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WHICH TOWN SHOULD BAJAJ FINANCE OPEN A BRANCH NEXT? Priyank Aranke ([email protected]) An approach using cutting edge computational techniques October 2016
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WHICH TOWN SHOULD BAJAJ FINANCE OPEN A BRANCH NEXT?
Priyank Aranke ([email protected])
An approach using cutting edge computational techniques
October 2016
THE PROBLEM
RAJEEV JAIN IN BAJAJ FINANCE Q1 FY2017 RESULTS CONFERENCE CALL JULY 26, 2016
CLEARLY THERE IS LOT OF OPPORTUNITY IN TIER 2/3 CITIES …
“ We are now doing salary personal loans with business in 70 cities, three years ago we were doing in 25 cities, five year ago, we are doing in 15 cities …
… if you change the mix and you can do less business through distributors you can do more business in tier II ”
WHERE SHOULD BAJAJ FINANCE OPEN SERVICES NEXT?
THERE ARE 496 CITIES IN INDIA WITH A POPULATION OF OVER 1 LAKH. BAJAJ FINANCE IS PRESENT IN 292. WHICH OF THE REMAINING 204 CITIES SHOULD BAJAJ FINANCE OPEN NEXT?
IN AUG 2016, USING THE APPROACH I WILL DESCRIBE NEXT, 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.
AN APPROACH BASED ON RECOMMENDER SYSTEMS
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 —
RECOMMENDER SYSTEMS AROUND US
IT’S THE SAME TECHNOLOGY THAT AMAZON USES TO RECOMMEND ITEMS
RECOMMENDER SYSTEMS AROUND US
AND NETFLIX USES TO RECOMMEND MOVIES
THE HIGH LEVEL APPROACH
IT’S 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
▸ Here’s 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 43Domino’s 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 JUL–OCT 2016
THE RECOMMENDER ALGORITHM
RECOMMENDER ALGORITHM
THE RECOMMENDER ALGORITHM GENERATES TOP LOCATIONS WHERE BAJAJ FINANCE SHOULD OPEN BRANCHES - BASED ON LOCATIONS OF OTHER SIMILAR BUSINESSES
SEE REFERENCES SLIDE FOR TECHNICAL DETAILS ABOUT THE RECOMMENDER ALGORITHM
13 CITIES ARE SUITABLE FOR MORE THAN 1 LOAN PRODUCT
RECOMMENDED NEW CITIES FOR DIFFERENT LOAN PRODUCTS FROM BAJAJ FINANCE
City Doctor Home Property Business Personal
Amritsar ✓ ✓ — ✓ ✓Goa — ✓ — ✓ —Guwahati ✓ — ✓ — ✓Jalandhar ✓ ✓ — ✓ —Kanpur ✓ — ✓ — —Lucknow ✓ ✓ — ✓ —Mangalore ✓ ✓ — ✓ —Mohali — — ✓ — ✓Mysore ✓ — — ✓ —Patiala — ✓ ✓ ✓ —Patna ✓ ✓ ✓ — —Raipur ✓ ✓ — — —Trivandrum — — ✓ ✓ —
DATA AS OF OCT 2016
HERE’S THE SAME DATA ON A MAP
DATA AS OF OCT 2016
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.
▸ For example, see the next slide for my prediction on where Capital First will open its next branches.
MANY WAYS TO USE THIS TECHNOLOGY
KEEP WATCH ON THE COMPETITION – TOP 10 CITIES WHERE CAPITAL FIRST WILL OPEN NEXT
DATA AS OF OCT 2016
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 [email protected]
Priyank Aranke ([email protected])
Thank you for your time.
REFERENCES
▸ Recommender Systems:
▸ https://en.wikipedia.org/wiki/Recommender_system
▸ https://en.wikipedia.org/wiki/Collaborative_filtering
▸ Data sources:
▸ Slide 2 – FY17 Q1 Bajaj Finance Transcript
▸ Slide 3 – 2011 India Census, Bajaj Finance branch locator
▸ Slide 9 – 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
