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Know Your Customer:Using Machine Learning to Improve Sales Conversions and Marketing Campaigns

Rajat Arya – Director, Salesrajat@dato.com @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: rajat@dato.com