WHICH CITY SHOULD OLA CABS START SERVICES NEXT?

Priyank Aranke (aranke.priyank99@gmail.com)

An approach using cutting edge computational techniques

November 2016

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

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

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–NOV 2016

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

AND THE OUTPUT OF THE ALGORITHM IS…

DATA AS OF NOV 2016

RECOMMENDED NEW CITIES F0R OLA CABS TO OPEN SERVICES IN NEXT

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

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 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?

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:

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.

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