Which city should Capital First open a branch next?

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  • WHICH CITY SHOULD CAPITAL FIRST OPEN A BRANCH NEXT?

    Priyank Aranke (aranke.priyank99@gmail.com)

    An approach using cutting edge computational techniques

    October 2016

    mailto:aranke.priyank99@gmail.com?subject=

  • THE PROBLEM

    V. VAIDYANATHAN IN CAPITAL FIRST ANNUAL REPORT 2015-16 MARCH 31, 2016

    CLEARLY THERE IS LOT OF OPPORTUNITY FOR MSME SECTOR IN TIER 2 CITIES

    the Cabinet approved a proposal for the introduction of the Micro, Small and Medium Enterprises Development (Amendment) Bill, 2015

    Indias 50 million MSMEs and its fast emerging middle class, with a differentiated model, based on new technologies, provides a large and unique opportunity.

  • WHERE SHOULD CAPITAL FIRST OPEN SERVICES NEXT?

    THERE ARE 496 CITIES IN INDIA WITH A POPULATION OF OVER 1 LAKH. CAPITAL FIRST IS PRESENT IN 43. WHICH OF THE REMAINING 453 CITIES SHOULD CAPITAL FIRST OPEN NEXT?

    SOURCE: HTTP://WWW.CAPITALFIRST.COM/BRANCH-LOCATOR

    http://www.capitalfirst.com/branch-locator

  • RECOMMENDER SYSTEMS AROUND US

    WE WILL USE THE SAME TECHNOLOGY THAT AMAZON USES TO RECOMMEND ITEMS

  • RECOMMENDER SYSTEMS AROUND US

    AND NETFLIX USES TO RECOMMEND MOVIES

  • THE HIGH LEVEL APPROACH

    ITS CALLED RECOMMENDER SYSTEMS

    Why Amazon and Netflix recommendations are so good

    They have data on purchase history of millions of people

    So they can figure out people who have tastes similar to you

    Then they recommend to you what people like you have liked

  • THE HIGH LEVEL APPROACH

    WHICH CAN BE ALSO APPLIED TO RECOMMEND NEW CITIES

    Heres how:

    Collect data on thousands of Indian towns and cities

    Find out the businesses who have locations similar to you

    Recommend the locations which businesses like you have discovered

  • FRANCHISE DATA

    PROPRIETARY, HAND-COLLECTED AND CAREFULLY CURATED DATA ON 26 FRANCHISES AND 1496 CITIES IS INPUT TO THE RECOMMENDER ALGORITHM

    Franchise No. of cities

    Axis Bank 462Bajaj Finserv 292Cafe Coffee Day 220Capital First 43Dominos Pizza 245Dunkin Donuts 23Eicher Motors 291Equitas Mf 36Gruh Finance 155Hero MotoCorp 603Hypercity 12Inox Leisure 52Janalakshmi Fin. 166

    Franchise No. of cities

    Kalyan Jewellers 59Kotak Mahindra Bank 537More Store 153Ola Cabs 87PVR Cinema 39Repco Home Finance 102Shoppers Stop 34Sony Electronics 145Sriram Vehicle Finance 794Tanishq Jewellers 108Toyota 220Uber 27V-Mart 104

    DATA AS OF JULOCT 2016

  • THE RECOMMENDER ALGORITHM

    RECOMMENDER ALGORITHM

    THE RECOMMENDER ALGORITHM GENERATES TOP LOCATIONS WHERE CAPITAL FIRST SHOULD OPEN BRANCHES - BASED ON LOCATIONS OF OTHER SIMILAR BUSINESSES

    SEE REFERENCES SLIDE FOR TECHNICAL DETAILS ABOUT THE RECOMMENDER ALGORITHM

  • AND THE OUTPUT OF THE ALGORITHM IS

    DATA AS OF OCT 2016

    RECOMMENDED NEW CITIES F0R CAPITAL FIRST TO OPEN BRANCHES NEXT

  • IN ADDITION TO RECOMMENDING WHERE YOU SHOULD OPEN THE STORES NEXT, THE TECHNIQUE CAN ALSO BE USED TO:

    Find out which of the existing stores are under or over-performing The model outputs a score for each city which indicates the business

    potential of that city. You can compare that score to the actual sales in that city to determine whether the store is under or over-performing.

