Presentation given at Cambridge Data Insights Meetup on 3rd November 2016 on use of Data Science at Metail in 2016.

1. A Year Of Data Science at Metail
Matt McDonnell - Data Scientist

2. Business Context
Startup: “A group of people operating in an environment of uncertainty
striving for a repeatable and scalable business model“

3. A scalable startup needs a Customer Factory
Figure adapted from ‘Scaling Lean’ by Ash Maurya https://leanstack.com/scaling-lean-book/

4. A look behind the curtain – what’s the data?
See Metail in action:
http://metail.myshopify.com?utm_source=DataInsightsNov2016
(Scary UTM code is there so I don’t have to spend the next week
digging into ‘Who are these mysterious visitors?’)
Live Demo Starts Here!
Sheepish explanation of why it’s not working starts here

5. The road to Data Science
• Understand the data
• Learn the tools
• Build the analytics for business intelligence
• More sophisticated data analysis for deeper understanding
• Apply machine learning techniques
• Develop models for prediction and decision making

6. My experience prior to Metail
Careers
• Physics Postdoc
Oxford, Griffith
• Technical Consultant
MathWorks
• Quant Developer
Fidelity Worldwide Investment
• Quant Analyst
Fidelity Worldwide Investment
Tools used:
(plus some Java, C#, Excel and VBA when I had to)
Understanding the data and tools

7. My experience since joining Metail
Lots of event stream data
Many AWS components
Outputs:
- Business Intelligence
- Bespoke Analysis
- Productionised Science

8. Tools to learn
Tools we used a year ago
• R for analysis and science
• dplyr, tidyr, ggplot
• Looker for some of the analysis
Tools we use now
• Python
• pandas, SQLAlchemy, boto3,
seaborn
• Still some R
• dplyr, tidyr, ggplot
• Looker for most of day to day
analysis
• Swagger
• AWS stack

9. Data Analytics
Business intelligence
• How well is the customer factory working? (KPIs)
• What about if we do this? (A/B Tests)
• How’s our retention? (Cohort analysis)
• How efficiently are we digitising garments? (Process monitoring)
• How are we growing?
To answer this we need …
LOTS AND LOTS OF SQL! (yay.)
Most of it embedded in Looker LookML (basically YAML) (yay - again.)

10. Data Analytics
Raw Events Engagement States Analytics Model
(Looker demo goes here if time allows)

11. Data Science
Exploring Digitised Garments

12. Event  Data
{
"schema": "iglu:com.snowplowanalytics.snowplow/unstruct_event/jsonschema/1-0-0",
"data": {
"schema": "",
"data": {
"name": "GarmentCoverage",
"data": {
"page": {
"garments": 24,
"garmentsWithCtas": 14,
"scrollPosY": 201,
"load": {
"isInitiator": false,
"elapsedTimeMs": 1424
}
},
"batch": {
"garments": 12,
"garmentsWithCtas": 7,
"ctas": [
{
"sku": "32536",
"x": 0.2721021611002,
"y": 1.6311844077961
},
{
"sku": "32544",
"x": 0.51768172888016,
"y": 1.6311844077961
},
{
"sku": "32545",
"x": 0.51768172888016,
"y": 1.0134932533733
},
{
"sku": "32548",
"x": 0.51768172888016,
"y": 0.39580209895052
},
{
"sku": "53282",
"x": 0.76326129666012,
"y": 0.39580209895052
},
{
"sku": "53337",
"x": 0.026522593320236,
"y": 1.0134932533733
},
{
"sku": "134499",
"x": 0.2721021611002,
"y": 0.39580209895052
}
]
}
}
}
}
}
GarmentCoverage event
"scrollPosY": 201,
"garmentsWithCtas": 7,
{
"sku": "32544",
"x": 0.51768172888016,
"y": 1.6311844077961
},

