How to make data science products
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Lessons learned by making data science products at Tokopedia. This includes a recommendation engine, marketing automation and challenges with data science and AI products.
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How to make data science products
- 1. How to make Data Science Products LESSONS FROM TOKOPEDIA
- 2. WHO AM I Paytm QR Code Paytm PassBook Paytm Merchant SDK Paytm Payments Bank Paytm Offline Pay etc. I started working as PM straight out of college with Paytm. I was focussed on making Paytm Consumer Products for next 2 years in which I launched - Referral and it's Fraud Detection User Clustering and Product Clustering Marketing Optimization NLP and Image Cassification for Product Catalogue Then I worked with Tokopedia in Indonesia as Growth and Data Product Lead. Products - Currently, I am working with Branch Metrics, which is based in SF and responsible for their Core Platform and Deep Linking product.
- 3. SOME MORE INFO Top Indonesian Everyday App with Valuation of $7B Founded: 6 February 2009 Tokopedia has raised a total of $2.4B in funding over 9 rounds Top App in Indonesia Interested in Data Science and Got my first job accidentaly because of it. Courses - Data Camp, AI Product Manager Udacity, Machine Learning and Deeplearning courses online. Spend my time reading Life 3.0, AI Superpowers, Fourth Age etc. Tokopedia - Me -
- 4. WHAT IS AI-ML-DL Image Source - Nividia Artificial Intelligence — Human Intelligence Exhibited by Machines Machine Learning — An Approach to Achieve Artificial Intelligence Deep Learning — A Technique for Implementing Machine Learning
- 5. LET'S SIMPLIFY THE FLUFF Prediction Optimization Classification Regression Optimization Simulation
- 6. Products worked on Recommendation Engine Marketing automation Products we will discuss Fraud Detection for Referral Recommendation Engine NLP based sort for Product Feed Image Recognition based product cleaning Marketing automation Purchase Predictor Chatbot with Microsoft
- 7. RECOMMENDATION User Based Clustering Product Based Clustering Cluster based Collaborative Filtering What - Recommend users product which they will be interested in, with a target to increase Conversion Rate (View->Purchase). How -
- 8. RECOMMENDATION Infer user properties based on user parameters like - Purchase, View, Price of Product, Category, Address, Device, Payment Method, Shops, Type of Product etc. Determine - Religion, SES, Occupation, Age, Gender, Interests, Marital Status, Kids Take explicit consent from user about data Give user an option to opt out of the recommendation and data processing User Based Clustering
- 9. C2C platform so anyone can upload anything and put description and categories. Use various techniques - Session Based clusters Image Recognition NLP on Product Title, Description, Category and Sub Category Why were merchants uploading bad image? Why the products were incorrectly labeled? Product Clustering
- 10. Things with similar attribute are going to be of similar interest to user. Recommend something based on past similar data. Can be product (Amazon), content (Netflix), or users (Facebook) E.g. People who are seeing iPhone will be interested in iphone cover too once they PURCHASE iPhone, because that's what other SIMILAR user did. Session Based Recommendation Cluster Based Collaborative Filtering
- 11. MARKETING AUTOMATION Targeting right user with right product at right time through right channel Using the Recommender system above and Some more data - Most active platform (App, Mobile Web, Desktop) Most active channel (Email, SMS, Push Notification, Banner) Most Active Time of day, week and month Then integrate the product recommendation to these marketing channels using APIs What - RESULT - ~26% increase in Conversion Rate and ~40% in CTR
- 12. CHALLENGES Scale of data - 60M DAU and data in peta byte, cost is a factor Edge Case - Blocked the sensitive categories in Ramadan and ended up showing a magazine with nudity. Social Media Bashing Seasonality - Festivals, Back to Schools, Measurement - Have the goal and how you will measure it in mind and discuss with team. Variance - If you don't include new data then you will get into spiral. Always have at least 30% variance. C2C Marketplace - There is no standardization here. Cultural aspects - Language, Trends, Life Style, Spending Power
- 13. UNDERSTAND THE PROBLEM Credits - Udacity
- 14. Five Vs of Data - Volume - Broadly speaking, the amount of data that is being produced over any given unit of time Variety - The level of deviation within your data, which can have both positive and negative effects depending on what it is you’re hoping to achieve Velocity - A term referring to how quickly new data is produced. Velocity can also allude to the concept of drift, or, how quickly data underlying a model can change over time Veracity - The accuracy of data that is being collected, a trait which can be affected by faulty inputs, poor organization, or a variety ofs other factors Value - A holistic measure based on all other underlying characteristics of data and rooted in how likely the data is to help you reach your desired end state EVALUATE THE DATA
- 16. Control Group Goal and Metrics Feedback Loop Improve DEPLOY SMALL, MEASURE AND OPTIMIZE