    Predicting which cities a given competitor would target next Since the recommendation engine works on publicly available data,

    we can use it to predict the locations which a competitor would target next. This will help you plan your response in advance.

    These predictions have worked in the past. See the next slides for my past successful predictions and future predictions on where Bajaj Finance will open its next branches.

    MANY WAYS TO USE THIS TECHNOLOGY

  • IN AUG 2016, USING THIS APPROACH, I PREDICTED THAT BAJAJ FINANCE WOULD OPEN IN 25 NEW CITIES. BY OCT 2016, BAJAJ FINANCE HAD OPENED A BRANCH IN 22 OF THESE 25 CITIES.

    SUCCESSFUL PREDICTIONS ON BAJAJ FINANCE BRANCH LOCATIONS

    Predicted in August 2016

    Added in October 2016

    Agra Ambala Bhopal Dehradun Erode Goa Jabalpur Jalandhar Jamshedpur Jodhpur Kanpur Kolhapur Lucknow

    Predicted in August 2016

    Added in October 2016

    Ludhiana Mangalore Patiala Patna Raipur Rajkot Ranchi Salem Tiruchirappalli Amritsar Guwahati Mohali

  • FUTURE PREDICTIONS ON BAJAJ FINANCE BRANCH LOCATIONS

    PREDICTED NEW CITIES FOR BAJAJ FINANCE LOAN PRODUCTS

    City Doctor Home Property Business Personal

    Amritsar Goa Guwahati Jalandhar Kanpur Lucknow Mangalore Mohali Mysore Patiala Patna Raipur Trivandrum

    DATA AS OF OCT 2016

  • DATA AS OF OCT 2016

    FUTURE PREDICTIONS ON BAJAJ FINANCE BRANCH LOCATIONS ON A MAP

  • TO KNOW FURTHER

    To get real time recommendations every month:

    Subscribe to my blog: https://chainsofindia.wordpress.com/

    Follow me on Twitter @aranke_priyank

    I would be happy to discuss the data and the algorithm behind the model and how it can used in your business. Please feel free to contact me at aranke.priyank99@gmail.com

    Priyank Aranke (aranke.priyank99@gmail.com)

    Thank you for your time.

    https://chainsofindia.wordpress.com/https://twitter.com/aranke_priyankmailto:aranke.priyank99@gmail.commailto:aranke.priyank99@gmail.com

  • REFERENCES

    Recommender Systems:

    https://en.wikipedia.org/wiki/Recommender_system

    https://en.wikipedia.org/wiki/Collaborative_filtering

    Data sources:

    Slide 2 Capital First Annual Report 2015-16

    Slide 3 2011 India Census, Capital First branch locator

    Slide 8 Respective Franchise websites

    Source code: https://github.com/priyankaranke/recsystemsforfranchise/blob/master/Rec_systems_for_franchises.R

    Locations data (for 26 businesses and 1476 locations) available for reference by request

    https://en.wikipedia.org/wiki/Recommender_systemhttps://en.wikipedia.org/wiki/Collaborative_filteringhttp://www.capitalfirst.com/pdfs/CFL-AR-2015-16-along-with-AGM-Notice.pdfhttp://www.census2011.co.in/city.phphttp://www.capitalfirst.com/branch-locatorhttps://github.com/priyankaranke/recsystemsforfranchise/blob/master/Rec_systems_for_franchises.Rhttps://github.com/priyankaranke/recsystemsforfranchise/blob/master/Rec_systems_for_franchises.Rhttps://github.com/priyankaranke/recsystemsforfranchise/blob/master/Rec_systems_for_franchises.R

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