Case studies w/Analytics, Real Time, DM/ML in a hackathon

L’Oreal 8/27/2013Not Hadoop

Agenda

• Problem Statement: – Digital and Retail behavior analysis:

• Long tail problem similarities

– Propensity Marketing: • Propensity for consumer to respond to promotion?• Cover DM/ML Demographics presentation

– Profitability Marketing• Who are the most profitable customers?• Obvious answer, select * from customers join orders order by amt

desc;

– Promotion Modeling• What drives order values and who should receive promotions?

What do I do

• Work, Tech lead Google, ~10y, Architect Absolute SW

• Teach, mentor others on Big Data, Hadoop, DM/ML

• http://www.meetup.com/HandsOnProgrammingEvents/.

Review

• Theory: – What is long tail? – Long tail success case studies– Demographic targeting/Modeling and prediction– ML/DM success case studies

• Data Analysis Strategies/Structure

What is the Long Tail?

• Originated from search engines/Google• Don’t focus on the top 20% queries, focus on

the bottom 50% first• Why? The bottom 50% was the hardest:

LP&SB. The top 20% was automatic

Long Tail Example, keywords

Keyword Lift/Complementary Strategies

• 70% of the keywords are not used frequently.• Page Rank/feature selection/Spam reduction– Most data (demographics is inaccurate, eBay problem)

• Quality of features enable ML/DM modeling – Identify these words first using simple SQL queries then

run a model and use A/B testing to iterate to better results

– Example of ML/DM later• Case study of data visualisation for search query

length

Complete solution not possible

• A complete solution to the long tail is not possible via a hackathon

• Examples of Complete Solutions– Example: Symantec uses modified page rank to see if

virus files are safe/not safe. Viruses are different, all are unique. You can’t rely on past examples. >90% accuracy rate. Uses people feedback.

– Example: Yahoo content system matching users to content ~100 attributes->1k attributes. Most users only go to Yahoo news for a few stories. MM guides this

Another long tail on search query length

Long Tail

• Obvious longer queries imply user wants more precise result. Precision vs. Recall

• Obvious these users are more valuable b/c the directed intent is more focused. Showing the user enter in queries with more precision is very very valuable for shopping and other applications with focused directed intent

• The above case results in a $50.00 click to Google for Salesforce/SAP ads (e.g home financing/mortgages)

• Best way to see this is in a demo: Move mouse on dots which are close to each other:

http://dataincolour.com:8888/#1144645000 DEMO!!!!!

Example real time applied to previous example

We looked at search keywords and search phrase length. Visualizations as a substitute for Machine Learning algorithms. Much faster to implement

Some students <~20 years old did this in a weekend hackathon: http://www.dataincolour.com/2011/06/curiousnakes-visualization-of-aol-questions/

http://datainsightsf.com/schedule-2/ Not repeated

What to do?

• Brainstorm some more, definitely something here, play w/data; will come in time. The most important part is the definition of the problem, not the code– Think more code less

• Should you copy the data visualisation example on Search Query Length?– Probably not

• A long long time ago Google displayed the incoming search queries in the lobby; this had practical use

• Real time constrain the problem, less complicated processing, less about the algorithm, more about the user

Why Real Time? Long Tail Do I really need real time? Yes, why? Pre2010 Google search displayed all the results, a combination

of precision and recall. Post 2010 Google went to instant search, limited recall. Nobody

drilled down to the 1Mth page for DVDs. Better ads results with real time

Analytics today is similar to pre2010 Google search, batch processing using click logs

Real time analytics mostly custom solutions but can be much more effective. Once user leaves the website too late to do anything. Many orders of magnitude difference. Precision >> Recall

UI:mouse over a stream of dots

Mouse on a dot which is part of a group which looks like a snake

Can see what user typed in as queries after another, here is one example;

How to fix car-> What is a fuel filter-> How to replace a fuel filter.

This is valuable in adding additional features to the user who asked this

Can't get this from SQL queries easily or at all.

What is the lesson here?

• Viewing data in real time has value• Minimum it helps clear the thinking for the

next step• Use as an alerting system/QC process to show

if ML/DM is running correctly (proprietary in Google/Yahoo). Every business has these.

• Key: visible to everybody w/o running a SQL query

Wisdom gained matches across 2 hackathons

• One of the most surprising pieces of work was a unique data visualization from the DM hackathon

• None of these positive results were defined in the problem statement. Required creativity.

• Careful

Review ML/DM

• Review a small subset of these slides:– http://www.slideshare.net/DougChang1/demogra

phics-andweblogtargeting-10757778• Agenda: review a case study of the Motley

Fool and how to create/target promotions to likely subscribers for problem #2, propensity marketing

• Case study of a past hackathon. – My role: I seed the ideas, Mike Bowles, Nick

Kolegraff

ML/DM Slides

• DO NOT INSERT SLIDES, cover the original so we don’t limit the scope of audience questions

ML/DM and Hackathons

• Done 2 as examples, – Motley Fool, cosponsored by Kaggle (Mike Bowles)– Best Buy, paid Kaggle (Nick Kolegraff@Accenture/DM SIG, we

sought him out)– These events require guidance/very successful, both still are

receptive to more DM/ML events • Careful: an algorithm doesn’t mean you have a

production process or something someone can manage via a paid analyst headcount

• Why aren’t there more? Time investment to clean data, tech talk to guide participants, min 3 months work

What do I do for others which may help you?

• Seed the ideas; should add a structure to this. NDA. Run SQL queries

• Current Case Study– Starting to do the prep work for another real time analytics example,

teaching from this– Nick/Mike did this for the other 2 hackathons.

• Match the strategy w/structure– Take time off work to build an engineering prototype (Twitter Storm

in old slide deck)– Not covering this here– Strategy: first display the data in a real time dashboard then iterate

the visualizations, then add DM/ML algorithms after the A/B testing framework is complete

One example, real time analytics, web page heat maps

Amazon Web Page

Google Shopping Example/Reversed/Why?

Upper Left hand corner

Example of Kiehls

Kiehl’s Example

• Put in offers w/($ amount, product desc, click url) customized per user, A/B test layouts and placement, store data for customization and measure lift

• Measure facebook ads via page rank• Predict missing links application• http://blog.echen.me/2012/07/31/edge-prediction-in-a-so

cial-graph-my-solution-to-facebooks-user-recommendation-contest-on-kaggle/

• Careful, don’t copy. Example only. Generalize to hackathon. Many other ideas

• Your answer is different from Yahoo & Google. This isn’t a roadmap.

Promotion Modeling

• Is this a long tail problem? – How to formulate the graph and influence across

nodes? – Which features to select to use for modeling?– Still ok if you don’t have the long tail answer.

Follow the Demographics Customer modeling ex. • How to change the model over time? • Metrics for promotion effectiveness

– Facebook campaigns are easy to iterate and run. Still need some form of A/B testing

Structure has to match Strategy

• Partner w/Macy’s? Develop a structure to work with retail partners to increase their sales– E.g. customized shopkick– Don’t just release APIs, release mobile app source

code ppl can modify• Test promotions and building profiles? • … lots of ideas