13. Spread of digitised garments
• Look at positions of all digitised garments for a given category.
• page is in units of #scrolls (based on browser height on the user’s device)
• Digitised garments on /women-dress and /women-tops-tees are more spread
out than garments on /women-jeans

14. Views by garment position
• Aggregate visitors who see garment ‘X’ in a given
category on a given date.
• Scale these visitor counts by the maximum #visitors for a
garment on that date in that category.
• In the /women-dress category:
• Digitised garments are spread between 0 and 120 page scrolls
with median ~40
• Long “tail” of digitised garments which get much fewer visits.
• The average digitised garment typically gets 20% of the visitors as
the most popular garment in that category (on a given day).
Date url_path sku Users Page scaled_count
2016-01-01 /women-
dress
101742 699 5.0 0.743617
2016-01-01 /women-
dress
101743 700 4.0 0.744681

15. Views by category
• Look at positions of all digitized garments for a given category.
• ‘page’ is in units of #scrolls (based on browser height on the user’s device)
• Digitised garments on /women-dress and /women-tops-tees are more spread out than digitised garments
on /women-jeans. Could also be that there are more digitised garments in /women-tops-tees.
• There are some “hotspots” of digitised garment positions e.g. ~page 100 for /women-tops-tees.
Unfortunately, they are quite far down the category page and visitor counts are typically around 10-20% of
the values for the most popular garments (closest to the top of the category page)
/women-tops-tees /women-jeans /women-dress

16. Views as time series
• Digitised garments on /women-dress over time
• The “hotspot” moves further down the page: most discernibly in the last 2 weeks.

17. Data Science
Exploring User Body Shapes

18. BMI Quantiles
BMI: 17.6
Height: 160cm
Weight: 45kg
BMI: 19.9
Height: 157cm
Weight: 49kg
BMI: 22.2
Height: 153cm
Weight: 52kg
BMI: 25.8
Height: 146cm
Weight: 55kg
BMI: 29.7
Height: 155cm
Weight: 71kg

19. Our Shape Segmentation
Spoon Triangle Bottom Hourglass Rectangle Hourglass Top Hourglass Inverted Triangle

20. Adapting the shape segmentation rules of the Lee et al. (2007) paper used by FFIT
Users Segmented by Shape
Hips – Waist (cm)
Bust–Waist(cm)

21. Shape Distribution and Popular Garments

22. Engagement by Shape
% of users trying on at least two garments on personalised MeModel
1SD

23. Data Science
Learning User Behaviour

24. Understanding Users
Event stream summary over a month
Visits by day of month
All users
Distinct types
Of users
Machine Learning Techniques

25. Data Driven User Segmentation
Distinct types
Of users
Use Machine Learning techniques to characterise which features define users in each cluster

26. Identify clusters: engaged and converted users
Cluster Labels into Redshift /
Looker
Acquisition
Rate
RPV
Seen Size
Advice Rate

27. Acquisition
Retention Reuse
Retention Revisit
Deep Funnel
Revenue
Revenue
674 users 595 users 541 users 721 users 312 users
Try-ons (any model)
A first look at the clusters

28. Future plans: more MODELLING!
Some possibilities:
• Use engagement clustering to create labels for supervised learning
• Engagement prediction using trained machine learning
• Apply Probabilistic Graphical Modelling techniques
• (I quite like Daphne Koller’s Coursera course and book
https://www.coursera.org/learn/probabilistic-graphical-models/home/welcome )
• More Bayesian reasoning
• … (any suggestions?)
Time permitting, SAMIAM (http://reasoning.cs.ucla.edu/samiam/) demo goes here

29. Bayesian inference – what are the variables?
(Disclaimer: this is me playing around with SAMIAM for 15 minutes and not an actual model)

30. Bayesian inference – how are things related?
(Disclaimer: this is me playing around with SAMIAM for 15 minutes and not an actual model)

31. Bayesian inference – what can we infer?
(Disclaimer: this is me playing around with SAMIAM for 15 minutes and not an actual model)

32. That’s all folks!
Questions?