Case Studies
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Client brief:
Client is the largest public sector bank in India and one of the largest banks in with world.
Problem statement:
Post merger of various affiliated state banks (like State bank of Hyderabad, State bank of Mysore, for example) with itself, the client wanted to identify potential areas for opening new branches and closure of existing branches within the city of Mumbai.
Quantta Solution:
Quantta used it’s proprietary Location Analytics algorithm along with various statistical and spatial techniques to provide ward and 1sqkm grid level assessment to support client's business objective. Quantta used total population, total households, age, credit/deposit, competition, points of interests, businesses, professionals, land use variables, for example, to forecast credit and deposit potential at ward and 1sqkm grid level. Output was shared as an interactive web-based map application displaying various data layers and supporting data for collaborative planning.
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Client brief:
Client is one of the new age private sector bank.
Problem statement:
Prioritize and identify next 200 cities for expanding client’s footprint.
QUANTTA Solution:
Quantta used hierarchical prioritization approach to identify top 200 cities in India. Quantta used credit/deposit ratio, competition, children population, number of businesses, number of banks, employment type, asset ownership, infrastructure status variables, for example, to prioritize and rank states, districts, taluka, and cities in India to support client's bank network expansion objectives. Output was shared as an interactive web-based map application displaying various data layers and supporting data for collaborative planning.
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Client brief:
Client is a leading gold loan company and one of India’s top non-banking financial companies financed by Foreign Institutional Investors (FIIs), Sequoia Capital and Hudson Equity Holdings.
Problem statement:
Identify potential areas to appoint business associate for increasing client’s goal loan market share in and around the talukas of client’s existing branches.
QUANTTA Solution:
Quantta used it’s proprietary Location Analytics algorithm and forecasting models to provide taluka level prioritization. Quantta used income, client’s performance data, population, competition, employment type, agri mandis, major industrial activities, accessibility, household availing banking services. NGOs variables, for example, to identify attractiveness of a taluka with respect business associates appointment. Output was shared in Excel and as an interactive web-based map application displaying various data layers and supporting data for collaborative planning. Client has used output to setup new branches and appoint business associate.
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Client brief:
Client is one of the largest micro finance companies in India, which recently got license to function as small finance bank and is a publicly listed company.
Problem statement:
Identify potential areas for strategic placement of client’s bank branches and expansion of client’s microfinance business across India.
QUANTTA Solution:
Quantta used RBI, client's performance data, client's centre locations, competition, literacy rate, total population, road accessibility, gender ratio, employment type, NGO's variables, for example, to help prioritize potential unbanked locations within the 25km catchment of client's 200 branches spread across India. Output was shared in Excel and as an interactive web-based map application displaying various data layers and supporting data for collaborative planning. Client has used output to setup lot of their branches and plan next phase of expansion.
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Client brief:
Client is one of the largest gold financing company in India in terms of loan portfolio.
Problem statement:
Identify potential areas for gold loan, SME loans, MSME loans and Money transfer services around 10km catchment of client’s existing and proposed branches.
QUANTTA Solution:
Quntta used its proprietary Location Analytics algorithm to provide catchment assessment. Quantta used credit/deposit ratio, competition, BPL households, employment type, NGOs variables, for example, to forecast credit and deposit potential in the catchment. Output was shared as an interactive web-based map application displaying various data layers and supporting data for collaborative planning.
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