AI prompts for WooCommerce analytics & CRO

For each prompt: export a CSV from the named Statnive report, paste it in, and copy the full prompt into your AI assistant. The prompts assume you are running Statnive on a solo WooCommerce store ($5K–$50K/mo) and that you want decision-ready output, not analysis paralysis.

1. #01

   Weekly review

   Use it when

   Every Monday morning. Paste your last-7-days vs. previous-7-days numbers and get a 3-item action list.

   Pairs with

   Overview + Revenue Report

   Caveat

   Pair Overview traffic deltas with Revenue Report channel deltas. v1.0.0 lets the AI tie source-of-traffic shifts to revenue shifts directly.

   Show prompt

   You are a CRO analyst for a solo WooCommerce store. Here is week-over-week data on Sessions, Bounces, and Channels:
   
   [PASTE CSV: 7-day current vs 7-day previous Overview export]
   
   Identify the 3 most important changes. Label each as 'investigate / act / ignore'. Suggest one experiment for the 'act' items. Keep it under 200 words.

2. #02

   Landing page CRO

   Use it when

   After a paid campaign launches, or whenever your top entry pages shift.

   Pairs with

   Pages → Entry pages

   Caveat

   AI suggests hypotheses, not verdicts. Always validate against Baymard checklists before shipping a change.

   Show prompt

   Here is Entry Count, Bounces, and Total Duration for my top 10 entry pages:
   
   [PASTE CSV from Statnive Pages report, sorted by Entry Count]
   
   Rank them by CRO opportunity. For the top 3, list 3 hypotheses each and one concrete experiment per hypothesis. Output as a table.

3. #03

   Product page optimization

   Use it when

   Quarterly PDP audit, or when a specific product underperforms.

   Pairs with

   Pages (filter /product/)

   Caveat

   High duration on a PDP can mean genuine interest OR confusion. Pair with exit count to disambiguate.

   Show prompt

   Here is Views, Total Duration, and Exit Count for my product pages (filtered to URLs containing /product/):
   
   [PASTE CSV]
   
   Identify the 3 PDPs with the strongest signal of friction. For each, hypothesize the 3 most likely causes. Recommend the lowest-effort fix per cause.

4. #04

   Campaign quality audit

   Use it when

   Monthly paid-ads review, or when you suspect a campaign is wasting money.

   Pairs with

   Referrers (UTM filtered) + Revenue by Channel

   Caveat

   As of v1.0.0, the Revenue Report's Channel breakdown gives you revenue per channel directly — pair with Referrers UTM data for source-level ROAS.

   Show prompt

   Here is UTM campaign data: Source, Medium, Campaign, Sessions, Bounces, Total Duration:
   
   [PASTE CSV from Referrers report, UTM dimensions]
   
   Identify campaigns to scale, fix, or pause. For each pause/fix recommendation, give the diagnostic signal and the next step. Output as a table with reasons. Apply the channel-health rule: pass = bounces below site avg AND duration above site avg.

5. #05

   UTM hygiene cleanup

   Use it when

   When your Direct traffic share suspiciously climbs above 25%, or before adding a new ad platform.

   Pairs with

   Referrers (UTM dimensions)

   Caveat

   AI catches naming inconsistencies but cannot fix the underlying campaign-management discipline. Adopt a single naming convention and enforce it at the ad-platform level.

   Show prompt

   Here is my UTM list (Source/Medium/Campaign distinct values for the last 90 days):
   
   [PASTE CSV]
   
   Identify (1) naming inconsistencies (capitalization, duplicates, typos), (2) the most likely consolidations, and (3) propose a standardized lowercase naming scheme with examples. Flag any 'utm_medium' value that does not match Statnive's 8 channel buckets.

6. #06

   Mobile UX gap detection

   Use it when

   Quarterly mobile audit, or whenever you ship a checkout/PDP change.

   Pairs with

   Devices

   Caveat

   Statnive measures bounce/exit by device type but not page-speed. Pair this analysis with a Lighthouse / Web Vitals run for the speed dimension.

   Show prompt

   Here is bounce rate by Device Type for my top 10 pages:
   
   [PASTE CSV: Pages × Device Type cross-tab from Statnive]
   
   For each page where mobile bounce exceeds desktop bounce by 15 percentage points or more, list the page and suggest 3 mobile-specific fixes (one for layout, one for speed, one for input/interaction). Skip pages where the gap is within 15pp.

