1. What types of data should the company collect?

Demographic data (population density, age groups, income levels, students vs workers)

Customer behavior data (coffee consumption habits, preferred brands, peak hours)

Competitor data (Highlands, Starbucks, Phúc Long, local cafés, pricing, locations)

Location & foot traffic data (shopping malls, office areas, universities, tourist zones)

Rental & cost data (rent prices, utilities, operating costs)

Economic data (average income, spending power in each city)

2. Where could the company obtain this data?

Field research (customer surveys, observing foot traffic)

Government datasets (population, income, urban planning)

External market research firms

Online sources (Google Maps, social media reviews, competitor websites)

3. What risks exist if the data is incomplete or inaccurate?

Wrong location selection: low foot traffic, low revenue

Underestimated competition: market saturation

Incorrect pricing strategy: customers switch to competitors

4. Which department should manage this data?

Data / Business Intelligence (BI) Team: collect, clean, and analyze data

Marketing Team: understand customer behavior

Operation Team: Serve Customer and test the data quality, report effectiveness to higher data governors

5. Recommendation to the company

Start with data-driven location selection

Focus on high foot traffic areas (universities, offices, tourist zones)

Conduct pilot testing (1–2 stores per city) before full expansion

Use continuous data monitoring to adjust pricing and strategy

Expand only after validating demand through field research + pilot stores, not assumptions.

• Ethnic & Cultural Insights: Since ~30% of the population is ethnic Khmer, the company must collect data on flavor preferences. Do locals prefer traditional ca phe sua da or are they open to regional specialties?

• Student Micro-Demographics: Tra Vinh University has over 20,000 students. Data on their peak study hours, budget (price sensitivity), and preferred "check-in" spots is vital.

• Religious/Festival Timing: Collect data on the local festival calendar (e.g., Chol Chnam Thmay, Ok Om Bok). These events cause massive surges in local foot traffic.

2. Specific Data Sources

• Tra Vinh University (TVU): Partner with the student union or conduct surveys on the Ward 5 campus to understand Gen Z spending habits.

• Local Government Offices: Consult the Tra Vinh Department of Planning and Investment (located on Nam Ky Khoi Nghia St) for upcoming infrastructure plans that might increase land value.

• Competitor Benchmarking: Observe "Café Amazon" (inside Go! Tra Vinh) and local favorites like "Cà Phê 1985" to see why they succeed in this specific market.

3. Risks of Inaccurate Data in Tra Vinh

• Seasonal Fluctuations: If data is only collected during the dry season, the company might underestimate the impact of heavy Mekong Delta rains on "sit-in" vs. delivery orders.

• Overestimating Price Ceiling: Tra Vinh is an emerging economy. Setting prices at "Saigon levels" (e.g., 65,000 VND+) without data on local purchasing power could lead to a quick exit.

4. Responsible Management

• Regional Manager (Mekong Cluster): A dedicated lead who understands the Delta’s business culture should oversee the data.

• Marketing & Localization Team: They must ensure the brand "speaks" to both the Kinh and Khmer communities without feeling like an "outsider" brand.

5. Group Recommendations for Tra Vinh

• The "University Hub" Strategy: Open the first branch near Tra Vinh University. Focus on high-speed Wi-Fi, air conditioning, and student-friendly pricing (30,000–45,000 VND).

• Cultural "Fusion" Interior: Use architectural elements inspired by local Khmer pagodas to create a space that feels familiar and respectful to locals.

• Eco-Friendly Branding: Since Tra Vinh is known as the "Green City" due to its thousands of ancient trees, the company should emphasize sustainable packaging to align with the city's identity.

• Demographic data

• Consumer behaviour data

• Competitor data

• Location & footfall data

• Real estate data

• Infrastructure data

2. Where could the company obtain this data?

• Primary research: in-store surveys, mystery shopping at competitors, footfall counters

• Government & public datasets: GSO Vietnam (General Statistics Office) for population, income, urban planning reports

• Digital sources: Google Maps reviews, Foursquare foot traffic data, social media sentiment analysis

• Real estate platforms for rental benchmarking

3. What risks exist if the data is incomplete or inaccurate?

• Poor location selection → low footfall, high rent-to-revenue ratio, early closure

• Underestimating competition density → price wars, margin erosion

• Misjudging consumer preferences → wrong product mix (e.g. over-investing in premium drinks in a price-sensitive district)

• Seasonal blind spots → ignoring Tết period shifts in foot traffic patterns

• Regulatory gaps → missing district-specific licensing or zoning restrictions

4. Which department should manage this data?

• Primary owner: Business Intelligence (BI) / Strategy & Expansion team

• Contributors: Marketing (consumer insights), Finance (cost modelling), Operations (site feasibility)

• Governance oversight: Data Steward or CDO office to ensure data quality, access control, and compliance with Vietnam's Personal Data Protection Decree (PDPD 2023)

1. What data is needed?

   Population, age group (important for customer cluster), trend drink

   Location & traffics (As more come even in small alley)

2. How to collects data

   Let them do survey for coffee voucher!!

3. Risk if they just put random text on collecting data process

   No insight, lead to no economy since out-dated data.

4. Which governance policies must exists?

   Should not ask for sensitive data

   Should aim for the needed data (coffee preference, seasonal drinks,...)

   Must have consent from customers, let them acknowledge

5. What should YOU do?

   BUY OUR COFFEE!!