Product quiz metrics: what to track to convert better

Four metrics predict whether a product recommendation quiz will move revenue or quietly underperform: completion rate, quiz-to-cart conversion, average order value, and recommendation precision. The platform data is unambiguous on the targets: top-converting quizzes hit 10.6% conversion when configured with a single results page, 71% of high performers make email capture required, and the 9-12 question sweet spot averages 11.0% completion-to-purchase. This guide walks through where to find each metric in the RevenueHunt dashboard, what target to aim for, and what to fix when the number lands below it.

For the foundations that produce these metrics, see how to build a successful product recommendation quiz. For the mistakes that quietly drag the numbers down, see quiz creation mistakes that hurt your ecommerce sales.

Where to find the data

The Metrics panel has the same three sub-sections on every version of the RevenueHunt app (Responses, Analytics, Drop-off), but the location of the panel and the depth of order-level data differ between Built for Shopify and the legacy / multi-platform versions.

On Built for Shopify

Two ways to open the Metrics panel:

• Dashboard sidebar: click the Analytics tab.
• Per quiz: select the quiz, click the ... menu, then Analytics or Responses.

Order tracking and revenue attribution work natively because the app is connected to Shopify Customers under App Settings. The Analytics dashboard surfaces customisable default cards (Quiz Responses, Number of Orders, Total Orders Value, Drop-off Rate, Selected Choices), and the Responses panel has no 100-item cap. Data refreshes in real time for response events, on a roughly 5-minute cycle for cached aggregates, and once daily for order and revenue sync.

On Shopify Legacy

Open the Metrics panel from the Dashboard. The three sub-sections are the same as Built for Shopify (Responses, Analytics, Drop-off).

Revenue tracking is available, but it requires a one-time setup step: connect the app to Shopify Orders via the Connect tab. Once connected, the Analytics panel surfaces the same quiz-attributed order count and total order value that Built for Shopify shows by default; before connecting, Analytics shows engagement and cart-level data only.

On WooCommerce, Magento, BigCommerce and Standalone

Open the Metrics panel from the Dashboard with the same three sub-sections (Responses, Analytics, Drop-off). The structural difference: no native order or revenue integration on these platforms. Analytics shows Starts, Responses, Completion Rate, Carts Count and Cart Values, but no post-purchase order tracking surfaces inside the app. For cart-to-purchase attribution, cross-reference quiz responses (via CSV export) with your storefront platform’s order data, or use the Google Analytics integration for event-level tracking.

📝 Note for Legacy and multi-platform versions: the Responses panel caps at the most recent 100 (Built for Shopify has no cap). For deeper historical analysis, export to CSV regularly. The export covers the last 90 days.

The three sub-sections in detail

• Responses. Individual quiz submissions organised by date and timestamp. Click any response to see what choices the customer made, which products were upvoted or excluded, which products were recommended, and which customer tags were applied through to the results page.

  

• Analytics. Graphs and KPIs covering quiz engagement, conversion rate, and (on Built for Shopify) revenue. Compare any date range against a previous period or another quiz on the same store.

  

• Drop-off. Percentage of users leaving the quiz at each question, shown as both a table and a funnel graph. The single most useful diagnostic when completion rate is below benchmark.

  

Download raw response data

If the in-app views don’t go deep enough, download all responses as a CSV file and analyse them in Google Sheets or Microsoft Excel. The export covers the last 90 days on the legacy and multi-platform versions and has no time cap on Built for Shopify.

Advanced tracking via Google Analytics and Facebook Pixel

For deeper attribution and choice-level event tracking, RevenueHunt integrates with Google Analytics 4 and Facebook Pixel. These integrations surface click rate per choice, traffic-source attribution, revenue per source, and per-step funnel data that the in-app Analytics panel doesn’t expose. On the legacy and multi-platform versions, the GA4 integration is the most reliable way to close the cart-to-purchase loop without leaving the analytics layer.

What to analyse and how to improve?

1️⃣ Customer Engagement

Quiz Starts and Completions
Tracking the number of quiz starts and responses/completions is fundamental. High start rates indicate good initial engagement, but the completion rate is what truly matters for conversions. If you notice a drop-off between starts and completions, it might be time to refine your quiz flow or simplify the questions.

