webinar - know your customer - arya (20160526)
TRANSCRIPT
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Know Your Customer:Using Machine Learning to Improve Sales Conversions and Marketing Campaigns
Rajat Arya – Director, [email protected] @rajatarya
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Hello, my name is…
Rajat AryaDirector, Sales (also Dato employee #1)
(software engineer, distributed systems, NBA and movie nerd, learning data science)
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Intelligent applications create tremendous value
…but are slow to build & require large specialized teams
RecommendersLead Scoring
Churn Prediction
Multi-channel TargetingAuto-Summarization
Fraud detectionIntrusion Detection
Demand Forecasting
Data MatchingFailure Prediction
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,….
Accelerate innovators to create intelligent applications
with agile machine learning
Our mission
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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
Dato Products – The Agile Machine Learning Platform
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
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We are making this happen now with our
customers
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Poll: Getting to know you
1. What do you do?2. Are you using Lead Scoring today?
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Intelligent applications create tremendous value
RecommendersLead Scoring
Churn Prediction
Multi-channel TargetingAuto-Summarization
Fraud detectionIntrusion Detection
Demand Forecasting
Data MatchingFailure Prediction
Lead Scoring : Use what you know about your customers to maximize your sales & marketing efforts.
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/
Teams that get Lead Scoring right have a 192% higher average qualification
rate.
Lead Scoring : Motivation
Aberdeen Group
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.
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
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.
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
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 customers
Deploy
• Predict likelihood to convert on new leads
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)
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.
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Poll: Data Science at your workplace
1. Does your team have data scientists or developers?2. Are you using Machine Learning in production today?
Lead Scoring Demo
Thank you!
Want to find out how to incorporate lead scoring into your organization? Ping me
Coursera ML Specializationhttp://coursera.org/specializations/machine-learning
twitter: @rajatarya, email: [email protected]