Setting Up Your Product Research Stack

A product-research stack is not a list of subscriptions. It is a reproducible evidence trail from a customer problem to a small test. Start with platform search, customer language, competitor offers, unit economics and supplier evidence; add paid tools only when a named limitation justifies them.

You will build a research workbook that another person can audit without seeing your browser history.

Design the Workbook Before Collecting Data

Create six tabs:

1. Questions — sanitized customer needs and where they came from.
2. Listings — channel, URL, capture date, title, price, reviews/count if visible, promise, variants and notes.
3. Economics — selling price, product cost, packaging, shipping contribution, platform/payment fees, ads, expected return allowance and margin.
4. Suppliers — identity evidence, sample status, lead time, MOQ, quote validity and defects.
5. Tests — hypothesis, smallest test, budget, deadline, success evidence and stop rule.
6. Sources — URL, publisher, publication/update date if shown, accessed date and claim supported.

Use dropdowns for channel and test status. Protect formula columns. Give every potential SKU a stable research ID such as RS-2026-001; a supplier’s changing product name should not break your evidence trail.

Let AI Structure, Not Manufacture, Evidence

Paste a bounded set of sanitized listing notes and ask AI to group patterns. Require citations back to your research IDs.

You are organizing observed ecommerce research, not estimating market size.
Using only the rows below, group repeated customer promises, price bands, variant patterns,
and visible evidence gaps. Cite the row IDs for every finding. Do not infer sales from
review counts, declare a product "winning," or invent customer demographics.
Return: observation | supporting IDs | uncertainty | smallest next test.

Review every citation. If the model says five listings offer warranties and cites two, reject the finding. Keep screenshots only where platform terms and your data policy allow; URLs plus dated notes are often sufficient.

Worked Example

A hypothetical Faisalabad seller investigates cable organizers. Fifteen dated listings show three recurring promises: desk appearance, easy cable access and adhesive strength. Prices vary, but bundles and materials also vary, so a simple average is misleading. Review text is not treated as verified demand or as reusable marketing copy.

The economics tab shows that a single low-priced unit has poor margin after packaging and fulfillment assumptions, while a three-pack may support a test. The team orders two supplier samples, photographs the adhesive on three surfaces, and defines success as a minimum number of paid orders from a capped listing/ad test—not likes or AI enthusiasm.

Failure Cases to Diagnose

• Saving screenshots with no URL or capture date.
• Treating review count as units sold.
• Comparing prices without normalizing size, quantity, warranty and shipping.
• Asking AI for “trending products in Pakistan” with no supplied evidence.
• Buying inventory before a sample, landed-cost sheet and stop rule exist.
• Collecting customer phone numbers or private messages in the research workbook.

🇵🇰 Pakistan Angle

Record PKR quote dates because currency movement and imported-input costs can invalidate margin quickly. Model city-to-city shipping, remote-area surcharges if applicable, packaging availability, failed delivery and return handling. Validate seasonal explanations—Ramzan, Eid, wedding season, school opening, heat or monsoon—with your own dated evidence rather than folklore.

Hands-On Exercise

1. Create the six-tab workbook.
2. Capture ten comparable listings with URLs and dates.
3. Add three sanitized customer questions from legitimate sources.
4. Calculate one conservative unit-economics scenario.
5. Use the bounded prompt, then verify every cited row.
6. Write a test with a budget ceiling and stop rule.

Completion Rubric

• Every observation has a source URL or internal evidence ID and date.
• Comparisons normalize bundle, variant and fulfillment differences.
• AI findings cite supplied rows and expose uncertainty.
• Unit economics include more than product cost.
• The next step is a capped test, not an inventory commitment.

Sources

• Google Sheets Help — Create and use pivot tables
• Shopify Help Center — Products
• Daraz Pakistan — Start Selling on Daraz

Key takeaway: A research stack is valuable when every product idea can be traced to dated evidence, conservative economics and a cheap test.