📝 Note: Quiz Starts refers to the number of people who have engaged with the quiz by passing the first slide. Quiz Responses refers to the number of people who have completed the quiz, aka. reached the quiz results page. If the customer closes the quiz before the results page, this will not be counted as a response/completion.

Completion Rate
Completion Rate is the percentage of quiz-starters who reach the results page. It’s the cleanest single signal of whether your quiz is engaging and user-friendly. Healthy ranges vary by category and quiz length, but as a rule of thumb: above 70% indicates a well-tuned quiz; below 50% means the quiz is too long, too text-heavy, or asking for personal data too early. Mid-quiz drop-off typically clusters around questions where the friction is structural — a mandatory phone-number field, a question the customer can’t answer with confidence, or a jarring design transition.

Platform data shows the 9-12 question sweet spot achieves 11.0% conversion on average; quizzes with only 1-5 questions actually underperform at 9.8% because the recommendation feels superficial and the customer doesn’t trust it.

Drop-off Points / Drop-off Rate
Identifying where users drop off in your quiz can provide insights into potential friction points. Are users abandoning the quiz at a specific question or step? Use this data to adjust those points, making them more intuitive or engaging.

📝 Note: The drop-off rate is measured based on events, which are triggered whenever someone starts a quiz. Some of these start events can be blocked by adblocking plugins in browsers. If you see a difference between the number of quiz starts and the dropoff rate, that is because the number of quiz starts in the analytics tab is automatically adjusted by the algorithm to filter in also the starts blocked by the ad blocker plugin.

Issue	Solution
Low Quiz Starts	Make the quiz more visible. Publish the quiz in multiple ways on your website, send a link to the quiz in your newsletter, or post a link on social media. Promote the quiz. Check this article for great advice on how to increase your quiz starts. Run ads to increase quiz popularity. If using GA4, check the best traffic sources for your quiz and invest more in them.
Low Completion Rate/High Drop Off	Audit question relevance, not just length. Platform data across 20,000+ stores shows 9-12 questions converts highest at 11.0%; quizzes shorter than 6 questions actually underperform at 9.8%. Drop-off comes from irrelevant or repetitive questions, not too many. Add more visuals to the quiz. Turn multiple-choice questions into picture questions. Add images and videos to your quiz. Visual quizzes have a much higher completion rate than pure text-based ones. Check your mandatory questions. Perhaps customers don't want to leave their phone number, name, or email. You can make any question optional in Question Settings. Add personal data questions towards the end of the quiz. Customers are more likely to stay and leave their data while already invested in the quiz.

2️⃣ Revenue

Carts Count and Total Carts Value
Tracking how many users proceed to cart after completing the quiz can help you gauge the effectiveness of your product recommendations. A high cart count and total cart value indicate that the recommended products resonate well with the users.

Average Cart Value
Understanding the average value of products in the carts can help you assess the monetary impact of your quiz. This metric is crucial for evaluating the overall financial performance and can guide you in setting appropriate pricing strategies.

Number of Orders and Total Orders Value
For a more direct measure of conversion, track the number of orders placed and their total value. This data is especially powerful when integrated with Shopify Revenue Reports, allowing you to directly correlate quiz engagement with sales performance.

Average Order Value
Monitoring the average order value post-quiz completion provides insights into the spending behaviour of your users. Stores running quiz funnels typically see meaningful AOV lift versus collection-page browsing because the quiz can recommend a complete routine (cleanser + serum + moisturiser as three line items) rather than a single SKU. Single-product browsers buy one product; quiz-completers often buy a routine.

Conversion Rate

Conversion rate = (Number of purchases after quiz completion ÷ Total number of quiz completions) × 100

To measure the conversion rate of your quiz, track the number of users who complete the quiz and proceed to make a purchase, then calculate the percentage of these users relative to the total number of quiz completions. This metric is essential because it reveals how effectively the quiz drives sales, aiding in optimising the quiz design and marketing strategies to maximise revenue.

These are the benchmarks to compare your own quiz against. If your conversion rate lands below 5%, the structural fixes in the table below recover most of the gap. If it lands above 8%, the rest of the leverage is in the follow-up email sequence and discount placement.

