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Priyank Aranke ([email protected])
An approach using cutting edge computational techniques
November 2016
WHICH CITY SHOULD UBER OPEN SERVICES NEXT?
THE PROBLEM
THERE ARE 496 CITIES IN INDIA WITH A POPULATION OF OVER 1 LAKH. UBER IS PRESENT IN 29. WHICH OF THE REMAINING 467 CITIES SHOULD UBER OPEN IN NEXT?
SOURCE: HTTPS://WWW.UBER.COM/EN-IN/CITIES/
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 29V-Mart 104
DATA AS OF JUL–NOV 2016
THE RECOMMENDER ALGORITHM
RECOMMENDER ALGORITHM
THE RECOMMENDER ALGORITHM GENERATES TOP LOCATIONS WHERE UBER SHOULD OPEN BRANCHES - BASED ON LOCATIONS OF OTHER SIMILAR BUSINESSES
SEE REFERENCES SLIDE FOR TECHNICAL DETAILS ABOUT THE RECOMMENDER ALGORITHM
DATA AS OF NOV 2016
AND THE OUTPUT OF THE ALGORITHM IS…
RECOMMENDED NEW CITIES F0R UBER 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 OLA CABS LOCATIONS
I MENTIONED THAT THIS MODEL CAN BE USED TO PREDICT A COMPETITORS’ NEXT MOVE.
SO… WHERE WILL OLA CABS START BUSINESS NEXT?
FUTURE PREDICTIONS ON OLA CABS LOCATIONS – ON A MAP
DATA AS OF NOV 2016
THESE ARE MY MODEL’S TOP PREDICTIONS ON WHERE OLA CABS 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 [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 – 2011 India Census, Uber Cities
▸ 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
