You’re lucky. Seriously. Ten or fifteen years ago, to get the kind of clarity you can now pull from a couple of CSVs and a smart prompt, companies needed a small army and big budgets. Today, tools like ChatGPT let a solo founder or a lean Shopify team dig into data, spot patterns, and turn them into action, without writing code or learning six different analytics platforms.
Back then, you’d assemble a cast like this: data scientists, BI analysts, web analysts, CRO specialists, UX researchers, product managers, marketing ops, SEO and PPC leads, email automation specialists, front-end developers, back-end engineers, QA analysts, plus a project manager to herd everyone. Now you can get 70–80% of that strategic lift by pairing clean exports with the right prompts, then validating ideas with quick tests inside your store.
This guide is your starting point. Follow it step-by-step to run a monthly analysis and turn insights into a simple, hour-bounded gameplan. We intentionally kept the language plain and the prompts copy-paste ready, no jargon, no fluff, so you can move fast, learn faster, and keep optimizing without getting overwhelmed.
Disclaimer (privacy & compliance): When sharing or processing data with any third-party tool (including ChatGPT), ensure it’s anonymized and stripped of personal data/PII. Maintain compliance with relevant regulations (e.g., GDPR, CCPA) and your contractual obligations. Use secure transfer methods, limit access on a need-to-know basis, respect data-retention policies, and consult your legal/compliance team before exporting or uploading any customer information.
Now, let’s get started. Each month, export a few Shopify reports (30/90/120 days), upload them to ChatGPT with clear prompts, and turn the insights into a prioritized, hour-bounded task list. Then, validate the plan (again with ChatGPT) before implementation.
Why do this monthly
- Make decisions on data, not gut feel.
- Quickly see what’s improving, what’s slipping, and which metrics matter now.
- Turn ideas into concrete tasks that fit your time/budget.
Step 1: Pull the data and ask for an objective read
1.1. What to export from Shopify
From Shopify Admin → Analytics → Reports, export CSVs for:
- Sessions (traffic)
- Online store conversion rate
- Average order value (AOV)
- Orders
- Total sales
Create 3 exports:
- Last 30 days
- Last 90 days
- Last 120 days
Optional (for month-over-month clarity): export 30-day slices per month (e.g., July, August, September as separate files).
1.2. Initial analysis prompt (copy/paste into ChatGPT)
Upload your CSVs, then:
- We run the Shopify store [NAME] selling [WHAT YOU SELL].
- The attached CSVs include metrics for the last 30/90/120 days.
- Please analyze and answer:
1) What’s improving recently and why?
2) What’s underperforming and the likely causes?
3) Identify the 3 most critical KPIs to focus on next month. Explain the “why” and expected impact.
4) Propose 5–7 optimization hypotheses to test in the next 4 weeks.
Tip: For strict month-over-month, upload one CSV per month and ask: “Compare months and explain the differences.”
Step 2: Translate insights into actions: UX, funnel, apps, competition
Use ChatGPT’s conclusions to guide a critical store walkthrough on desktop and mobile (prioritize your top device from analytics).
2.1. Fast UX/Conversion audit checklist
- Can shoppers find products quickly? (search, filters, sorting)
- Does the PDP cover essentials? (benefits, trust, shipping/returns, reviews)
- Any cart/checkout friction? (surprise fees, unnecessary fields)
- Errors/speed issues/poor imagery?
- Anything obviously missing? (cross-sell, upsell, bundles, sticky ATC, clear size guides, trust badges, benefits bar)
2.2. Ask for targeted ideas (example for low AOV)
AOV is below the historical average by [X]%.
Based on the CSVs and context above, propose 5 specific tactics to lift AOV in 30 days.
Give Shopify-applicable steps, KPIs to measure, effort (low/medium/high), and risks.
2.3. Apps: what to keep, add, or remove
- Do you need a new capability? (cross-sell/upsell, bundles, reviews, search/merch)
- Are current apps actually delivering? (attributed revenue, engagement, errors)
- Do monthly app hygiene: remove redundant/conflicting/low-ROI apps.
Helpful prompt:
Given these weaknesses (low AOV, high mobile bounce, weak filtering), recommend 3–5 types of Shopify capabilities/apps.
For each: why it matters, how to measure impact in 30 days, risks, and a no-app alternative (theme/light code).
2.4. Competitors: monitor monthly
Keep a tight list (3–5 competitors). Review:
- UX (filters, search, PDP structure, trust, merchandising)
- Offers, bundles, gifts
- New collections, landing pages, seasonal pushes
Prompt:
Competitors: [URL1], [URL2], [URL3].
Compare homepage, PLP, PDP, and (as feasible) checkout.
Extract 10 tactics I could test. Mark effort (low/med/high) and expected impact (low/med/high).
Step 3: Build and validate your gameplan (within real hours)
Decide how many hours you have this month (you + team/agency). Create a monthly gameplan with 6–12 prioritized tasks.
3.1. What a good task looks like (DO vs Don’t)
Don’t
“Install a cross-sell app on PDP.”
DO
“AOV decreased X% over the last 90 days. Implement PDP cross-sell (relevant recs near ATC).
