titles are high-visibility and low-friction to change. They appear in collection pages, search results, cart line items and often in social shares. Small wording changes can improve click-through rates and conversion because titles set expectations.
Hypotheses to try:
- Adding a clear benefit increases conversions: "Waterproof Running Jacket" rather than "Trail Jacket".
- Using a numeric specification helps scannability: "16oz Stainless Steel Bottle" vs "Stainless Steel Bottle".
- Including brand or material where relevant improves trust: adding "Organic" or "GOTS-certified" for textiles.
Designing the test:
- Primary metric: product page conversion rate or click-through rate from collection pages if you can measure it.
- Secondary metrics: add-to-cart rate, bounce rate, time on page.
- Variants: keep it simple; test the control vs one or two alternate titles. Multiple variants increase sample size needs.
- Traffic split: 50/50 for two variants; avoid tiny allocations.
Sample-size guidance:
Estimate baseline conversion rate for the product or category; many product pages convert between 1 and 4 percent. Use a sample size calculator to determine visitors needed to detect a realistic minimum detectable effect (MDE). For example, to detect a 10 percent relative lift over a 2 percent baseline at 95 percent confidence and 80 percent power, you might need tens of thousands of visitors per variant. If product traffic is low, run title tests across a category or create pooled tests across similar SKUs.
Implementation on Shopify:
- If you use a testing app such as ConvertLab, you can set title variants without duplicating products; the app serves different titles to different visitors and reports results.
- Alternatively, create duplicate products and route traffic via collection filters, but be careful with inventory and SEO implications.
- Ensure your analytics tracks product-level events: product_view, add_to_cart and purchase with product_id or SKU so results map back to variants.
Common pitfalls:
- Changing titles that affect SEO for long periods; short tests are less likely to damage search rankings but avoid changing live canonical tags for weeks.
- Testing titles alone while descriptions or images differ across variants; isolate the variable.
- Small sample sizes: do not draw conclusions from underpowered tests.
Second priority: Product descriptions
Why descriptions second: they allow you to test persuasive copy and information architecture. Descriptions affect on-page readability, perceived value and can answer buyer objections; changes here often increase conversion velocity and average order value.
Hypotheses and experiments to try:
- Feature-led vs benefit-led copy: does emphasising benefits increase conversions more than a list of specs?
- Short summary with bullet points vs long-form storytelling: which performs better for your customers?
- Adding social proof: short testimonial snippet or star ratings in the description may increase trust.
- Structural changes: an FAQ block addressing returns, shipping and fit may reduce hesitation.
Designing the test:
- Primary metric: product page conversion rate or revenue per visitor.
- Secondary metrics: add-to-cart rate, time on page and scroll depth.
- Variant types: A/B two variants, or A/B/n with a small number of thoughtful alternatives. Avoid many variants at once unless traffic is abundant.
Content tips for variants:
- Write hypotheses tied to customer objections: e.g. "If customers worry about fit, adding a sizing FAQ will reduce returns and increase buys."
- Use visual hierarchy: headings, bullet points and bolding can be part of the test; ensure variants contain the same content but different structure to isolate the effect.
- Localise language and measurements for different markets; language that resonates differs between regions.
Sample-size and duration considerations:
Description tests often need similar sample sizes to title tests. If you expect a smaller lift, adjust the MDE. For lower-traffic products, run pooled experiments across a product group, or prioritise products with the highest traffic for early learning.
Implementation on Shopify:
- Use an A/B testing app to swap description HTML for randomised visitors; this preserves inventory and product IDs.
- Ensure rich snippets and structured data are not broken by variant swaps if SEO is a concern.
- Test one content element at a time where possible: headline, intro paragraph or bullet list.
Common pitfalls:
- Long description changes that add new images or scripts. Those add confounding variables; isolate copy changes.
- Running many description experiments in parallel across related products without controlling for overlap; results get noisy.
- Ignoring mobile layout: long descriptions may push calls to action below the fold on mobile; check mobile UX.