Issue	Solution
Customers don't add products to cart	Check if the quiz returns recommendations. Check your quiz checkout settings. Limit the number of recommended products. A good product selector should recommend only one product that matches all the customer criteria or one product per routine step (in case of beauty shops). Make the call-to-action buttons more visible by styling them. Offer a discount or a coupon code.
Low Conversion Rate	Limit the number of recommended products. A good product selector should recommend only one product that matches all the customer criteria or one product per routine step (in case of beauty shops). Offer a discount or a coupon code. Set up a post-quiz email flow to recapture the lost revenue.
Customers add to the cart but don't buy	Offer a discount or a coupon code. Set up a post-quiz email flow to recapture the lost revenue.
Customers purchase only once	Customer preferences change. Make new versions of the quiz and improve upon it by, for example, giving personalised advice along the recommendations. Segment your customers and follow up with more targeted marketing campaigns post-quiz.

🤔 Tip: Not sure how to measure quiz revenue? Check our step-by-step guide here.

3️⃣ Recommendations

Recommendation Analysis
Delve into why specific products or routines were recommended most often. This helps in fine-tuning your recommendation algorithms. Analyzing individual responses allows you to understand customer preferences better and adjust your product catalogue accordingly.

Product and Slot Blocks
Evaluate the performance of product and slot blocks within the quiz. Ensure that the products being recommended are aligned with user preferences and catch their attention. This granular analysis can lead to better-targeted recommendations, improving user satisfaction and conversion rates.

Most Clicked Products
Track which products are most frequently clicked on during the quiz. This data reveals which products are generating the most interest and engagement. Use this insight to highlight popular products more prominently in your quiz and marketing efforts.

Most Purchased Products
Identify which recommended products are being purchased most often. This metric helps you understand the effectiveness of your recommendations in driving sales. Emphasize these high-performing products in future quizzes and promotional campaigns.

Least Purchased Products
Determine which products are being recommended but not purchased. Investigate potential reasons for low conversions, such as pricing, product appeal, or placement within the quiz. Adjust your strategy to either improve the attractiveness of these products or replace them with alternatives.

Issue	Solution
Customers buy the same products all the time	Use the responses search bar to check if some products in your quiz get by default more votes than others. If that's the case, you can diversify the product recommendations by adding an extra question to your quiz or by linking other products to more choices.
Customers get empty recommendations	Check if your Results Page has a Product Block or a Slot Block to show the products. Check if you've linked products or collections to choices in the quiz. Check if you're not excluding too many products from choices.

4️⃣ Customer Answers

The quiz should help you answer the questions, such as who is my customer and what kind of products are they interested in, and what products they buy. You can understand these things by diving deeper into customer choices and open answers.

Most Clicked Choices

Measure which quiz choices are most frequently clicked. This data provides insights into customer interests and preferences. By understanding which options are most popular, you can improve quiz engagement by highlighting popular choices more prominently and ensuring that the flow of the quiz guides users towards these choices. To get data about most clicked choices you can use the two method highlighted below:

• Export as CSV
  Regularly export quiz response data for in-depth analysis in Google Sheets or Microsoft Excel. This allows you to spot trends, understand user behaviour over time, and make data-driven decisions to enhance your quiz strategy.
• Use Google Analytics
  With GA4 you can measure extra information about the quiz and the customer. You can track customer behaviour with events like clicking a choice or a questions, answering an open question or clicking on a product or description. This provides more insight into who the customers are and how they interact with the quiz. By gathering information on the most clicked choices or elements, you can refine your quiz to better meet customer preferences, optimise the user experience, and increase engagement. These insights will enable you to continuously improve your marketing strategies, ensuring they remain relevant in attracting and retaining customers.

Customer Tags
Use customer tags collected from quiz responses to segment your audience effectively. Each quiz typically produces 5-10 zero-party data points per completion (skin type, concerns, goals, preferences, budget tier), which sync directly to Klaviyo as profile properties via the native integration. Segmented Klaviyo campaigns earn over 3x the revenue per recipient of generic sends (Klaviyo segmentation benchmark), and across the platform 1 in 5 quiz-attributed orders lands more than 30 days after the quiz (benchmark report) — both signals that the segmentation layer is where most of the durable revenue lives. See how Klaviyo segmentation unlocks once zero-party data lands in profiles for the full activation chain.

