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Webinar - Know Your Customer - Arya (20160526)

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Webinar - Know Your Customer - Arya (20160526)

  1. 1. 11 Know Your Customer: Using Machine Learning to Improve Sales Conversions and Marketing Campaigns Rajat Arya – Director, Sales rajat@dato.com @rajatarya
  2. 2. 22 Hello, my name is… Rajat Arya Director, Sales (also Dato employee #1) (software engineer, distributed systems, NBA and movie nerd, learning data science)
  3. 3. 33 Intelligent applications create tremendous value …but are slow to build & require large specialized teams Recommenders Lead Scoring Churn Prediction Multi-channel Targeting Auto-Summarization Fraud detection Intrusion Detection Demand Forecasting Data Matching Failure Prediction
  4. 4. Core blockers to innovators • Mapping business task to ML problem requires experts - For example certain recommender systems require matrix factorization… • Painful to evaluate, improve & combine ML models - Enormous amount of time on low-value integration, feature engineering & validation • Multiple systems to deploy & manage ML in production - Custom build everything: deployment, monitoring, online experimentation,….
  5. 5. Accelerate innovators to create intelligent applications with agile machine learning Our mission
  6. 6. 6 Dato’s Machine Learning Core Tenets • Maps business tasks to machine learning routines • Eliminates bottlenecks to production • Simplifies iteration & understanding Create Value Fast • Easily combine any variety of features & ML tasks with any data • Platform components are open, reusable, & sharable • Easily extend & integrate with other frameworks Flexibility to Innovate • Make ML safe & consumable for the enterprise • Easily deploy, manage, and improve ML as intelligent micro-services • Adapt to a changing world that drifts from your historical data Intelligence in Production
  7. 7. Dato Products – The Agile Machine Learning Platform
  8. 8. import graphlab as gl data = gl.SFrame.read_csv('my_data.csv') model = gl.recommender.create( data, user_id='user', item_id='movie’, target='rating') recommendations = model.recommend(k=5) cluster = gl.deploy.load(‘s3://path’) cluster.add(‘servicename’, model) Agile ML Example: create a live machine learning service Create a Recommender 5 lines of code Toolkit w/auto selection Deploy in minutes
  9. 9. 9 We are making this happen now with our customers
  10. 10. Poll: Getting to know you 1. What do you do? 2. Are you using Lead Scoring today? 10
  11. 11. 1111 Intelligent applications create tremendous value Recommenders Lead Scoring Churn Prediction Multi-channel Targeting Auto-Summarization Fraud detection Intrusion Detection Demand Forecasting Data Matching Failure Prediction
  12. 12. Lead Scoring : Use what you know about your customers to maximize your sales & marketing efforts.
  13. 13. Teams that implement Lead Scoring see a 77% lift in ROI. Lead Scoring : Motivation http://sherpablog.marketingsherpa.com/b2b-marketing/lead-gen/lead-scoring-tips/
  14. 14. Teams that get Lead Scoring right have a 192% higher average qualification rate. Lead Scoring : Motivation Aberdeen Group
  15. 15. Lead Scoring : Practical Definition Inefficient customer acquisition is costing your business money. Your teams have limited resources (money, people, & time) Lead Scoring enables sales & marketing teams to prioritize incoming leads to maximize their efficiency in gaining new customers.
  16. 16. Lead Scoring : Practical Results Once your teams are scoring leads, you can expect: 1. Higher conversion rates 2. Shorter conversion cycles 3. Increased revenue Metric Before After ’Qualified’ Leads 1,000 600 Opportunity win rate 25% 40% Average Revenue per sale $50,000 $62,500 Total Revenue $25MM $32MM
  17. 17. Lead Scoring : Without Machine Learning Belief & Intuition about customers: We are hot with the youth segment, we should target them. Or your customers are price-sensitive which overlaps with youth. We should be reaching out to people within an hour of signing up. Being timely in 1st contact is critical. Does data back this up? Maybe 4th day is equally effective.
  18. 18. Lead Scoring : With Machine Learning Benefits of Machine Learning for Lead Scoring: • Leverage historical data about customers • Learn patterns of behavior and customer profile that indicate propensity to convert (quickly) • Understand what attributes of a user indicate their likelihood to become a customer • Predict probability of conversion of new lead, prioritize accordingly
  19. 19. Lead Scoring : Machine Learning Process Supervised Machine Learning workflow: Historical Data • Split train/test datasets • Customers & non- customers Train ML Model • Use the attributes of customers • Use behaviors of Deploy • Predict likelihood to convert on new leads
  20. 20. Lead Scoring : Machine Learning (Advanced) • Incorporate Time as a feature (ex. when did a customer take an action, how much time elapsed between actions, how many total actions, how many actions per week) • Transform customer attributes to more meaningful data (ex. age  age range, zip code  state, time of day  morning/evening) • Predict when a customer will convert (ex. Bob will convert in next 7 days with 80% probability)
  21. 21. Lead Scoring & Customer Segmentation Customer Segmentation is learning the common attributes of your customers and splitting them accordingly. Better target each segment. Predict which segment a new lead belongs to utilize that for prioritization or conversion strategy.
  22. 22. Poll: Data Science at your workplace 1. Does your team have data scientists or developers? 2. Are you using Machine Learning in production today? 22
  23. 23. Lead Scoring Demo
  24. 24. Thank you! Want to find out how to incorporate lead scoring into your organization? Ping me Coursera ML Specialization http://coursera.org/specializations/machine-learning twitter: @rajatarya, email: rajat@dato.com

Notes de l'éditeur

  • Notes:
    Didn’t reiterate intelligent applications
    Didn’t go into the building blocks of intelligent apps
    Didn’t talk about why this is painful
    Didn’t hit the plethora of applications possible
    Didn’t bring it back to what they care about most “did I answer your questions, do you see how we would fit/be used by your company”
    Didn’t talk about the future w/many models in prod
    Microservices seemed to low level
    Less filler talk – ANSWER THE QUESTION!

    Applications with data, it’s not professional, looking for better ways

    Didn’t land how these applications cut across groups

    How do you compare? Where is SAS?

    Do you handle compliance – explaining predictions (important for compliance?)

    Bring collateral & handouts

    Intelligent microservices wraps models/analyses in a consumable service accessible & consumable by anyone across the enterprise
  • Empower businesses not about create, stay competitive, destroy,
  • Move this up
  • Poll:
    What do you do?
    Product Development
    Information Technology
    Human Resources
    Are you using Lead Scoring Today?
    No, here to learn more.
    Yes, with Marketo
    Yes, with Salesforce
    Yes, with Tableau
    Not sure, I think so.
    Not sure, I don’t think so.
  • Poll:
    Does your team have data scientists or developers?
    Yes, a full team of data scientists.
    Yes, a full team of developers.
    Yes, a mixed team of both.
    No, but my engineering team does.
    No, but my R+D team does.
    I don’t have a team.
    Are you using ML in production today?
    Yes, for real-time predictions.
    Yes, for batch predictions.
    No, but on the roadmap.
    No, not sure.