Third priority: Price experiments
Why prices third: price testing can generate the largest revenue impact but carries risks. A price change affects margin, customer expectations and can be sensitive for brand positioning. Save pricing for after you have optimised messaging so that price tests evaluate value perception, not confusion.
Types of price tests to consider:
- Absolute price changes: test different price points.
- Price endings: 19.99 vs 20.00; psychological pricing can sometimes move conversion modestly.
- Discount framing: 20 percent off vs save £10; test which framing yields higher revenue and post-purchase behaviour.
- Bundling and volume pricing: test bundles or tiered pricing to increase average order value.
Designing the test:
- Primary metric: revenue per visitor or profit per visitor. Use RPV where margin matters.
- Secondary metrics: conversion rate, average order value, refund/return rate over time.
- Variants: keep variants limited because price tests influence purchase intent strongly; consider 2-3 price points spaced sensibly.
Statistical and financial considerations:
- Power and sample size requirements are often similar to conversion tests but consider revenue variance: this may increase the sample size needed to detect differences in RPV.
- Set guard rails for profitability: a price that increases conversion but reduces margin may be undesirable. Measure profit per visitor, not just revenue.
- Monitor longer-term effects: returns, subscription cancellations or changes in lifetime value may not appear immediately; include a follow-up analysis window.
Implementation on Shopify:
- Use a testing app that supports price variants without creating separate SKUs; some apps adjust displayed price while preserving checkout handling so taxes and shipping calculate correctly.
- Be careful with discount codes, promotions and checkout scripts. Ensure variant prices do not conflict with global discounts.
- Comply with local pricing regulations and store policies; rapid price swings can confuse repeat customers.
Common pitfalls:
- Confounding promotions: running site-wide sales during a price test will muddy results.
- Not tracking profit: a higher conversion at a lower price can reduce gross profit.
- Ignoring stock and fulfilment costs: price optimisation should factor in unit economics.
Shared methodology: test planning and execution checklist
Use this checklist each time you run a product test to keep experiments comparable and trustworthy:
- Define the hypothesis in a single sentence and list expected outcomes.
- Choose the primary metric and any guard-rail metrics (e.g. returns, AOV).
- Calculate required sample size using baseline metrics and MDE; record the calculation.
- Decide traffic allocation and test duration; commit to not peeking frequently.
- Set up analytics: ensure events, product IDs and revenue data are accurate and consistent.
- QA variants on devices and browsers; verify checkout flow and tracking for each variant.
- Launch and monitor for external issues: site outages, ad campaign changes or promotions.
- Run significance analysis at the pre-defined end; report results and next steps.
How to calculate sample size quickly
Practical approach to sample-size estimation:
- Estimate baseline conversion rate from your product or category. If unknown, use broader store average for the category.
- Decide the minimum detectable effect: a realistic relative lift you care about, often 10 to 20 percent for product tests.
- Use an online calculator or a pre-built spreadsheet to compute visitors needed per variant for 80 percent power and 95 percent confidence.
Example: baseline conversion 2 percent, MDE 15 percent relative lift: you will need a substantial number of visitors per variant, often multiple thousands. If you do not have enough traffic, pool similar products or increase the expected duration.
Interpreting results: statistics, practical significance and follow-ups
Statistical significance is necessary but not sufficient. When a result shows statistical significance consider:
- Practical significance: is the uplift large enough to justify a permanent change? Does the lift pay for the effort or margin impact?
- Segmentation: did the effect come from desktop only, organic traffic or new customers? Use segments to refine roll-out strategy.
- Duration and seasonality: did the test run across a representative period? Short bursts during unusual traffic patterns can mislead.
- Retention effects: for price and policy changes, examine post-purchase metrics over time.
If a test does not reach significance, do not discard the idea immediately. Check power, variability and whether the hypothesis was poorly specified. You can refine and re-run with a clearer design.
Advanced techniques and cautions
Once you are comfortable with the basic roadmap, consider advanced methods to speed learning or target personalisation:
- Sequential testing and false discovery control: apply methods that allow safe early stopping when used correctly; avoid repeated peeking that inflates Type I error unless proper sequential tests are used.
