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Gousto USE SNOWPLOW
Dejan Petelin
Head of Data Science
love
—Our journey of leveraging Snowplow Analy9cs …
•  An online recipe box service.
•  Customers come to our site, or use
our apps and select from 22 meals
each week.
•  The...
•  Transac@onal database and loads of
external data sources, e.g. Excel
spredsheets, 3rd party tools etc.
•  Mul@ple ad-ho...
Growing data capabili9es
Data Science
Analy9cs
DataEngineering
•  As a subscrip@on service we are
very retenDon focused – ...
Our data stack
Airflow (ETLs orchestra9on)
Trans DB
Data-warehouse (lake)
Daily email reportsAd-hoc analysesPredic9ve model...
Snowplow as unified log
Customer
Service
AcDvity
Log Service
Order
Service
Product
Service
Recipe
Service
. . .
AWS
Lambda
...
Snowplow on isomorphic JS
•  Shiny and super quick, but… what
happened to my events?!
•  No page loads – no automa@c page
...
Moving to the real-9me pipeline – use case
Snowplow
1
5
Store acDon taken
Churn
model
GiK
service
Process event
2Events st...
How we leverage Snowplow data?
From analy9cs to op9misa9on …
Raw
data
Standard
reports
Op9misa9on
Predic9ve
modelling
Generic
predic9ve
analy9csAd-hoc
re...
From analy9cs to op9misa9on …
Raw
data
Standard
reports
Op9misa9on
Predic9ve
modelling
Generic
predic9ve
analy9csAd-hoc
re...
From analy9cs to op9misa9on …
•  Daily trading reports, e.g. signups by
channel, conversion rate, orders etc.
Raw
data
Sta...
From analy9cs to op9misa9on …
Raw
data
Standard
reports
Op9misa9on
Predic9ve
modelling
Generic
predic9ve
analy9csAd-hoc
re...
From analy9cs to op9misa9on …
Raw
data
Standard
reports
Op9misa9on
Predic9ve
modelling
Generic
predic9ve
analy9csAd-hoc
re...
From analy9cs to op9misa9on …
Raw
data
Standard
reports
Op9misa9on
Predic9ve
modelling
Generic
predic9ve
analy9csAd-hoc
re...
From analy9cs to op9misa9on …
•  Daily trading reports, e.g. signups by
channel, conversion rate, orders etc.
•  Analy@cs
...
Churn predic9on – intro
•  As a subscrip@on service, we are very
reten9on focused.
•  Some customers are immediately
convi...
Churn predic9on – how we did it
Alice
Bob
Churn predic9on – how we did it
Alice
Bob
Churn predic9on – how we did it
Alice
Bob
Churn predic9on – how we did it
Alice
Bob
Churn predic9on – how we did it
Alice
Bob
Churn predic9on – how we did it
Alice
Bob
Churn predic9on – how we did it
Alice
Bob
Churn predic9on – how we did it
Alice
Bob
Churn predic9on – how we did it
Alice
Bob
Churn predic9on – how we did it
Alice
Bob
Churn predic9on – how we did it
Alice
Bob
Churn predic9on – how we did it
Alice
Bob
Churn predic9on – how we did it
Alice
Bob
Churn predic9on – how we did it
Alice
Bob
Churn predic9on – piTalls
•  What churn actually is? How to
define it?
•  It might be beber trying to predict
the likelihoo...
Churn predic9on – future
•  Predic@ng when the next event will
happen, rather then probability of an
event in the next X w...
Churn predic9on – results
•  Accuracy of the model is ~80%.
•  A bit too op@mis@c in the lower region
and a bit too pessim...
Automated menu design - intro
•  The food team used to manually
design menus – every week.
•  With 22 recipes this task ha...
Automated menu design – how it works (I)
•  We developed a very detailed
ontology to describe our recipes.
•  We built an ...
Automated menu design – how it works (II)
•  Mul@-objec@ve op@misa@on:
•  Maximising recipe diversity
•  Maximising menu p...
Concluding thoughts
•  Snowplow has helped us to scale our data capabili@es with limited data
engineering resources.
•  TI...
@GoustoTech
techbrunch.gousto.co.uk
dejan@gousto.co.uk
Thank you
Also… we’re recruiting
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How Gousto is moving to just-in-time personalization with Snowplow

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Presented at Snowplow London Meetup, 8 February 2017

Dejan Petelin, head of data science at Gousto, gave a presentation about their data journey, explaining how data reflects the customer’s voice and the importance of joining up all data sources. The goal is to delight and retain customers – critical for a subscription business like Gousto’s. Gousto is using Snowplow as a unified log, to scale up its data capabilities, listen to its customer and provide them with a more personalized experience. Finally, Gousto is moving to the real-time pipeline to enable just-in-time personalization.