Issue	Solution
I don't know who my customers are	Analyze customer quiz responses in the Responses panel. Use Customer Tags to mark certain quiz choices to help you define the customer group. For example, if you ask your customer an age question, you can tag them based on their age group. You can do the same with other quiz questions, and soon you'll be able to identify the most popular tags and buyer personas.
I don't know what my customers buy	Check your Orders and Revenue tracking tools to see the most frequently purchased items. Analyze the product recommendations given in the quiz and cross-reference them with actual purchases to see what recommended products are being bought. Use post-purchase surveys to confirm why customers chose certain products.
I don't know how to make customers purchase regularly	Use Customer Tags to mark certain quiz choices to help you define the customer group. You can then create a marketing campaign in your CRM platform to target that specific group of customers. Highly segmented campaigns tend to have higher returns. Offer loyalty programs or subscription services to encourage repeat purchases. Send personalised follow-up emails with product recommendations based on previous purchases and quiz responses. Introduce time-limited discounts or special offers to incentivize repeat purchases.
I don't know which products are underperforming	Review the quiz responses to see which products are least selected or recommended. Analyze sales data to identify products with low conversion rates. Gather customer feedback to understand why certain products may not be appealing. Consider revising the product descriptions, images, or prices to improve their attractiveness. Test different product placements in your quiz to see if visibility affects their performance.
I don't know how to keep my quiz content fresh	Regularly update your quiz questions and product recommendations based on current trends and customer feedback. Introduce seasonal or thematic quizzes to keep the content relevant and engaging. Monitor industry trends and competitor quizzes to stay ahead in content innovation. Solicit feedback from quiz takers on how to improve the quiz and incorporate their suggestions. Use A/B testing to continuously refine quiz elements and identify what resonates best with your audience.

Frequently asked questions

What completion rate should I aim for on my product recommendation quiz?

Above 70% indicates a well-tuned quiz; below 50% means the quiz is too long, too text-heavy, or asking for personal data too early. Healthy ranges vary by category and quiz length, but 70-90% is the band most well-built quizzes occupy. Anything claiming “90-100% completion” should be treated with scepticism: real quizzes have real drop-off, even excellent ones.

What conversion rate should I aim for after quiz completion?

The platform benchmark is 10.6% for top-converting quizzes with a single results page (which 79% of high performers use). Industry-average browse-and-buy conversion sits at 1.8-3%, so a well-built quiz funnel runs 3-5x the baseline. If your conversion is below 5%, the most common structural fixes are: collapse to a single results page, require email capture, connect to Klaviyo, and add a results-page discount.

How many questions should my quiz have?

6 to 12, with the 9-12 band typically performing best at 11.0% conversion. Shorter quizzes (1-5 questions) actually underperform at 9.8% because the customer doesn’t feel they’ve given enough signal for the recommendation to be personalised. The “keep it short” advice from older quiz guides was wrong; relevance matters more than brevity.

How do I track quiz revenue specifically?

The Analytics tab inside the RevenueHunt app surfaces Quiz Revenue alongside completion rate, start rate and drop-off-by-question. For Shopify stores, the Built for Shopify version reports quiz-attributed orders natively in the Analytics tab without any setup. For deeper analysis, export raw responses as CSV and cross-reference with Shopify order data, or use the Google Analytics integration for event-level tracking.

Which metric should I focus on first?

Conversion rate is the most actionable single number. Completion rate diagnoses friction in the quiz itself; AOV diagnoses recommendation quality; recommendation precision (which products convert from which answers) diagnoses your product-to-choice mapping. Start with conversion rate; if it’s below benchmark, the diagnostic order is: results page count first (collapse to one), then question count (6-12), then email capture (make it required), then Klaviyo integration (connect it).

Takeaways

By focusing on these four metrics, you can transform your product recommendation quiz into a measurable conversion engine. The platform benchmarks (5.5% quiz-taker conversion, 10.6% at single results page, 11.0% at 9-12 questions, +11-15% AOV uplift, 3-5x lift over baseline browsing) give you targets to compare against; the Issue/Solution tables above give you the structural fixes when a number lands below target. To estimate the lift on your own store, use our quiz ROI calculator.

For the foundation, see how to build a successful product recommendation quiz. For the mistakes that erode each metric, see quiz creation mistakes that hurt your ecommerce sales. For the email and discount layers that compound the wins, see the post-quiz follow-up sequence and product quiz discounts.

Install RevenueHunt: Recommender Quiz for Shopify and start tracking your first quiz response today. Free plan available.