🇷🇺 Русская версия | 🇬🇧 English version
University project: Business Studio (Central University)
Team: Ruslan Huseinov (Lead), Kirill Uvarov (Marketing Researcher), Arseny Sushkov (Market Analyst), Egor Starikov (Designer & Field Researcher)
Timeline: September–December 2024
Develop and validate revenue growth initiatives for Dodo Pizza through market research, customer development, field studies, and financial modeling to increase company profitability.
Our team proposed and validated five data-driven initiatives combining customer insights, competitive analysis, and financial projections.
Problem: Long queues during peak hours reduce customer satisfaction
Solution: Deploy 750 self-service kiosks across network
Impact:
- Payback: 3.5 months per location
- Revenue lift: +4.5% per location
- Investment: ₽858M (₽1.14M per location)
- Opex savings: ₽350K/month (staff reduction)
Problem: Limited seasonal offerings during high-demand periods
Solution: Launch holiday menu (glühwein, gingerbread cookies, mandarin pizza, pastries)
Impact:
- ROI: 48-57%
- Payback: 1 season (53.3%)
- Revenue lift: +1.3% company-wide
Problem: Missing nighttime demand in major cities
Solution: Operate 16 locations (160 units) with night shifts (23:00-03:00)
Impact:
- Monthly revenue: ₽264M
- Net profit: ₽24.5M/month
- Market capture: +2.7% order volume
Problem: Existing locations poorly positioned vs. high-traffic zones
Solution: Open 16 new units near offices, universities, and mall food courts
Impact:
- Revenue per location: ₽8.4M/month
- Company-wide lift: +2.01%
- Investment: ₽25M per location
Problem: Limited options for single diners (only 5% of current traffic)
Solution: Launch discounted combo meals targeting solo customers
Impact:
- Revenue lift: ₽32.8M
- Payback: 2 months
- Order volume: +1%
Combined effect: +10.63% company-wide revenue growth
Investment range: ₽20M-₽858M per initiative
Payback periods: 2-3.5 months
- Field research: Conducted on-site observations and time studies at 5 Dodo Pizza locations, measuring service speed, queue lengths, and average check across self-service kiosks vs. traditional cashiers
- Unit economics modeling: Built financial models in Excel for self-service kiosk initiative, calculating ROI (4.5%), payback period (3.5 months), and opex savings (₽350K/month)
- Data collection: Designed and executed customer surveys (60+ respondents), informal interviews with staff and patrons, and competitive benchmarking (McDonald's, KFC, Burger King)
- Quantitative analysis: Performed time-motion studies showing 34% reduction in order processing time and 9% increase in average check with kiosks
- Surveys: 200+ respondents across multiple waves (weeks 2-3, 13)
- Time studies: Service speed measurements at 5 locations
- Financial modeling: ROI, payback, sensitivity analysis for all initiatives
- Field observations: Customer behavior analysis at Aviamall food court
- Customer development: Informal interviews (60+ conversations)
- Competitive analysis: Benchmarking vs. McDonald's, KFC, Burger King, Rostic's
- RICE prioritization: Reach, Impact, Confidence, Effort scoring for each initiative
- A/B test design: Pilot rollout strategy for kiosks (10-20 locations first)
- Sensitivity analysis: Revenue scenarios at 29%, 30%, 31% adoption rates
- Financial modeling: MS Excel (DCF, unit economics, scenario planning)
- Surveys: Google Forms, manual data collection
- Presentation: MS PowerPoint
- Research: Field studies, CustDev interviews, competitive benchmarking
Final Presentation — complete strategy with 5 initiatives, financial projections, and recommendations
📁 View project evolution (weekly deliverables)
- Week 1: Project kickoff & hypothesis formation
- Week 2: Market research & initial surveys — Survey data
- Week 3: Customer segmentation — Survey data
Business Studio is a semester-long project-based course at Central University where teams analyze real companies and propose data-driven growth strategies. Teams conduct iterative research, validate hypotheses through field work, and present actionable recommendations backed by financial models.