Published in: Data & Analytics
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How Gousto is moving to just-in-time personalization with Snowplow

  1. 1. Gousto USE SNOWPLOW Dejan Petelin Head of Data Science love —Our journey of leveraging Snowplow Analy9cs …
  2. 2. •  An online recipe box service. •  Customers come to our site, or use our apps and select from 22 meals each week. •  They pick the meals they want to cook and say how many people they’re cooking for. •  We deliver all the ingredients they need in exact propor@ons with step-by-step recipe cards in 2-3 days. •  No planning, no supermarkets and no food waste – you just cook (and eat). •  We’re a rapidly growing business. About Gousto
  3. 3. •  Transac@onal database and loads of external data sources, e.g. Excel spredsheets, 3rd party tools etc. •  Mul@ple ad-hoc analyses, mostly in Excel, which are difficult to update. •  Gap between web analy@cs (GA) and transac@onal data. •  Lack of customer event logs •  we started snapsho@ng transac@onal database. •  Loads of ques@ons from our CEO Timo :) Our data journey… MySQL Transac@onal Read Replica Mailchimp Excel spreadsheets Google Analy@cs Zendesk CRM Geo-demographic data CRONed data processing Ad-hoc analyses MySQL Data Warehouse Excel reports Stakeholders
  4. 4. Growing data capabili9es Data Science Analy9cs DataEngineering •  As a subscrip@on service we are very retenDon focused – linking all the data sources is challenging. •  We believe that data is the voice of our customers, so we try to collect as much data as possible. •  Therefore we invested a lot in Snowplow as we own the data, which is very valuable asset and core of the business. •  The data is available to everyone – SQL is a great competency at Gousto.
  5. 5. Our data stack Airflow (ETLs orchestra9on) Trans DB Data-warehouse (lake) Daily email reportsAd-hoc analysesPredic9ve modelling WMS
  6. 6. Snowplow as unified log Customer Service AcDvity Log Service Order Service Product Service Recipe Service . . . AWS Lambda Amazon DynamoDB Platform Deployment Bucket SNS Subscribe to all messages Event API Amazon RedshiK AWS Lambda Subscribe to customer related messages
  7. 7. Snowplow on isomorphic JS •  Shiny and super quick, but… what happened to my events?! •  No page loads – no automa@c page views. •  We developed our custom framework for triggering events. •  We use structured events for that purpose, but store (unstructured) JSON objects in them. •  Such approach allows us to be flexible and quickly introduce new events. •  But, no data valida@on can lead to garbage leaking. •  Data modelling in Redshi[. Client Server App API
  8. 8. Moving to the real-9me pipeline – use case Snowplow 1 5 Store acDon taken Churn model GiK service Process event 2Events stream 4 If likely to churn 3 Store churn score •  Analyse customer behaviour in real- @me. •  Automa@cally react as soon as possible. •  Feed the response back to Snowplow (serving as a unified log). •  So the whole customer journey is available to CRM & reten@on teams instantly.
  9. 9. How we leverage Snowplow data?
  10. 10. From analy9cs to op9misa9on … Raw data Standard reports Op9misa9on Predic9ve modelling Generic predic9ve analy9csAd-hoc reports Source: Gartner Sense & Respond Predict & Act Complexity / Maturity Compe@@veadvantage
  11. 11. From analy9cs to op9misa9on … Raw data Standard reports Op9misa9on Predic9ve modelling Generic predic9ve analy9csAd-hoc reports Source: Gartner Sense & Respond Predict & Act Complexity / Maturity Compe@@veadvantage
  12. 12. From analy9cs to op9misa9on … •  Daily trading reports, e.g. signups by channel, conversion rate, orders etc. Raw data Standard reports Op9misa9on Predic9ve modelling Generic predic9ve analy9csAd-hoc reports Source: Gartner Sense & Respond Predict & Act Complexity / Maturity Compe@@veadvantage
  13. 13. From analy9cs to op9misa9on … Raw data Standard reports Op9misa9on Predic9ve modelling Generic predic9ve analy9csAd-hoc reports Source: Gartner Sense & Respond Predict & Act Complexity / Maturity Compe@@veadvantage •  Daily trading reports, e.g. signups by channel, conversion rate, orders etc. •  Analy@cs •  Customer behaviour •  Ac@onable insights
  14. 14. From analy9cs to op9misa9on … Raw data Standard reports Op9misa9on Predic9ve modelling Generic predic9ve analy9csAd-hoc reports Source: Gartner Sense & Respond Predict & Act Complexity / Maturity Compe@@veadvantage •  Daily trading reports, e.g. signups by channel, conversion rate, orders etc. •  Analy@cs •  Customer behaviour •  Ac@onable insights •  Customer segmenta@on •  Marke@ng abribu@on
  15. 15. From analy9cs to op9misa9on … Raw data Standard reports Op9misa9on Predic9ve modelling Generic predic9ve analy9csAd-hoc reports Source: Gartner Sense & Respond Predict & Act Complexity / Maturity Compe@@veadvantage •  Daily trading reports, e.g. signups by channel, conversion rate, orders etc. •  Analy@cs •  Customer behaviour •  Ac@onable insights •  Customer segmenta@on •  Marke@ng abribu@on •  Churn predic,on
