Market Entry

Thailand Market Feasibility Study: What It Covers and How to Read the Result

By ZhongTai Bridge Editorial  ·  Last reviewed October 2026  ·  5 min read

Photo: Unsplash

What is a feasibility study, and how does it differ from market research?

Market research describes the market. A feasibility study tests whether your product, price and channels can win in it, and it ends in a decision. The study treats your plan as a hypothesis, identifies the assumptions that matter most and shows what must be true for the plan to work.

What does a Thailand feasibility study cover?

Anatomy of a feasibility study

Process diagram. 1. Define success: Thresholds that decide go, adjust or stop. 2. Validate demand: Search, marketplace, competitor and price evidence. 3. Model economics: Landed cost, margin, scenarios and break-even ROAS. 4. Assess operations and risk: Fulfillment, service, licensing overview, concentration and policy risk. 5. Recommend: Go, adjust or stop, with conditions and a test plan.

A licensing and regulatory overview belongs in the study, but legal structure, product registration and tax treatment should be confirmed with qualified advisers.

How do you build a landed-cost model?

Start with ex-factory cost, convert to baht at your current exchange rate and add freight and handling, import duty, platform commission, payment fees, fulfillment, advertising and a returns allowance. Thailand's VAT is a reduced 7% until 30 September 2027, against a statutory 10%, and import duty now applies to online imports from the first baht, collected through platforms. Whether import VAT is a cost depends on your VAT status, so model both.

Where a THB 899 shelf price goes (illustrative example)

Horizontal bar chart. Product cost (converted): 270.0 THB; Freight and handling: 35.0 THB; Import duty: 30.5 THB; VAT (included in price): 58.8 THB; Platform commission + payment fee: 89.9 THB; Fulfillment: 30.0 THB; Advertising: 100.8 THB; Returns and damage: 33.6 THB; Net profit per unit: 250.4 THB

In the example, a 29.8% net margin looks healthy, and break-even return on ad spend is about 2.4x. Raise the advertising share or the return rate and the picture changes quickly, which is why the study tests scenarios instead of trusting a single case.

How should scenarios be designed?

• Conservative: lower conversion, higher ad cost per order, higher returns, slower review accumulation.
• Base: the most likely case, based on comparable listings and your own tests.
• Ambitious: stronger conversion and repeat purchase, used to understand upside, not to justify spending.

Identify which assumptions drive the result. Usually they are conversion rate, advertising cost per order, return rate and shelf price. Test those first with a small pilot. Add the household context to the downside case: debt-to-GDP fell to 85.9% in early 2026 because credit tightened, not because households grew stronger, so assume value-seeking behavior.

How do you read the result?

Reading feasibility results

Two-by-two matrix. Horizontal axis: Margin after advertising. Vertical axis: Conversion vs threshold. Adjust: Conversion is strong but margin is thin: fix price, cost or pack design. Go: Both clear thresholds: proceed to pilot and scale in steps. Stop: Both fail: do not commit more budget. Fix the listing: Margin is fine but conversion is weak: improve pages, price test or channel.

Write thresholds into the report. For example: proceed if contribution margin after ads is at least your target at the base case and the pilot reaches the agreed conversion rate by day 60. A study that says no is still valuable, because it saves the cost of a failed launch.

What are the warning signs?

• Margin works only at China-level prices.
• Demand depends entirely on paid advertising.
• No credible plan for after-sales chat and returns.
• Competitors already own the main search terms and have hundreds of reviews.
• Cross-border parcel shipping is the only route and duty erases the margin.
• The plan relies on a single large customer or channel.

What inputs do you need to provide?

• Product costs, minimum order quantities and lead times.
• Packaging dimensions and weight, so freight can be calculated.
• Existing certifications and documentation.
• Production capacity and quality control process.
• Budget range, decision timeline and internal approval steps.

The faster you provide accurate inputs, the sooner the study can reach a useful answer.

A worked example: a feasibility readout for a kitchen accessory brand

The example is illustrative. The study tests a silicone food-storage set. Demand evidence shows steady search interest and many competing listings, with crowded low price tiers and fewer options around THB 600 to 900 with design and material claims. The landed-cost model finds that at THB 899 and a 12% advertising share the unit earns a healthy margin, but at THB 599 margin disappears after fees, ads and returns. Scenarios show that margin is most sensitive to conversion and return rate. The study recommends a go for the THB 899 core model with a pilot on one marketplace, a stop on the lowest tier and a condition: proceed to scale only if conversion reaches the threshold and returns stay below the ceiling by day 60.

A good readout is short, explicit and conditional. It tells the board what to do, what to avoid and what would change the recommendation.

Feasibility deliverables checklist

• Executive summary and recommendation on one page.
• Demand evidence with sources and dates.
• Landed-cost and scenario model in a spreadsheet you keep.
• Risk register with owners and mitigations.
• Pilot design with thresholds and a decision date.
• Appendix with methods and assumptions.

Executive questions on feasibility

Who should commission the study? The executive responsible for the decision, with finance and the commercial lead involved so that assumptions are shared.

How do we avoid a study that confirms what we hoped? Set thresholds first, include a downside scenario and ask the author to list what would change the recommendation.

Can we run the study ourselves? You can build the model, but independent input on demand evidence and local costs reduces bias.

What happens after a go decision? A pilot with the same thresholds, then staged scaling.

Frequently asked questions