Website Launch Optimization
A phased survey framework for capturing baseline metrics, catching post-launch friction, and building a continuous optimization loop
The Problem
You launch a redesigned site for a client and immediately face the same question: what should we optimize next?
Analytics tells you what happened — bounce rates, conversion rates, session duration — but not why. Heatmaps show where people clicked, but not what confused them. You're left guessing which changes actually moved the needle and which ones introduced new friction.
The result: agencies fly blind after launch, clients get impatient waiting for results, and the first round of "optimizations" is based on hunches instead of data.
The solution: deploy surveys at every phase of the launch lifecycle — before, during, and after — to build a feedback loop that turns launch day into the start of a continuous optimization process, not the end of a project.
Who This Is For
CRO agencies launching site redesigns for clients
Shopify dev shops handing off new builds and wanting to prove the site works
In-house teams evaluating a platform migration (e.g., Magento → Shopify, custom → headless)
Freelance developers who want to offer post-launch optimization as an upsell
If you deliver websites to clients, this playbook gives you a structured way to measure what's working, catch what isn't, and prove the value of your work with real customer feedback.
Phase 1: Pre-Launch Baseline (Before Go-Live)
Before you touch anything on the new site, capture how the current site is performing from the customer's perspective. This gives you a baseline to compare against after launch.
What to Measure
Checkout friction: Are customers hitting obstacles during purchase?
Navigation clarity: Can people find what they're looking for?
Product page completeness: Do product pages answer all the questions customers have?
Overall satisfaction: How do customers feel about the current experience?
Surveys to Deploy
1. Exit-Intent Survey (All Pages)
Trigger when visitors move to leave the site. This catches friction you'd never see in analytics.
"Was there anything confusing about this site?" (multiple choice)
Navigation was difficult to use
Couldn't find what I was looking for
Product descriptions weren't clear
Prices or shipping info was unclear
Other (freeform)
See the Exit Intent Survey tutorial for setup instructions.
2. Post-Purchase Survey
Capture feedback from people who did convert — they'll tell you what almost stopped them.
"Was there anything that almost prevented you from completing your purchase?" (open-ended)
"How would you rate your overall shopping experience?" (star rating)
Layer in attribution questions to establish a baseline of where customers are coming from. See Attribution Surveys and Layered Post Purchase Surveys for guidance on structuring multi-question flows.
3. Product Page Survey (PDP, after 120 seconds)
Target product detail pages to gauge content completeness.
"Does this page contain everything you need to know about this product?" (yes/no)
If no: "What's missing?" (open-ended)
How Long to Run
Run pre-launch surveys for 2–4 weeks to collect enough responses for a meaningful baseline. Aim for at least 50–100 responses per survey type. See Top 4 Surveys to Start With for more on getting started quickly.
Phase 2: Launch Day Deployment (Day 1)
The moment the new site goes live, swap your surveys over. The goal is continuity — same questions, new site — so you can make direct before/after comparisons.
What to Do
Redeploy the same surveys from Phase 1 on the new site with identical questions
Add one new question to each survey: "Is this your first time visiting our site?" (yes/no) — this lets you separate feedback from returning visitors (who are comparing old vs. new) from new visitors (who are experiencing the site fresh)
Set up real-time monitoring so you catch critical issues immediately
Real-Time Monitoring
Connect Zigpoll to Slack so your team gets notified of every response as it comes in. On launch day, you want to see feedback in real time — not in a report next week.
What to watch for on Day 1:
Sudden spikes in negative exit-intent responses (something is broken or confusing)
Post-purchase complaints about checkout flow changes
"Couldn't find what I was looking for" responses (navigation regression)
Custom Triggers
If the new site has specific flows you want to test (e.g., a new mega menu, a redesigned cart drawer, a new product configurator), use custom triggers to fire targeted surveys after users interact with those elements.
See Display Rules for configuring exactly when and where each survey appears.
Phase 3: First 30 Days — Catch and Fix
The first month after launch is where you earn your keep. This is the window where small issues become big problems if they go unnoticed — and where quick fixes have the highest ROI.
What to Monitor
Exit-Intent Responses: Are new friction points appearing that didn't exist on the old site? Compare the distribution of responses to your Phase 1 baseline. If "Navigation was difficult to use" jumped from 12% to 35%, that's a clear signal the new nav needs work.
Checkout Friction (Quick Pulse): Add a short binary question to your post-purchase survey:
"Did you run into any issues during checkout?" (yes/no)
If yes: "What happened?" (open-ended)
These simple yes/no questions give you a fast quantitative signal. The open-ended follow-up gives you the qualitative depth to understand what's actually going wrong.
Open-Ended Themes: This is where the real insights hide. Use Z-GPT to summarize open-ended responses weekly. Instead of reading through hundreds of individual responses, Z-GPT surfaces the top 3–5 themes so you can spot patterns fast.
