Data Mining with Sitecore xDB

Celebrate Your Inner Geek with Sitecore xDBPresented by:Amanda Shiga Courtney Brownell

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Who is Rotman?


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Who is Rotman

John Hull“wrote the book on derivatives.”

Richard FloridaBest-selling author/Top50 Thinker

Anita McGahanHead of the Academy of Management


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Who is Rotman?

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A full service digital agency.

Specializing in the multi-billion dollar markets of content management & digital marketing

TORONTO | OTTAWA | NEW YORK | LONDON | SAO PAULO | FLORIANOPOLIS

Over 200 Sitecore deployments

From basic sites to complex

enterprise engagements across a

range of vertical markets.

40+ trained, experienced

Sitecore developers

Ready to meet and exceed

your all Sitecore needs.

Full services teams

on three continents

With offices in Canada,

the United States, the UK,

and Brazil.


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Nonlinear + Rotman: Since 2011


Rules-Based Personalization Example



Rotman – Behaviour Varies by Visit

10.11%

8.65%

6.94%

4.03%

New Visitors

Second time visitors

Third time visitors

Fourth+ visit

Home page visitors going to fees and expenses - MBA - by

visit number

4.42%

5.47%

5.67%

6.54%

New Visitors


Third time visitors

Fourth+ visit

Application deadlines from MBA Fulltime page

13.49%

11.09%

10.53%

7.16%

New Visitors


Third time visitors

Fourth+ visit

Admissions criteria from MBA Fulltime page


Personalization by Visit Number (V = 1)

Can I get

in and

how much

does it

cost?


Personalization by Visit Number (V=2)

Why

should I

choose

Rotman?


Rotman Geographic Personalization Results


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• With Sitecore 8, we have access to rich, granular interaction data

• How can Sitecore Analytics and the xDB further inform our web optimization strategy?

• Also – data science is cool.

• What can we explore and learn?

Exploring a new phase of website optimization


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Quick refresher on xDB

• Collects all customer interactions

• Consists of a few different pieces

• Our focus: the raw data collection


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Business Context – 2016

We had this notion that our audience was compelled by ideas

Sitecore Analytics and xDB let us test it!


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We wanted to answer these 3 questions:

Business context

We tend to look at our audience in binary terms Finance vs Creative types, the Poets and Quants — how is this characterization playing out?


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Business context

We assume that thought leadership plays an important role in our students deciding to attend Rotman— is this true?


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“What drew me to Rotman was its world-class faculty & reputation as the Top MBA in Canada.”-Lakshmi Ra, FTMBA ‘15

Business Context


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Andrew’s finest moments were when he felt himself transformed by an idea.

“I wish each interaction at Rotman could inspire you with a rush of new ideas.”-Andrew Peters, Morning MBA Student ’10,Management Consultant, 15 years of work experience

Business Context


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“You get set in your ways. Being here really challenged me to do things differently.”-Executive MBA focus group participant

Business Context


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Business context

How do you take an academic research idea and make it broadly relevant?


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Organizational Need


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Dean’s Vision

• Research Excellence

• Globally Engaged Alumni

• Excellent Student Experience

Organizational Need


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Big image slide with beaker >> Entering the data science lab…

Entering the data science lab


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• The perfect ingredients for taking a small step

• An organizational culture open to experimentation and plenty of curiosity

• A base level of organizational analytics maturity

• Interesting hypotheses about audience behaviour and drivers

• Limited time, data, resources and processing power

• Fantastic data in Sitecore Analytics & xDB!

Shaping our experiment roadmap


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• 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?

• Data Mining for the Masses by Matthew North – applied to business scenarios

• RapidMiner Lead Scoring pilot – a 2-month pilot using Salesforce, Pardot

Opportunities for marketing teams

http://docs.rapidminer.com/downloads/DataMiningForTheMasses.pdf

https://rapidminer.com/resource/using-data-science-identify-qualified-leads/


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Our process


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Courtney B. MarComms / Web / DesignSitecore Power User

YuanYuan H.PhD, Applied MathData scientist

Amanda S.Comp Sci / MarTechSitecore MVP

Our team

L to R


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Our Data & Engagement Model

Interactions = Value

Tip:Make sure your goals are measureable by a pageview!


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Content Profiling

Think about your audience.Who are they? How do you segment them?


