An approach based on cutting edge computational techniques. The approach uses data mining and analysis, R, Recommender systems and business intelligence to suggest a business its next move.

1. WHICH CITY SHOULD OLA CABS START SERVICES NEXT?
Priyank Aranke (aranke.priyank99@gmail.com)
An approach using cutting edge computational techniques
November 2016

2. THE PROBLEM
THERE ARE 496 CITIES IN INDIA WITH A POPULATION OF OVER 1 LAKH.
OLA CABS IS PRESENT IN 87.
WHICH OF THE REMAINING 409 CITIES SHOULD OLA OPEN IN NEXT?
SOURCE: HTTPS://WWW.OLACABS.COM/FARES

3. RECOMMENDER SYSTEMS AROUND US
WE WILL USE THE SAME TECHNOLOGY THAT AMAZON USES TO RECOMMEND ITEMS

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

5. 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

6. 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

7. 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–NOV 2016

8. THE RECOMMENDER ALGORITHM
RECOMMENDER ALGORITHM
THE RECOMMENDER ALGORITHM GENERATES TOP LOCATIONS WHERE OLA CABS
SHOULD OPEN—BASED ON LOCATIONS OF OTHER SIMILAR BUSINESSES
SEE REFERENCES SLIDE FOR TECHNICAL DETAILS ABOUT THE RECOMMENDER ALGORITHM

9. AND THE OUTPUT OF THE ALGORITHM IS…
DATA AS OF NOV 2016
RECOMMENDED NEW CITIES F0R OLA CABS TO OPEN SERVICES IN NEXT

10. IN ADDITION TO RECOMMENDING WHERE YOU SHOULD START
SERVICES NEXT, THE TECHNIQUE CAN ALSO BE USED TO:
▸ Find out where business in existing cities is 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
number of rides in that city to determine whether that city is under or
over-performing.
▸ Predicting which cities a given competitor will target next
▸ Since the recommendation engine works on publicly available data,
we can use it to predict the locations which a competitor will target
next. This will help you plan your response in advance.
▸ These predictions have worked in the past. See next slides for my
successful past predictions on loan provider Bajaj Finance’s store
openings.
MANY WAYS TO USE THIS TECHNOLOGY

11. 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 —

12. FUTURE PREDICTIONS ON UBER’S LOCATIONS
I’VE MENTIONED THAT THIS MODEL CAN BE USED TO PREDICT A
COMPETITORS’ NEXT MOVE.
SO… WHERE WILL UBER START BUSINESS NEXT?

13. DATA AS OF NOV 2016
FUTURE PREDICTIONS ON UBER’S LOCATIONS – ON A MAP
THESE ARE MY MODEL’S TOP PREDICTIONS ON WHERE UBER WILL OPEN NEXT:

14. 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.

15. REFERENCES
▸ Recommender Systems:
▸ https://en.wikipedia.org/wiki/Recommender_system
▸ https://en.wikipedia.org/wiki/Collaborative_filtering
▸ Data sources:
▸ Slide 2 – 2011 India Census, Ola Cabs Fare Chart
▸ Slide 7 – 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