lessons learnt from applying pydata to getyourguide marketing

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1. Lessons learned from applying PyData to our marketing organization PyData + Marketing = ??? Jose Luis Lopez Pino @jllopezpino 2. First some bragging! - We have grown x3 our marketing efforts. - We have reduced ~90% of the time spent on creating a new ad. - Launched in 7 new markets. - We havent grown the team. - No expensive marketing software. 3. Two advices before starting... You wont go far without domain expertise 4. Two advices before starting... Marketing is a fast-paced competition 5. What is GYG? 6. Marketing Technology Create, control and target online ads 7. Create - Ads that are relevant to the user. - Within the limitations of the advertising service. - Send them to the best landing page. 8. Control How much should we bid? How much we expect to receive from this ad? Ranking factors? How good are we doing? How much room for improvement do we have? 9. Target - Keywords, keywords, keywords! - Remarketing lists. - Reach relevant audiences. - Language. 10. Three steps - Set up the infrastructure and tools - Data retrieval and storage - Analyse, automate and train models! 11. Infrastructure and tools - Dedicate server? - Schedule requests? - Queue requests? - Multiple nodes? - Storage? Databases? Structured? - Talk to other systems? - Do you need an interface? - How to deploy? 12. Data retrieval and storage - Data modeling? - Alembic (database migration tool) to make changes in your DB and keep track of them. - API migrations and sunsets: write better code! - Rely on third-party systems? - Optimize for speed? 13. Data retrieval and storage - Scrape without being banned? - Extract data from other systems of the organisation. - Users also input data. 14. Analyse and automate When to use SQL? When to use pandas? Complex pivot tables with SQL? Load all my keyword space again and again in pandas? Schedule queries to provide data for spreadsheets every day. Allow marketers to make changes that they cant do with any other interface. 15. Some examples - What are the products that people show interest at this time of the year? - Customer segmentation. What are the best attributes to segment them? - How do I estimate the potential size of a market that I dont know? - Monitor important ranking changes. - What are my competitors doing? 16. Some examples - What are the outliers in our accounts that need human attention? - What are the most important keywords for a particular page? - What are the products that I need in my marketplace? 17. Some ML examples - Are we going to sell out this product? - Sentiment analysis on customer reviews. - Regression model of our ROAS. - How to cluster our adgroups to make decisions on them? 18. And we are still learning 19. Data scientists 13.03.2015 GetYourGuide AG 21 statistics forecasting and estimation pandas, scipy, R 20. 13.03.2015 GetYourGuide AG 22 Engineers databases & data mining machine learning python visitor tracking & metrics 21. How to measure success? 22. Takeaways from this talk - Marketing is not the latest buzzword, but its fun to do for data-driven people. - Technology can have an enormous impact on the marketing results. - And the PyData stack provides tools to do it. 23. Thats all! Jose Luis Lopez Pino @jllopezpino