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Vinkal Chadha
Managing Partner, Global Business Development
Search among the top brands by number of locations
Join us for a demo where we'll discuss how Realytics can support your business growth and adaptability in changing market conditions
Managing Partner, Global Business Development
Search among the top brands by number of locations
Join us for a demo where we'll discuss how Realytics can support your business growth and adaptability in changing market conditions
Managing Partner, Global Business Development
Search among the top brands by number of locations
Join us for a demo where we'll discuss how Realytics can support your business growth and adaptability in changing market conditions
Managing Partner, Global Business Development
Search among the top brands by number of locations
Join us for a demo where we'll discuss how Realytics can support your business growth and adaptability in changing market conditions
Managing Partner, Global Business Development
Available by subscription
Available by subscription
See what factors influence Milano performance in the Russia and how they change over time
See what factors influence Milano performance in the Russia and how they change over time
Join us for a demo where we'll discuss how Realytics can support your business growth and adaptability in changing market conditions
Managing Partner, Global Business Development
Discover your traffic workload during different times of the day
An analysis of Milano' competitors in the Russia
An analysis of Milano' competitors in the Russia
Top-5 brands that brand's customers also visit
Search among the top brands by number of locations
In Cafés & Restaurants
·Jun – Aug 25
Milano is in the lower 30% of brands
Sample of brands in the same percentile
Cafés & Restaurants
Market performance percentile shows brand's share of foot traffic, revealing its competitive strength and customer preference in the industry.
Milano's market performance is in the lower 30%, indicating a below average market standing. This means Milano captures less foot traffic compared to most competitors. Performance peers in the same range include Sushi Ot Zebrushi, Ketch Up, Inyan, LA RUSS, OUGI Sushi, and Soma.
Customer satisfaction (CSAT) reflects customer happiness, impacting loyalty and brand reputation. Higher CSAT often correlates with increased sales and positive word-of-mouth.
Milano's overall CSAT is 35%, a 5.9 percentage point increase year-over-year. In the Volga Federal District, CSAT is also 35%, with an 11.2 percentage point increase. This suggests improving customer sentiment, particularly in the Volga region.
Average check reveals customer spending habits. Monitoring this KPI helps optimize pricing strategies and identify opportunities to increase revenue per transaction.
The average check in the Volga Federal District is 980.8 RUB, with no growth compared to the previous year. Dynamic data shows an average check of 866.67 RUB. Overall average check data is not available.
Outlet count indicates brand reach and market presence. Tracking outlet distribution helps assess expansion effectiveness and identify untapped geographic opportunities.
Milano has 20 outlets in the Volga Federal District, 6 in the Northwestern Federal District, and 3 in the Central Federal District. The Volga region represents the largest concentration of Milano outlets.
Identifying top competitors helps understand the competitive landscape. Analyzing cross-visitation patterns reveals shared customer bases and potential areas for differentiation.
Milano's top competitors, based on customer cross-visitation, include Lampa Grina, Teplaya rechka, Au, Korchma, and Russkiye Bliny, each sharing 20% of Milano's customer base. This indicates these brands attract a similar customer demographic.
Traffic workload analysis by hour helps optimize staffing and resource allocation. Understanding peak hours ensures adequate service levels and maximizes revenue potential.
Milano experiences peak traffic workload between 10:00 and 21:00, with the highest workload at 18:00 (68.74%). Traffic is minimal between 22:00 and 9:00. This data suggests staffing should be prioritized during peak hours.