Gousto use and love Snowplow

Our journey of leveraging Snowplow Analytics.

Transcript

1. Gousto USE SNOWPLOW Dejan Petelin Head of Data Science love — Our journey of leveraging Snowplow Analy9cs …
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. •  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. Growing data capabili9es Data Science Analy9cs Data Engineering •  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. Our data stack Airflow (ETLs orchestra9on) Trans DB Data-warehouse (lake) Daily email reports Ad-hoc analyses Predic9ve modelling WMS
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. 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. Moving to the real-9me pipeline – use case Snowplow 1 5 Store acDon taken Churn model GiK service Process event 2 Events 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. How we leverage Snowplow data?
10. 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 aaribu@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 analy9cs Ad-hoc reports Source: Gartner Sense & Respond Predict & Act Complexity / Maturity Compe@@ve advantage
11. 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.
12. Churn predic9on – how we did it Alice Bob Iterate over historic events to generate the training dataset. input data (features) outcomes (targets)
13. Churn predic9on – piValls •  What churn actually is? How to define it? •  It might be beaer 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 maaer how accurate the model, the profit is what it counts at the end.
14. 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.
15. 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% Actual propor+on Predicted likelihood control varia9on A varia9on B
16. 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?
17. 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.
18. 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
19. 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.
20. @GoustoTech techbrunch.gousto.co.uk dejan@gousto.co.uk Thank you Also… we’re recruiting