THE PROMPT LIBRARY
Forty-four real questions, organized by the decision you’re trying to make. Click copy, paste into Claude, and get an answer from your live survey data.
44 PROMPTS
01 / ATTRIBUTION & HDYHAU
Break down my How Did You Hear About Us responses by channel for the last 30 days and compare against the prior 30. Which channels moved the most?
Which discovery channels have the highest average order value over the last 90 days? Rank them.
Show me this quarter's HDYHAU distribution by total order value, not just response count. Which channel is actually driving the most revenue?
Read the write-in and 'Other' responses on my HDYHAU question from the last 60 days. What channels are customers naming that aren't answer options yet?
How many customers mentioned a specific podcast by name in the last 60 days? Group the mentions by show.
Summarize what customers say when they pick 'Word of mouth.' Who is referring them and in what context?
02 / STATED VS TRACKED: SURVEY X UTM
Cross-tab my How Did You Hear About Us answers against the UTM source on those same orders for the last 30 days. Where do what customers say and what the click data says disagree the most?
Look at my orders from the last 30 days that arrived with no UTM parameters at all, the ones analytics calls direct. What channels do those customers say they actually came from?
Which UTM campaigns drive customers who say they heard about us through word of mouth, a podcast, or a friend? Use the last 30 days. I want to know which paid campaigns get credit they didn't earn.
Find customers from the last 30 days who said they discovered us on TikTok or Instagram but whose order UTM says search or email. How much of my demand is created in one channel and captured in another?
Rank my UTM content values by the average order value of survey respondents over the last 30 days. Which ads bring the best customers, not just the most customers? Flag any sample too small to trust.
03 / PROMO CODES & OFFERS
Which promo codes show up on my surveyed orders in the last 30 days? Rank them by order count and total order value.
What share of each discovery channel's orders used a promo code in the last 30 days? Which channels bring me full-price customers?
Match the promo codes on my orders against the influencers and shows customers name in my follow-up write-ins from the last 60 days. Do creator codes and stated attribution back each other up, and which creators drive orders even when their code goes unused?
Compare average order value with a promo code versus without one over the last 30 days, split by discovery channel. Where are discounts buying revenue we would have gotten anyway?
Take my most-used promo codes from the last 30 days and check what the customers using each one say about how they found us. Is a creator or partner code being used by people who never mention that creator? Tell me which codes have leaked to coupon sites and what the mismatch is costing me.
04 / ACQUISITION VS RETENTION
Compare the discovery channel mix of first-time buyers versus repeat buyers over the last 90 days. Which channels acquire new customers, and which ones do returning customers come back through?
Compare the UTM parameters on first orders versus repeat orders over the last 30 days, then compare each against what those customers say in the survey. Are my paid campaigns actually acquiring new customers, or taking credit for people who were coming back anyway?
For customers on their second or later order, what do they say brought them back? Compare their answers against first-time buyers. If I run a 'How did you find us today?' question alongside my first-touch question, use both: which channels create customers and which ones reactivate them?
05 / HOLIDAY & BFCM
Show me the HDYHAU breakdown for my last sale week versus a normal week. Did the sale pull in different channels, and what should that change about how I plan the next one?
Pull my HDYHAU channel mix for Black Friday through Cyber Monday last year and compare it against a normal October week. Which channels over-delivered during the sale, and where should I concentrate this year?
Find every response this quarter that mentions 'gift' and tell me what share of our order value looks like gift purchases. What channels are they coming from?
Compare my holiday-window buyers against my normal baseline: share of first-time buyers, average order value, and promo code usage. Did the holiday bring me new customers or just discount my regulars?
06 / SURVEY DESIGN & DATA QUALITY
List every question currently running with its response count for the last 30 days.
Review my HDYHAU answer options against the write-in responses from the last 90 days. Which options should I add, and which got under 1% of responses and should be cut or consolidated?
Read the 'Other' write-ins across all my questions this month. What are customers trying to tell me that my answer options don't capture?
Audit my open-text follow-ups from the last 30 days for data quality: what share is blank or junk, and where is the same influencer, show, or store spelled three different ways? Tell me what would be fixable with better answer options.
07 / CUSTOMER & VOICE-OF-CUSTOMER INSIGHT
Summarize the top 5 themes in our 'What almost stopped you from buying?' responses this quarter. For each one, tell me whether the fix is an FAQ answer, a page change, or a CRO experiment, and draft the FAQ answers.
What do customers say motivated their purchase? Break down the distribution and summarize the write-ins.
What language do customers use to describe our product in open-text responses? Give me the exact recurring phrases for our ad copy.
Do customers who discover us through word of mouth spend more per order than customers from paid social? Compare their AOVs over the last 6 months.
Calculate my NPS week by week for the last 8 weeks, then read the follow-up comments behind the scores. What themes separate my detractors from my promoters, and did the themes shift in any week that broke from the trend?
08 / REPORTING & RECURRING
Build me a Monday-morning summary: last week's response volume, top 5 channels, and any channel that moved more than 2 points week over week.
What changed in our survey data this week that I should know about? Flag anything unusual.
Format last quarter's channel breakdown (responses, total order value, AOV) as a table I can drop into a slide.
Pull last week's attribution split with response counts and revenue per channel, formatted as clean rows I can paste into my KPI spreadsheet. Keep the format identical every week so I can track it over time.
09 / RECIPES: MULTI-STEP CHAINS
Multi-step analyses that chain several questions into one working session.
Pull the last 90 days of HDYHAU responses with order value per channel. I'll paste our ad spend by channel; if you use a spend aggregator like Pipeboard, export it from there. Compare each channel's revenue share to its spend share, flag the two biggest mismatches, and recommend a specific reallocation with the sample sizes that support it.
We're considering spending on TV next quarter. Before we do, establish the baseline: what share of customers mention TV today, how much that share bounces week to week over the last 90 days, and how big a lift we'd need to see to be confident the spend is working. Give me the number that would prove it.
We started podcast ads on June 1. Compare podcast responses before and after that date, list the shows customers name in write-ins, compare AOV of podcast-attributed customers to our average, and tell me if the channel is working given what we paid: [amount].
List all my running questions with response counts, find answer options under 1% share, surface write-in themes not covered by existing options, then propose a revised question set I can implement in Fairing.
10 / META
What questions can I ask you about my Fairing data? List what's available to analyze on my store.
Before we analyze anything, check what my survey data actually includes: are UTM parameters, order revenue, and promo codes populated on my responses, and how many responses do I collect in a typical week? Tell me which analyses will be reliable on my store and which won't.
This number looks wrong to me. Send feedback to the Fairing team with the context of what we were analyzing.
I want to analyze something Fairing's MCP can't do yet. Send the Fairing team a feature request describing what I was trying to accomplish.
