The document discusses how Paytm, India's largest mobile commerce platform, uses data science. It outlines Paytm's business model of providing mobile recharging, bill payments, e-commerce marketplace, digital wallet, and other services. Paytm focuses on data science to detect fraud, understand customer behavior, perform predictive analytics, and make marketing and business decisions. The company's data science team uses techniques like Hadoop, text mining, and social media analysis. Lessons learned are that data science helps Paytm understand consumers and cope with fraud while increasing products.

1. Analytics in Action
Case Study on
Paytm
February 2018
S.GAUTAM
R.BHATNAGAR
K.CHELANI

2. Introduction
©2016 L. SCHLENKER
Structure
Introduction
Business Model
Why Data Science ?
Techniques and Decision Making
Data Science Team
Lessons Learned

3. • What is the organization’s business
model?
• Why does the organization focus on
data?
• Which data science techniques does
the organization favor ?
• What is the link between data science
and decision making?
• How is the Data Science team
organized?
• How does the organization use Data
Science to propel growth
Case Study Questions
Technology

4. Reference: https://expandedramblings.com/index.php/paytm-statistics-facts/
• Started in 2010, Founder – Vijay Shekhar
Sharma
• CEO – Renu Satti
• India's Largest mobile commerce
• At Paytm, you have the option of
recharging and shoping from whenever,
anywhere and are equipped with a
secure online wallet called Paytm Cash.
• Revenue: Rs. 813.88 Crore
• Value: Rs. 2,376.6 Crore
• 280 million registered users, 5 million
daily transactions
• SoftBank, SAIF Partners, Alibaba Group,
Ant Financial (Alipay) and Mediatek.
Introduction
Technology

5. Reference: https://www.feedough.com/paytm-business-model-how-paytm-makes-money/
• Recharge
• Bill Payments
• Marketplace
• Wallet
• Digital Gold
• Paytm Bank
Business Model
Technology
Business Model
Recharge
Bill
Payments
Marketplace
Wallet
Digital Gold
Paytm Bank

6. Paytm Payment Bank
Technology

7. Reference: https://analyticsindiamag.com/paytm-unveils-payments-bank-data-science-core
• Fraud Detection: Hadoop
• Advertising
• User behaviour
• Customer acquisition
• Build credit score models
• Reach last mile customers
• Quick summarization, plots and
prototypes
• Text mining, production models and
scalable solutions
Why Data Science ?
Technology

8. • Predictive Analytics (Future
needs)
• Facebook Analytics (Reactive
marketing)
• Google Analytics (Track and
monitoring)
Techniques and Decision Making
Technology

9. • Social Media Sites –
Facebook, Twitter, etc.
• Mobile application
consumption pattern
• Users Worldwide
Data Sources
Technology

10. • Paytm Labs – Toronto
• Head – Harinder Takhar
• 18 Engineers, 5 Machine
learning engineers, 12 system
engineers.
• Labs with Highly Qualified team
with 52% Bachelors, 45% Masters
& 3% Doctorate Degree.
• People from Different countries
speaking 18 languages
• Innovation Lab – Canada
• 50 engineers, developers and
data scientists
Data Science Team
Technology

11. Lessons learned
Technology
• Data Science is helping Paytm to know
consumer behavior.
• Analytics is helping Paytm to detect frauds
and cope with them.
• Analytics helped them to increase their
product portfolio.

12. • https://analyticsindiamag.com/paytm-unveils-
payments-bank-data-science-core
• https://expandedramblings.com/index.php/paytm
-statistics-facts/
• https://successstory.com/companies/paytm
• https://www.feedough.com/paytm-business-
model-how-paytm-makes-money/
• https://www.paytmlabs.com/
Bibliography
Next Steps