Expanding into new markets requires a detailed understanding of various factors that can influence store performance.
A major skincare brand wanted to identify revenue potential for their current operational stores and additional cities where they aim to establish new stores based on their available existing sales data. They also wanted to know the key factors behind the underperforming stores.
The Challenge
– Identifying the location factors responsible for underperforming stores
– Prioritising new cities for expansion where they do not have a retail presence yet
– Get site recommendations with a high revenue potential
– Assess the achievable revenue potential of existing stores
– Minimise payback period at new stores, which is more than one year for existing stores
The Outcome
Utilising GeoIQ’s solution, the brand has identified 41 cities across India and recommended locations within these cities, where they can generate the highest revenues. They have also identified key parameters that are influencing underperforming stores both positively and negatively.
About the Client
Our client is a renowned skincare brand with a strong presence in India and the Middle East. With over two decades of experience, they operate 100+ clinics across metro and Tier-1 cities in India. They offer a wide range of personalized skincare consultations by expert dermatologists and customized solutions.
Methodology
Step 1:
Assess the performance of current existing stores shared by the brand
Step 2:
Identify factors influencing the success or failure of the stores to highlight reasons behind underperforming stores
Step 3:
Develop Revenue Model: To forecast the revenue potential of multiple locations across India.
Step 4:
Prioritize cities and recommend the most promising locations for new stores based on revenue projections.
Insights
The modeling exercise uncovered certain brands’ (previously not on the radar of the client) performance of which positively impacts the performance of stores, these brands attract similar types of audiences
The recommended high-revenue potential locations were characterized by the following:
– Target Audience: Presence of affluent households with working women
– Density of Complimentary: High-end apparel and big box electronics and particular brands identified
– Presence of Derma market: Unbranded, single-operated dermatology clinics
– Whitespace: Low density of organized competitors
Conclusion
By applying our predictive models, the client now has a clear roadmap for expanding into new cities, with the confidence to open stores in high potential locations and avoid seemingly good markets with low density of their target audience.
They’ve also avoided potential losses by not opening stores in 3 cities where the projections did not meet expectations. This approach helped streamline their expansion while ensuring profitability.