7. #07

   Localization opportunity scan

   Use it when

   Quarterly international audit; or before considering a translation/currency project.

   Pairs with

   Geography + Languages

   Caveat

   AI ranks opportunities; the actual localization ROI depends on your shipping costs, support capacity, and tax setup per country. Treat the output as a shortlist, not a commit.

   Show prompt

   Here is Geography data: Country, Visitors, Total Duration, Bounces:
   
   [PASTE CSV from Statnive Geography report]
   
   Identify the top 3 countries with: at least 5% share of total visitors AND total duration at least 80% of my domestic visitors' duration. For each, recommend currency-first or language-first as the cheapest first localization test, and explain why.

8. #08

   Content-to-product attribution

   Use it when

   When a blog post is your best-performing entry page but does not seem to drive product sales.

   Pairs with

   Pages (blog + product cross-reference)

   Caveat

   Without next-page events, this is correlation analysis, not attribution. Treat as a content-strategy signal, not a revenue model.

   Show prompt

   Here are my top 20 blog posts by Views and Exit Count:
   
   [PASTE CSV from Statnive Pages report, filtered to blog URLs]
   
   Which posts are 'bleeding' traffic (high views + high exits + low next-page conversion)? For each, suggest 2 contextual internal links to product pages that would naturally fit the post's topic.

9. #09

   Exit page diagnosis

   Use it when

   After identifying a high-exit page via the absolute-loss math, before shipping a fix.

   Pairs with

   Pages → Exit pages

   Caveat

   The AI hypothesizes; you validate. Always cross-check against Baymard checklists for the page type (PDP, cart, checkout).

   Show prompt

   For this exit page: [PASTE: page URL + Exit Count + Views + Total Duration + which page type (PDP/cart/checkout)]
   
   Hypothesize 5 reasons users leave. Rank by likelihood. For the top 2, suggest one diagnostic check and one experimental fix.

10. #10

    Real-time launch monitoring

    Use it when

    During a flash sale, email send, or influencer post — the first hour matters most.

    Pairs with

    Real-time

    Caveat

    Real-time is for monitoring, not deciding. Do not change a campaign based on the first 15 minutes of data.

    Show prompt

    During my flash sale, here is real-time visitor pattern by minute:
    
    [PASTE CSV: Real-time visitor counts in 5-min buckets from launch]
    
    Identify whether the campaign is on track vs. the expected baseline (which I supply). Flag any unusual patterns (sudden spike, abnormal source distribution). Suggest one action only if the deviation is greater than 2x baseline.

11. #11

    Funnel drop-off diagnosis

    Use it when

    After at least 7 days of v1.0.0 funnel data — pick the worst step and fix it.

    Pairs with

    Revenue Report → Funnel

    Caveat

    AI suggests Baymard-aligned fixes. You ship one, measure for two weeks, then move to the next step. Do not change three steps at once.

    Show prompt

    Here is my Cart-to-Purchase Funnel for the last 30 days from Statnive Revenue Report:
    
    [PASTE CSV: Viewed product → Added to cart → Started checkout → Completed purchase counts + per-step conversion rate, ideally with per-channel breakdown]
    
    Identify the biggest funnel drop-off step. Suggest 3 fixes specific to that step (PDP issues for view→cart drop; cart issues for cart→checkout drop; checkout-form issues for checkout→purchase drop). Cite which fix Baymard research supports.

12. #12

    Revenue per channel — budget allocation

    Use it when

    Quarterly budget planning; or before scaling a paid channel.

    Pairs with

    Revenue Report → Channel + Referrers

    Caveat

    As of v1.0.0, Revenue by Channel is a first-class breakdown — paste both the Channel table and matching session counts from Referrers. The AI Assistants channel often surfaces here as a long-tail growth opportunity.

    Show prompt

    Here are Orders, Revenue (net), and AOV per channel from the Statnive Revenue Report, plus session counts from the Referrers report for the same period:
    
    [PASTE CSV]
    
    Calculate revenue per session (RPV) by channel. Rank channels for next-quarter budget. Flag any channel with high session volume but bottom-quartile RPV as a budget-cut candidate. Call out the AI Assistants channel specifically if its RPV beats paid channels — that's a free-acquisition signal worth investing content in.