  16. 16. From analy9cs to op9misa9on … •  Daily trading reports, e.g. signups by channel, conversion rate, orders etc. •  Analy@cs •  Customer behaviour •  Ac@onable insights •  Customer segmenta@on •  Marke@ng abribu@on •  Channel mix op@misa@on •  Churn predic,on •  Automated menu design •  Warehouse op@misa@on •  Tracking performance Raw data Standard reports Op9misa9on Predic9ve modelling Generic predic9ve analy9csAd-hoc reports Source: Gartner Sense & Respond Predict & Act Complexity / Maturity Compe@@veadvantage
  17. 17. Churn predic9on – intro •  As a subscrip@on service, we are very reten9on focused. •  Some customers are immediately convinced and become very loyal customers, while some customers need a bit more effort to get hooked. •  We use Snowplow events data to model customer behavior and find customers more likely to churn so we can focus on them. •  Use personalised approach to retain customers.
  18. 18. Churn predic9on – how we did it Alice Bob
  19. 19. Churn predic9on – how we did it Alice Bob
  20. 20. Churn predic9on – how we did it Alice Bob
  21. 21. Churn predic9on – how we did it Alice Bob
  22. 22. Churn predic9on – how we did it Alice Bob
  23. 23. Churn predic9on – how we did it Alice Bob
  24. 24. Churn predic9on – how we did it Alice Bob
  25. 25. Churn predic9on – how we did it Alice Bob
  26. 26. Churn predic9on – how we did it Alice Bob
  27. 27. Churn predic9on – how we did it Alice Bob
  28. 28. Churn predic9on – how we did it Alice Bob
  29. 29. Churn predic9on – how we did it Alice Bob
  30. 30. Churn predic9on – how we did it Alice Bob
  31. 31. Churn predic9on – how we did it Alice Bob
  32. 32. Churn predic9on – piTalls •  What churn actually is? How to define it? •  It might be beber trying to predict the likelihood of customer placing an order. •  How big should be a horizon? Where should we draw a line? •  Using events data, there is almost unlimited number of features – how to find really informa@ve ones? •  How do we keep model up to date if we are affec@ng customer journeys? •  How to measure success? •  No maber how accurate the model, the profit is what it counts at the end.
  33. 33. Churn predic9on – future •  Predic@ng when the next event will happen, rather then probability of an event in the next X weeks. •  Using recursive (deep) neural networks (RNN) to model events recursively, rather than engineering features.
  34. 34. Churn predic9on – results •  Accuracy of the model is ~80%. •  A bit too op@mis@c in the lower region and a bit too pessimis@c in higher region. •  Significant upli[ in the reten@on. •  Indeed, it depends on the ac@on taken. •  Loads of A/B tes@ng to find the right ac@ons to be taken. •  In the future, we want to build another model, sugges@ng what ac@on should be taken for each customer. •  Actually, why not build an autonomous system trying different approaches and communica@on channels to find the best approach? 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100% Actualpropor+on Predicted likelihood control varia9on A varia9on B
  35. 35. Automated menu design - intro •  The food team used to manually design menus – every week. •  With 22 recipes this task has become too demanding – diversity, mul@ple constraints, costs etc. •  They should be focusing on recipe development to keep delivering delicious recipes. •  Why not use machine learning to leverage the data to understand customers’ taste and design popular menus?
  36. 36. Automated menu design – how it works (I) •  We developed a very detailed ontology to describe our recipes. •  We built an internal Slack bot to collect data on recipe similarity. •  Insights gathered with that data enabled us to provide diverse menus. •  Understanding customers’ taste is a crucial part of designing popular menus. •  Transac@onal data (orders) is not enough – Snowplow data gives us way more insights on how customers explore menus.
  37. 37. Automated menu design – how it works (II) •  Mul@-objec@ve op@misa@on: •  Maximising recipe diversity •  Maximising menu popularity •  Balancing costs •  Matching forecasts •  Using Gene@c Algorithms (GA) •  Speed is not an issue as we have a whole week to generate new menu :) •  Mul@ple solu@ons so the food team can choose which menu best fit their objec@ves. Selec9on Cross-over Muta9on Evalua9on
  38. 38. Concluding thoughts •  Snowplow has helped us to scale our data capabili@es with limited data engineering resources. •  TIP TO STARTUPS: start building data capabili9es as early as possible – data is a huge asset. •  Snowplow also serves us as a unified log. •  Not necessarily limited to customer focused data. •  Snowplow enables us to ‘listen’ to our customers and provide them more personalised experience. •  Moving to the real-Dme pipeline to realise just-in-@me personalisa@on, e.g. personalised recipe ordering, add-on recommenda@ons (upselling) etc.
  39. 39. @GoustoTech techbrunch.gousto.co.uk dejan@gousto.co.uk Thank you Also… we’re recruiting

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