ML & Prediction with xDB

DATA MINING,
PREDICTION AND
MACHINE LEARNING
WITHTHE XDB
Amanda Shiga
May 18, 2017
#sugcon
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© 2017 SitecoreUser Group Conference Europe and its respective speakers. All rightsreserved. 2
Once upon a time at Nonlinear Toronto…
Didn’t hit your
target sprint
velocity?
The opportunity we have with data, machine
learning and xDB!

Utopia?
Business value of
applying data science to
digital marketing
XP optimization programs
Our goals and philosophy
Self-optimizing
digital channel?

The behavioural system of record

Why do we care?

• Machine learning produces models that can make decisions
and predict outcomes
• The more we can know and predict about behaviour, the better
we can target audience segments and send the “right message
at the right time”.
• Ultimately we seek to reframe expert human judgement into
prediction
• Practically, we can optimize on a lever to win; improve delivery,
availability, accuracy, convenience, engagement or cost
Why do we care?

• On the creative side, automate image repository tagging with
facial and object recognition
• On the optimization side, automate testing and segmentation
Simple digital marketing examples

Ingredient #1: A great client and…

Ingredient #1: Great business questions

• We want to collect the most valuable information possible,
within the constraints that we have
• The data privacy question
• We started with only anonymous, public data
• We got explicit, written permission when we needed to share the data
Ingredient #2: Data collection and enrichment

Sitecore Engagement Model

Content profiling – power of behavioural segments

Additional personas

Content profiling
The pages we tagged were
given a value from 1-10 in each
of the segments.

Tagging – example 1

Tagging – example 2

Value of Sitecore Analytics

Digging deeper

• Lead scoring - understanding the factors that predict
conversion
• Can this help us test our theories on poets/quants and thought
leadership as a driving force of engagement?
• Audience segmentation - clustering into groups showing
similar behaviour
• Can this help us understand better how poets and quants behave, or
whether there are other behavioural clusters we may not be aware of?
Ingredient #3: Mapping the spaces to biz questions

Opportunities playbook

Ingredient #4: A solid, cost-effective toolset

[Sidenote]

Experience Extractor vs. ReportingAPI vs. Mongo

Ingredient #5: Smart and insightful feature engineering
• The “fun art” of feature engineering: thoughtful creation of new
attributes based on domain knowledge and end business goal
• Translate into columns easily consumable by prediction models
• The more you can feed the model, the more it will give back!

Let’s get creative
Generate as
many helpful
columns as
possible
Each one could
be a strong
predictor or
cluster
characteristic
BONUS: An
algorithm can
remove columns
that don’t add
any value

Back to the xDB: from ~24 attributes to 47
Profile key
threshold
Visited
other
areas of
website
Country /
Region
Page view
sequence
CRM /
Application
status

We predicted two attributes:
1)hasConversion – indicating whether they converted on any of
the 12 goals on the website
2) highValue – indicating whether the session achieved an
engagement value higher than 75 points
Ingredient #6: Supervised Machine Learning

Binominal classification using GLM operator

Predictive model

Unsupervised machine learning: what don’t we know?
The biggest (or highest prioritized by HiPPO) marketing-
defined segment may not be the most profitable or valuable

Switch toVM demo

1) Conversion is predicted by:
1) Visit to Research area of the website
2) Matched toThought Leader pattern
3) Matched to Full-Time MBA Prospect pattern
4) Visit to “IdeasYou Can Use” area of the website
* Model performance accuracy 88%
Results and interpretation

1)High engagement is predicted by:
1) Matched to Full-Time MBA Prospect pattern
2) Matched toThought Leader pattern
3) Visit to Academic/Faculty area of website
4) Visit to ResearchAreas of website
* Model performance accuracy 85%
Results and interpretation

1) Push highly engagedCreatives towards high value conversion like
Apply Now
2) Lift the engagement ofQuants by presenting very targeted financial
thought leadership
3) Bring forward academic research, “ideas you can use” and professor
bios more prominently on high traffic areas
4) Evangelize internally… already working!
Using the results

Ingredient #7: Operationalization
What is the most useful way the model can be used in the day to
day operations of a marketer?

Ingredient #7: Operationalization
Algorithmic trading

• As standalone input to digital strategy
• New audience clusters to target and investigate
• Data-backed justification for tactic prioritization
• Incorporated into broader data warehouse
• As an input to customer data for segmentation
• Quasi-realtime updates back to CRM and/or Contact database
• Rapidminer, for example, can expose processes as web services
• Accessible via the Rules Engine
• Personalization based on model prediction
• Latency/performance risks
Most accessible avenues

Burning questions: What skillsets were needed?

Burning question: how long did it take?

We continue to see good lift in value per visit, with a focus placed
on Finance programs via ad campaigns, events and targeted
content
Continuing lift in engagement

Additional persona data collecting

Unsupervised machine learning

CRM
Social
Media
Customer
Support
Website
POS AppsxDB
IoT

Top five tips to consider
Adjacent
experimental
spike
Cost
effective
tools
Start with
non-PII and
written
permission
Internal
beachhead;
magic words!
Automate
human
judgement
into prediction Take the plunge!

• Data Mining for the Masses by Matthew North – applied to business scenarios
• RapidMiner Lead Scoring pilot – a 2-month pilot using Salesforce, Pardot
• Georgian Partners –The 11 Principles of Applied Analytics
• Georgian Partners –Applied Artificial Intelligence: Trends and Principles
Helpful resources
“Technology is a resource-liberating force. It is the catalyst that
takes what used to be scarce and makes it abundant.”
“If you want to do any innovation, anything new, you’re going to
have failures because you need to experiment.”
- Peter Diamandis, Abundance360

ML & Prediction with xDB

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