Predictive Next Best Action for

Marketing Demand Generation and

Sales

Erik Bower & Josip Lazarevski (Palo Alto Networks)

3/11/17

Agenda

• Current state of marketing

• Omnichannel predictive next best action marketing concept

• The plumbing

• The model and how it was made

• Results so far

First up – A better name

Omnichannel real

time predictive

next best action

marketing

“Machine

Marketing”

Marketing nirvana

4

!

Right timeRight message Right person

Omnichannel

What are “channels”?

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Get right message? Get right timing? Target the right people?

Email A/B testing Test send times, schedule, some have automatic “throttling”

Segmentation 1-2-1 and audience based

Web Personalization A/B testing Depends on the prospect Segmentation 1-2-1 and audience based

Display/Programmatic Advanced optimization trained on conversion

Day parting, frequrency capping Segmentation 1-2-1 and audience based

How to reach nirvana?

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Example “Buyer’s Journey”

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Traditional Marketing

Automation

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• Logic is static

• Content gap, content age

• Coverage is spotty

• Customer journeys are infinite

▪ Dynamic and adaptive

▪ Greater coverage of micro-segments

▪ Predicts best content/offer

▪ Syncs and optimizes across channels

Machine Marketing

VS.

How would Machine Marketing work?

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First you need the messages or “offers”

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Then you need to be able to sync messages across channels

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Target

Campaign

AnalyticsPredictive

Audience Manager

Then deploy that message on the web….

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….on the display channel….

..and via email channel

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Figure out the best time to send an email

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July 2016 – December 2016

1 MONTH

TRUE

FALSE

Contacts we have sent at least 1 email between July 2016 and December 2016

Clicked on at least 1 email

Not clicked on any email

|_________________________________________________|________________________|

Modeling Period Performance Testing Period

Contacts we have sent at least 1 email on January 2017

Clicked on at least 1 email

Not clicked on any email

January 2017

6 MONTHS

DnB

Web Visits,

Form Fills

Account

Contact

Product

TRUE

FALSE

77%Accuracy

80%Accuracy

But wait there’s more

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Identity resolution

+

Behaviorial data

+

CRM data

+

Other data

A bit complicated to pull off

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How the model was built

Understand the audience and their preferences

Lookalike modeling using similarity search to understand user behavior

B2B: Understanding account/company behavior

Lookalike modeling using similarity search (similar to household in B2C)

Hybrid of boosted trees and ensemble trees

These are the people in your neighborhood

All that joined together

forms our neighborhoods

from where we extract

knowledge –

collaborative filtering

Organizing the messages with keyword mining

Humans got

tagging wrong

70% of the time

+ Keyword Clustering (KNN,SVM)

+ Term Grading

= Keyword – Topic Mapping

The formula

• We want to localize our recommendations but we don’t know their language

• Using blend of R script and KNIME we can recognize 32 languages

library("textcat");

xls<- knime.in

result.detectedLanguage<-textcat(xls$DMText)

knime.out <-data.frame(xls$"RECSentity.id",result.detectedLanguage);

Language recognition

Deploy to Adobe Marketing Cloud

Understand the content

of assets

Understand the audience

and their preferences

Asset to person matching

Results so far

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Homepage “King of the Hill” results

PredictedDefault

+1300%

Programmatic in Doubleclick results

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0.000%

0.200%

0.400%

0.600%

0.800%

1.000%

1.200%

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Click-Thru-Rate (CTR) By Audience

1st Party Cookies Display Standard Retargeting

Maestro Display

Standard Display CTR: .027%

Retargeting CTR: .080%

Maestro Display CTR: .351%

+338% Increase in Engagement

Headline

Button

Exit URL

Ad

Populates

on 3rd Party

Sites

Dynamically Populated

Early signs are positive

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If you are a marketing person, don’t worry……

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I’m not going

to take your

jobNo really