Goal: +8–12% AOV in 30–45 days.
KPIs: AOV uplift, attach rate on targeted products.
Effort: medium (card design, rec logic, mobile QA).
Risk: page speed → mitigate with lazy-load and limit recommendation count.”
3.2. Final validation prompt (objective)
Store context: [niche, price point, seasonality].
Top weaknesses: [1], [2], [3].
Here’s my 4-week gameplan for [HOURS AVAILABLE]:
[list tasks with goal + KPI + effort]
Evaluate objectively:
1) Do these tasks directly address the weaknesses? What’s missing?
2) How would you redesign to increase impact within the same hours?
3) Propose execution order and a simple A/B plan.
If some tasks get a “no,” adjust and re-validate. The goal is quality, not speed.
Monthly Gameplan Template (ready to copy)
Context & 30-Day Objectives
- Niche: […]
- AOV current vs target: […]
- CR current vs target: […]
- Primary test hypotheses: […]
Prioritized Tasks (fit within X hours)
- [Task] , goal, KPIs, effort, risks, measurement
- [Task] , goal, KPIs, effort, risks, measurement
… (6–12 tasks)
Weekly Metrics to Track
- Sessions, Conversion Rate, AOV, Orders, Revenue
- Add to cart rate, Reached checkout, Checkout completion
- Bounce rate (by device), Mobile page speed
Testing Plan
- What you A/B, variables, winner threshold, when to cut losses
Tech Debt / App Hygiene
- [Remove/replace app X], [Speed optimization Y], [Mobile QA on high-traffic pages]
Competitor Notes
- 3–5 actionable observations for this month
Prompt Pack (plug and play)
P1 , Analyze CSVs (30/90/120 days)
Analyze the attached CSVs (30/90/120 days).
Tell me what’s improving/declining and the 3 critical KPIs for next month, with rationale.
P2 , AOV/CR tactics
Based on the findings, give me 7 Shopify-applicable tactics to increase [AOV/CR] in 30 days.
For each: steps, KPIs, effort (L/M/H), risks.
P3 , Mobile UX audit
Simulate a mobile user journey (home → PLP → PDP → cart → checkout) and list 10 friction points
that can hurt conversion, with quick fixes.
P4 , Competitor sweep
Compare these 3 sites [URL1–3] on home/PLP/PDP and list 10 replicable tactics
with estimated impact and difficulty.
P5 , Gameplan validation for X hours
Here’s my 4-week gameplan (X hours available):
[tasks]. Evaluate objectively, re-order, remove noise, and maximize ROI within the hours.
Monthly Checklist (print & go)
- Export CSVs: 30/90/120 days (Sessions, CR, AOV, Orders, Sales)
- Initial ChatGPT analysis (P1)
- UX walkthrough (desktop + mobile) and friction list
- Targeted tactics (P2) and app selection (if needed)
- Competitor sweep (P4)
- Gameplan within hours: tasks with goal, KPIs, effort, risks
- Final validation in ChatGPT (P5)
- Implementation + weekly measurement
- App hygiene + backlog for next month
FAQ
1) Why use ChatGPT for a Shopify analysis?
Speed and structure. It digests CSVs and returns organized insights, test hypotheses, and a plan skeleton, which you then validate and adapt to your store’s reality.
2) Which monthly “core” metrics matter?
Sessions, Conversion Rate, AOV, Orders, Revenue. Add: Add to cart rate, Reached checkout, Checkout completion, Bounce (by device), Mobile speed.
3) 30/90/120 vs month-over-month , which is better?
Both. 30/90/120 surfaces trends; month-over-month highlights seasonality/anomalies. Use both when possible.
4) Can I let ChatGPT decide what to do?
No. It’s a co-pilot, not the pilot. Check feasibility, theme/app compatibility, and time budget.
5) Too many recommendations, too few hours, now what?
Prioritize by impact/effort and maintain a backlog for next month. A small executed plan beats a big postponed one.
6) How do I measure if the plan worked?
Define KPI targets and an evaluation window (e.g., 30–45 days). Compare to your baseline and similar cohorts. Don’t judge after 48 hours.
7) Apps slow my store. Should I add another?
Only if the hypothesis has clear ROI and success criteria. Consider “no-app” options (theme/light code) and run monthly app hygiene.
8) What belongs in competitor monitoring?
UX (filters, search, PDP structure, trust), offers, bundles, seasonal landers, messaging, and mobile execution. Pull 3–5 replicable tactics monthly.
9) Mobile vs desktop, where to focus?
Mobile first. It’s often the majority of sessions and conversions. Fix mobile speed and UX before desktop polish.
10) ChatGPT rejected parts of my plan. Is that bad?
It’s great. Integrate the feedback, adjust tasks, and re-validate. The goal is a better plan, not a rubber-stamped “yes.”
11) Can I automate the data export?
Yes, with BI/reporting tools. But a manual monthly loop (CSV + prompt) works excellently for small and mid-size teams.
12) What defines a “good” task in the gameplan?
Clear goal, KPI, effort, risks, and measurement. The AOV example above is a solid template.