An approach using cutting edge computational techniques

1. WHICH TOWN SHOULD BAJAJ FINANCE OPEN A BRANCH NEXT?
Priyank Aranke (aranke.priyank99@gmail.com)
An approach using cutting edge computational techniques
October 2016

2. 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 ”

3. 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?

4. 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 —

5. RECOMMENDER SYSTEMS AROUND US
IT’S THE SAME TECHNOLOGY THAT AMAZON USES TO RECOMMEND ITEMS

6. RECOMMENDER SYSTEMS AROUND US
AND NETFLIX USES TO RECOMMEND MOVIES

7. 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

8. 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

9. 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 462
Bajaj Finserv 292
Cafe Coffee Day 220
Capital First 43
Domino’s Pizza 245
Dunkin’ Donuts 23
Eicher Motors 291
Equitas Mf 36
Gruh Finance 155
Hero MotoCorp 603
Hypercity 12
Inox Leisure 52
Janalakshmi Fin. 166
Franchise No. of cities
Kalyan Jewellers 59
Kotak Mahindra Bank 537
More Store 153
Ola Cabs 87
PVR Cinema 39
Repco Home Finance 102
Shoppers’ Stop 34
Sony Electronics 145
Sriram Vehicle Finance 794
Tanishq Jewellers 108
Toyota 220
Uber 27
V-Mart 104
DATA AS OF JUL–OCT 2016

10. 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

11. 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

12. HERE’S THE SAME DATA ON A MAP
DATA AS OF OCT 2016

13. 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

14. KEEP WATCH ON THE COMPETITION – TOP 10 CITIES WHERE CAPITAL FIRST WILL OPEN NEXT
DATA AS OF OCT 2016

15. 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.

16. 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