Which city should Capital First open a branch next?

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<ul><li><p>WHICH CITY SHOULD CAPITAL FIRST OPEN A BRANCH NEXT?</p><p>Priyank Aranke (aranke.priyank99@gmail.com)</p><p>An approach using cutting edge computational techniques</p><p>October 2016</p><p>mailto:aranke.priyank99@gmail.com?subject=</p></li><li><p>THE PROBLEM</p><p>V. VAIDYANATHAN IN CAPITAL FIRST ANNUAL REPORT 2015-16 MARCH 31, 2016</p><p>CLEARLY THERE IS LOT OF OPPORTUNITY FOR MSME SECTOR IN TIER 2 CITIES </p><p>the Cabinet approved a proposal for the introduction of the Micro, Small and Medium Enterprises Development (Amendment) Bill, 2015</p><p>Indias 50 million MSMEs and its fast emerging middle class, with a differentiated model, based on new technologies, provides a large and unique opportunity.</p></li><li><p>WHERE SHOULD CAPITAL FIRST OPEN SERVICES NEXT?</p><p>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?</p><p>SOURCE: HTTP://WWW.CAPITALFIRST.COM/BRANCH-LOCATOR</p><p>http://www.capitalfirst.com/branch-locator</p></li><li><p>RECOMMENDER SYSTEMS AROUND US</p><p>WE WILL USE THE SAME TECHNOLOGY THAT AMAZON USES TO RECOMMEND ITEMS</p></li><li><p>RECOMMENDER SYSTEMS AROUND US</p><p>AND NETFLIX USES TO RECOMMEND MOVIES</p></li><li><p>THE HIGH LEVEL APPROACH</p><p>ITS CALLED RECOMMENDER SYSTEMS</p><p> Why Amazon and Netflix recommendations are so good </p><p> They have data on purchase history of millions of people </p><p> So they can figure out people who have tastes similar to you </p><p> Then they recommend to you what people like you have liked</p></li><li><p>THE HIGH LEVEL APPROACH</p><p>WHICH CAN BE ALSO APPLIED TO RECOMMEND NEW CITIES</p><p> Heres how: </p><p> Collect data on thousands of Indian towns and cities </p><p> Find out the businesses who have locations similar to you </p><p> Recommend the locations which businesses like you have discovered</p></li><li><p>FRANCHISE DATA</p><p>PROPRIETARY, HAND-COLLECTED AND CAREFULLY CURATED DATA ON 26 FRANCHISES AND 1496 CITIES IS INPUT TO THE RECOMMENDER ALGORITHM</p><p>Franchise No. of cities</p><p>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</p><p>Franchise No. of cities</p><p>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</p><p>DATA AS OF JULOCT 2016</p></li><li><p>THE RECOMMENDER ALGORITHM</p><p>RECOMMENDER ALGORITHM</p><p>THE RECOMMENDER ALGORITHM GENERATES TOP LOCATIONS WHERE CAPITAL FIRST SHOULD OPEN BRANCHES - BASED ON LOCATIONS OF OTHER SIMILAR BUSINESSES</p><p>SEE REFERENCES SLIDE FOR TECHNICAL DETAILS ABOUT THE RECOMMENDER ALGORITHM</p></li><li><p>AND THE OUTPUT OF THE ALGORITHM IS</p><p>DATA AS OF OCT 2016</p><p>RECOMMENDED NEW CITIES F0R CAPITAL FIRST TO OPEN BRANCHES NEXT</p></li><li><p>IN ADDITION TO RECOMMENDING WHERE YOU SHOULD OPEN THE STORES NEXT, THE TECHNIQUE CAN ALSO BE USED TO:</p><p> Find out which of the existing stores are under or over-performing The model outputs a score for each city which indicates the business </p><p>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. </p><p> Predicting which cities a given competitor would target next Since the recommendation engine works on publicly available data, </p><p>we can use it to predict the locations which a competitor would target next. This will help you plan your response in advance. </p><p> 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.</p><p>MANY WAYS TO USE THIS TECHNOLOGY</p></li><li><p>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.</p><p>SUCCESSFUL PREDICTIONS ON BAJAJ FINANCE BRANCH LOCATIONS</p><p>Predicted in August 2016</p><p>Added in October 2016</p><p>Agra Ambala Bhopal Dehradun Erode Goa Jabalpur Jalandhar Jamshedpur Jodhpur Kanpur Kolhapur Lucknow </p><p>Predicted in August 2016</p><p>Added in October 2016</p><p>Ludhiana Mangalore Patiala Patna Raipur Rajkot Ranchi Salem Tiruchirappalli Amritsar Guwahati Mohali </p></li><li><p>FUTURE PREDICTIONS ON BAJAJ FINANCE BRANCH LOCATIONS</p><p>PREDICTED NEW CITIES FOR BAJAJ FINANCE LOAN PRODUCTS</p><p>City Doctor Home Property Business Personal</p><p>Amritsar Goa Guwahati Jalandhar Kanpur Lucknow Mangalore Mohali Mysore Patiala Patna Raipur Trivandrum </p><p>DATA AS OF OCT 2016</p></li><li><p>DATA AS OF OCT 2016</p><p>FUTURE PREDICTIONS ON BAJAJ FINANCE BRANCH LOCATIONS ON A MAP</p></li><li><p>TO KNOW FURTHER</p><p> To get real time recommendations every month: </p><p> Subscribe to my blog: https://chainsofindia.wordpress.com/ </p><p> Follow me on Twitter @aranke_priyank </p><p> 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</p><p>Priyank Aranke (aranke.priyank99@gmail.com)</p><p>Thank you for your time.</p><p>https://chainsofindia.wordpress.com/https://twitter.com/aranke_priyankmailto:aranke.priyank99@gmail.commailto:aranke.priyank99@gmail.com</p></li><li><p>REFERENCES</p><p> Recommender Systems: </p><p> https://en.wikipedia.org/wiki/Recommender_system </p><p> https://en.wikipedia.org/wiki/Collaborative_filtering </p><p> Data sources: </p><p> Slide 2 Capital First Annual Report 2015-16 </p><p> Slide 3 2011 India Census, Capital First branch locator </p><p> Slide 8 Respective Franchise websites </p><p> Source code: https://github.com/priyankaranke/recsystemsforfranchise/blob/master/Rec_systems_for_franchises.R </p><p> Locations data (for 26 businesses and 1476 locations) available for reference by request</p><p>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</p></li></ul>


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