- Multi-armed bandits: useful for revenue optimisation when you want to shift traffic quickly to better performers; however, they complicate statistical inference and are best used after initial A/B learnings.
- Personalisation and segmentation: run tests for different audience cohorts to build targeted experiences; for instance, test descriptions for new visitors vs returning customers.
- Cross-product tests: once you find winning copy or pricing treatments, test them across categories to validate generalisability.
Be cautious with experimentation overload: if you run many tests that affect the same metric concurrently, interpret results carefully and prefer orthogonal tests when possible.
How ConvertLab can fit into this roadmap
ConvertLab is a Shopify app built to make product-level A/B testing easier: swapping titles, descriptions and prices without creating duplicate products; tracking results; and reporting on key metrics such as conversion rate and revenue per visitor. Use ConvertLab to implement the tests described in this a/b testing roadmap while keeping inventory and checkout flow intact.
Use the app to:
- Roll out title variants across collections quickly to capture early wins.
- Create pooled tests for descriptions across many SKUs when single-product traffic is low.
- Run controlled price variants and measure revenue and profit impacts accurately.
ConvertLab integrates with Shopify analytics and common attribution setups to reduce QA work and speed analysis; it is a tool to implement the methodology described here, not a substitute for planning and hypothesis discipline.
Case study examples
Illustrative examples to show the roadmap in practice:
- Title win: a store selling reusable water bottles tested titles that added "BPA-free" and "18-hour insulation". The new title increased add-to-cart rates by 12 percent and page conversion by 9 percent across the category.
- Description shift: a lifestyle brand tested benefit-led versus feature-led descriptions for a knit jumper. Benefit-led copy focussed on warmth, fit and care increased conversion by 14 percent and reduced returns slightly due to clearer expectations.
- Price test: a kitchenware seller tested three price points for a popular pan. The middle price produced the highest revenue per visitor; the lowest price raised conversion but reduced profit per sale. The store rolled out the middle price and added a bundled accessory at a small up-sell to improve margin.
These examples highlight sequencing: message clarity first, deeper persuasion next, price optimisation once value perception is aligned.
Common questions from merchants
How long should a product test run?
Run tests long enough to collect the required sample size and span typical weekly cycles. For many stores that means 2 to 4 weeks, sometimes longer for lower-traffic items. Avoid stopping early even if trends look promising.
Can I test multiple elements at once?
Yes, but this makes it harder to attribute the cause of a change. If the goal is rapid optimisation, you can run combined changes as "experience tests", then run follow-up A/Bs to isolate high-impact elements. For learning, prefer single-variable tests where feasible.
What if my product has low traffic?
Pool similar products into a category-level experiment, focus on titles first for smaller sample needs, or run longer tests. You can also prioritise higher-traffic SKUs for faster iteration.
Conclusion and next steps
Follow this product testing sequence to build a disciplined CRO programme: start with titles because they are quick to implement and often deliver early wins; move to descriptions to deepen persuasion; then test prices once messaging has aligned with perceived value. Use a prioritisation framework such as ICE to decide which products to test first, calculate sample sizes up-front and keep experiments isolated and well-documented.
Next steps:
- Audit your top 20 products by traffic and score potential tests with ICE.
- Create a testing calendar: schedule title experiments first, descriptions second, prices third.
- Set up tracking and QA; calculate sample sizes and commit to consistent stopping rules.
Call to action
This roadmap plus ConvertLab's tools equals systematic improvement. Start with titles, move to descriptions, then test prices. If you want a straightforward way to run product-level A/B tests on Shopify, try ConvertLab on the Shopify App Store: https://apps.shopify.com/ab-tester-improve-conversion.
📚 Want to dive deeper?
This post is part of our comprehensive A/B testing series.
Read the Complete Guide to A/B Testing Product Descriptions →ConvertLab Team
The ConvertLab team helps Shopify merchants optimise their product listings through data-driven A/B testing. Our mission is to make conversion rate optimisation accessible to stores of all sizes.
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