Weekly Review Cadence
Set a recurring weekly review:
Pull response summaries from Zigpoll's analytics dashboard
Run Z-GPT on open-ended responses to surface new themes
Compare current response distributions to Phase 1 baseline
Flag any new friction points introduced by the redesign
Create a prioritized list of fixes based on response volume and severity
Reporting to Clients
Share a weekly summary with your client during this phase. Include:
Total responses collected
Top friction points identified (with supporting quotes)
Before/after comparison on key metrics
Recommended fixes, ranked by impact
This cadence shows clients you're actively monitoring the site — not just launching and walking away.
Phase 4: 60–90 Days — Optimize and Measure
By now you have a meaningful dataset: pre-launch baseline, launch-day reactions, and 30+ days of post-launch feedback. It's time to step back and look at the bigger picture.
Compare Before and After
Pull your pre-launch and post-launch data side by side:
Did checkout friction go up or down?
Did navigation complaints decrease?
Are product page completeness ratings improving?
How does overall satisfaction compare?
Use Google Sheets to export response data and build comparison visualizations. See the Building Automated Reporting playbook for setting up ongoing dashboards.
Identify Patterns Across Survey Types
Look for signals that appear across multiple surveys:
If exit-intent and post-purchase surveys both mention confusing product options, that's a high-confidence issue
If new visitors love the navigation but returning visitors are frustrated, the redesign may have moved things people relied on
If satisfaction is up but checkout friction is also up, there may be a specific step in the new checkout flow that needs attention
Prioritize the Next Round
Use survey data to build a prioritized optimization roadmap:
High volume + high severity — Issues mentioned by many respondents that directly impact conversion (fix immediately)
High volume + low severity — Common complaints that affect experience but not revenue (schedule for next sprint)
Low volume + high severity — Critical issues affecting a small segment (investigate and monitor)
Low volume + low severity — Minor annoyances (backlog)
This roadmap becomes the foundation for your ongoing work — and the justification for a continuing engagement.
The Feedback Loop
The real value of this framework isn't any single survey or phase — it's the loop:
Capture → Interpret → Decide → Execute → Measure
Capture: Deploy surveys to collect customer feedback
Interpret: Use Z-GPT and analytics to identify themes and patterns
Decide: Prioritize changes based on impact and effort
Execute: Implement the changes
Measure: Use the same surveys to verify the changes worked
Then repeat. Every cycle through the loop makes the site better and generates new data that informs the next round.
This is the framework that turns a one-time launch into an ongoing optimization program — and it's the service agencies can sell as a recurring engagement rather than a one-time project deliverable.
Surveys to Deploy: Quick Reference
Here's a summary of every survey across all four phases, with example questions and setup guidance.
Pre-Launch (Phase 1)
Exit-Intent
Mouse leave / back button
"Was there anything confusing about this site?" (MC)
Post-Purchase
Order confirmation
"Was there anything that almost prevented your purchase?" (OE), Attribution questions
Product Page
After 120s on PDP
"Does this page have everything you need?" (Y/N), "What's missing?" (OE)
Launch Day (Phase 2)
Deploy the same surveys as Phase 1, plus:
New vs. Returning
All surveys
"Is this your first time visiting our site?" (Y/N)
Feature-Specific
Custom trigger on new elements
"How easy was it to use [feature]?" (rating)
Set up Slack notifications for all surveys.
First 30 Days (Phase 3)
Exit-Intent
Mouse leave / back button
Same as Phase 1 (for comparison)
Checkout Pulse
Post-purchase
"Did you run into any issues during checkout?" (Y/N), "What happened?" (OE)
Post-Purchase
Order confirmation
Same as Phase 1 + checkout friction questions
Use Z-GPT for weekly open-ended response summaries.
60–90 Days (Phase 4)
Continue Phase 3 surveys. Add:
Satisfaction Benchmark
Post-purchase
"How would you rate your overall experience?" (1–5 star)
Optimization-Specific
Targeted to changed pages
"Did this page answer your questions?" (Y/N), "What would you improve?" (OE)
Export data to Google Sheets for before/after comparison dashboards.
Turning This Into a Recurring Service
The biggest mistake agencies make after a launch is treating it as the finish line. The playbook above gives you a framework to sell ongoing optimization as a service — here's how to package it.
The Pitch
"We don't just launch sites. We launch, measure, and optimize — using real customer feedback, not guesses. The site you get on Day 1 is the starting point. What we build together over the next 90 days is what actually drives results."
What to Include in a Retainer
Monthly survey management: Deploying, adjusting, and monitoring all surveys
Weekly insight reports: Z-GPT summaries + key themes + recommended actions
Monthly optimization roadmap: Prioritized list of changes based on survey data
Quarterly before/after analysis: Showing measurable improvement over time
How to Price It
Survey-driven optimization is a high-margin service because the data collection is automated. Your time goes into analysis and recommendations, not manual data gathering. Position it as a strategic service, not a technical one.
Related Resources
See the Win More Clients playbook for using surveys in your sales process
See the Building Automated Reporting playbook for setting up dashboards that run themselves
See the CRO Audit playbook for a lighter-weight version focused on quick wins
Last updated