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Content Profiling


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Content Profiling

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


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Content Profiling Example


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Content Profiling Example


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Our tools and infrastructure - keeping costs low


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Step 1: What can Sitecore Analytics tell us?


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Sitecore Analytics – comparing engagement


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Step 2: Where do we get the data?

• Experience Extractor vs. Reporting API vs. Mongo


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• Time was spent translating the xDB into a dataset that could best answer our business questions and was easily consumable by our models

Step 2: Making the xDB as consumable as possible


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• The “fun art” of feature engineering: thoughtful creation of new attributesbased on domain knowledge and end business goal

• Extract as many helpful columns as possible from your raw data

• Keep in mind what’s accessible in Sitecore Rules Engine – you’ll want to make use of your strong predictors!

Step 2: Making the xDB as consumable as possible


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• 2 months of data = ~200,000 interaction records

• Approx. 60-90 minutes to run processing batches

• Ended up with 47 attributes (columns)

Step 2: Final ExampleSet


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We ran binominal classification to predict 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

Step 3: Running a supervised ML classification


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Supervised machine learning

The model learns… And then we test how good it is!


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Supervised machine learning


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1) Conversion is predicted by:

1) Visit to Research area of the website

2) Matched to Thought Leader pattern

3) Matched to Full-Time MBA Prospect pattern

4) Visit to “Ideas You Can Use” area of the website

* Model performance accuracy 88%

Step 4: Results and interpretation


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1) High engagement is predicted by:

1) Matched to Full-Time MBA Prospect pattern

2) Matched to Thought Leader pattern

3) Visit to Academic/Faculty area of website

4) Visit to Research Areas of website

* Model performance accuracy 85%

Step 4: Results and interpretation


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Let’s inform marketing strategy for Homepage refresh initiative

1) Push the highly engaged Creatives towards a higher value conversion like Apply Now

2) Lift the engagement of Quants 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

The power of the results


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Tie back to the Rules Engine


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Second application: A predictor model for conversion that gets stronger with more training

We can ultimately start a lead scoring feedback loop at the xDB Contact level – likely to convert?

The power of the results

Retrain model

with new data

Auto scoring of customer records

Update Contact with lead

score


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1) More feature engineering to drive wider set of predictors

1) Pull out more behaviour data from pages visited

2) Add GeoIP data

2) Audience clustering

1) Deeper insight into finance vs. creativity theory

2) Refine understanding of thought leadership consumption

Step 5: What’s next? (Short term)


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• Analyzing behaviour patterns over a years’ time • Seasonal variations in the higher ed space

• Adding in Customer Data • Looking at data governance and privacy

• Considering Student Data to append: events, enrollment, courses, profs, etc.

• Folding back into Marketing Operations • For lead scoring and clustering, retraining models regularly and feeding back into

the xDB at the Contact level

• Making the information available in the Rules Engine and other marketing platforms

Step 6: Long term possibilities


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More data: CRM Connect / Data Exchange Framework

CRMSocial

Media

Customer

SupportWebsite

POS AppsxDB

IoT


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• Incorporate data enrichment early in your IA and content strategy

• Quick Wins for Richer Sitecore xDB

• If you’ve got a solid measurement program in place, mining the xDB for repeatable insight is worth it (for your team skillset too)

• Be confident in your domain knowledge and the support of a data scientist; find pockets of enthusiasm in your team

• Treat your first foray as a low-pressure pilot project, and allow for ~3-6 weeks of data shaping and feature engineering

• If you’re extending xDB or xConnect, keep prediction and clustering in mind for your data model

Key takeaways and lessons learned

http://www.nonlinearcreations.com/Digital/how-we-think/articles/2016/01/Quick-wins-for-richer-Sitecore-Experience-Database.aspx

Next steps/Resources

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What to do• Apply a quick data enrichment strategy

• Boldly enter the world of data science

• Give us a call! (And see back of room )

Resources available• Slide deck / recorded presentation

• Data enrichment blog post

• Upcoming article series


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FOR DISCUSSION PURPOSES ONLY. SitecoreConfidential and Proprietary. © 2016 SitecoreCorporation A/S. All rights reserved. Sitecore®

and Own the Experience® are registered trademarks of Sitecore Corporation A/S. All

other brand and product names are the property of their respective owners.

Data & Analytics

Data Mining with Sitecore xDB