Smarter Predict ions

Dean Abbott Co-Founder and Chief Data Scient ist , SmarterHQ

President, Abbott Analyt ics

dabbott@smarterhq.com Twit ter : @deanabb

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SmarterHQ - Why

Built For Marketers, By Marketers 40+ years multi-channel retail

25+ years machine learning

20+ years digital marketing

Value Delivered, Clients Proven time to value

Self funding platform

20x ROI

Thought Leadership, Funding 2015 Sherpa campaign of the year

8 eTail award winning clients

Funded by Battery Ventures & CNB

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SmarterHQ - Who

•  A platform that unites customer intelligence and cross-channel marketing.

•  Focuses on attainable innovation that grows with retailers.

•  Delivers on the promise of unifying marketing channels to provide consistent messaging, offers

and promotions…and more.

•  Places shoppers at the center of the conversation to provide a full understanding of a shoppers

relationship with a brand, from online to in-store.

•  Enables marketers to take action on this intelligence within their existing marketing partner

investments; from email, to display to onsite personalization.

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Website Mobile

App In-Store Call

Center 3rd Party

Data for Customer Intelligence Customer-centric, Multi-channel

B R A N D L E V E L

E N T E R P R I S E L E V E L

O T H E R B R A N D S

#3314

#9102

#2456

#5306

Droid

iOS

Mobile S

ite

Desktop S

ite

Am

azon

eBay

Web S

upport

Spring C

atalog

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Website Mobile

App In-Store Website Mobile

App In-Store

B R A N D L E V E L B R A N D L E V E L

iOS

Desktop S

ite

Call Center 3rd Party

#3314 #2456

iOS

Desktop S

ite

Mobile S

ite

Customer Intelligence – Why It Matters

Droid

Am

azon

eBay

Web S

upport

Spring C

atalog

#9102

E N T E R P R I S E L E V E L

O T H E R B R A N D S

He is a different shopper at every level…

Integrating his interactions tells a complete story.

W H O I S F R E D ? #5306

Droid

#9102

#3314

E N T E R P R I S E L E V E L

O T H E R B R A N D S

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H O W

• How are we communicating?

W H A T

• What are we talking about?

It’s A Conversation

G O A L

Each interaction, or lack thereof, with a brand is a shopper trying to have a conversation.

W H O

• Who am I talking to?

Email Display

Complete The Conversation

Customer Intelligence

W H O W H A T

Cross-Channel Marketing

H O W

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Tuning Abandonment - Conversation

G O A L

Recover a shopper that abandoned their shopping cart .

W H O W H A T H O W

• Anyone • Recent Visit To Website

• Carted Items

• Did Not Purchase

• Targeted Messaging

• Automated Email

Email

Version 1…

What is the right action?

W H O I S K R I S T E N ?

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Tuning Abandonment - Conversation

G O A L

Recover a shopper that abandoned their shopping cart that was acquired through paid media.

W H O W H A T H O W

• Guest

• High Marketing Cost

• First Visit To Website

• Carted Items

• Did Not Purchase

• Targeted Messaging

• Targeted Offer

• Automated Email

Email

Version 2…

What is the right action?

W H O I S K R I S T E N ?

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Tuning Abandonment - Conversation

G O A L

Recover a shopper that abandoned their shopping cart that was acquired through paid media.

W H O W H A T H O W

• Guest

• High Marketing Cost

• First Visit To Website

• Carted Items

• Did Not Purchase

• Targeted Messaging

• Targeted Offer

• Automated Email

• Onsite Messaging

Email Onsite

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Optimizing Margin – The “Non-Conversation”

G O A L

I want to optimize the value of my High AOV, engaged customer.

W H O W H A T H O W

• Customer

• High Dollar AOV

• Highly Engaged

• Browsed Premium Product several

times in past 3 days

• High predicted propensity to purchase

within the next 3 days

• Get out of his way and let him

purchase.

• Save margin and do not make an offer

• if he doesn’t purchase within 3 days,

then make an offer if purchase propensity

still high

Print

Email Onsite Display

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Predictive Models -> Insights

Asset Quality Visit Quality Engagement (with

Visit Quality Decay)

Checkout Abandon Causality

Marketing Attribution

Category Browse Abandonment

Customer Attrition Risk

Purchase Replenishment

Predicted Next Product/Category

to Purchase

Cross-Sell / Up-Sell /

Recommendations

Predicted Days to Next Purchase

Predicted Days to Next Visit

Customer (Lifetime) Value

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Days To Next Purchase: Single Regression Model

15  days  

All  customers  

1day   7days   30  days  

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Days To Next Purchase: Single Regression Model, One Customer

15  days  1day   7days   30  days  

Single  customer  

6.8  days  +/-­‐  1  day  •  Uni-­‐modal  •  One  value  per  customer  

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Days To Next Purchase: Set of Binary Classification Models

15  days  1  day   7days   30  days  3  days  

15-­‐30  days  

7-­‐15  days  

3-­‐7  days  2-­‐3  days  

Single  customer  

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Days To Next Purchase: Set of Binary Classification Models

15  days  1  day   7days   30  days  3  days  

15-­‐30  days  7-­‐15  days  3-­‐7  days  2-­‐3  days  

Single  customer  

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Days To Next Purchase: Set of Binary Classification Models

15  days  1  day   7days   30  days  3  days  

15-­‐30  days  7-­‐15  days  3-­‐7  days  

2-­‐3  days  Single  customer  

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Website Mobile

App In-Store Call

Center 3rd Party

Models for Customer Intelligence How Many Do We Need?

B R A N D L E V E L

E N T E R P R I S E L E V E L

O T H E R B R A N D S

#3314

#9102

#2456

#5306

Droid

iOS

Mobile S

ite

Desktop S

ite

Am

azon

eBay

Web S

upport

Spring C

atalog

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What Data Best Predicts Behavior?

hDp://www.kaushik.net/avinash/compeHHve-­‐intelligence-­‐analysis-­‐tools-­‐metrics-­‐reports-­‐techniques/?utm_campaign=viralheat&utm_medium=social&utm_source=twiDer  

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Data Preparation: Feature Creation

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Data Preparation: Variable Reduction

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Data Preparation: Stratified Sampling (for speed only)

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Modeling—Lots of Preparation to Get to a Single Model

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A Quick Aside on Target Shuffling

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A Quick Aside on Target Shuffling: 500 runs

C U S T O M E R I N T E L L I G E N C E U N I T E S C R O S S - C H A N N E L M A R K E T I N G

Thank  You!!