Digital Analytics and Machine Learning tips for Sales Optimization
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Digital Analytics and Machine Learning tips for Sales Optimization, presented at ProductTank event in Helsinki, September 6, 2018. By Petri Mertanen, Director of Digital Analytics at Annalect, Omnicom Media Group Finland.
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Digital Analytics and Machine Learning tips for Sales Optimization
- 1. Digital Analytics and Machine Learning tips for Sales Optimization #ProductTank Helsinki | September 6, 2018 @mertanen Slideshare.net/mertanen
- 2. Digital Analytics infrastructure Digital Analytics Platform
- 3. Digital Analytics Platform Google Data Studio Google Optimize Attribution Audience Center APIs GTM Big Query
- 4. Some tools to consider Web Analytics: • Google Analytics • Adobe Analytics • IBM (Coremetrics) • Kissmetrics • Mixpanel • Siteimprove • Webtrends • Snoobi • Piwik • Etc. Tag Management: • Google Tag Manager • Adobe Dynamic Tag Management • IBM DDX • Ensighten • Tealium • Segment • Tag Commander • Signal • Etc. CRO / User experience: • Google Optimize • Adobe Test & Target • Visual Website Optimizer • Optimizely • Unbounce • Clicktale • CrazyEgg • ThunderHead • Hotjar • Etc. Dashboard: • Google Data Studio • Microsoft Power BI • IBM Cognos • SAP • SAS • Tableau • Qlik • Klipfolio • Geckoboard • Etc.
- 9. Analytics is easy to start Just place the snippet in website and ”enjoy” multiple reports
- 11. You have visits – so what? What Google Analytics doesn’t track with the basic setup: • Outbound links • Sharing features • Email, chat or phone number clicks • Ecommerce process, transactions or revenue • Goals & goal funnels • Custom dimensions or metrics • File downloads, forms, videos • UserID, sessionID • Internal search GOOGLE TAG MANAGER HAS AUTOMATIC LISTENERS AND OTHER COOL THINGS. WE CAN USE THEM TO SAVE THE DATA NEEDED EASILY TO GOOGLE ANALYTICS
- 13. Get 20 days trial period and -10 % off from your subscription with this link: https://www.leadfeeder.com/?rc=mertanen or partner code: mertanen
- 14. Admin side
- 15. Define goals
- 17. We’ll define what data we need & implement Google Analytics accordingly We’ll see what kind of data we get from Google Analytics by default Google Analytics implementation
- 21. Google Analytics as a data platform. 21
- 22. Universal Analytics is more open data platform How Google Analytics works: https://www.youtube.com/watch?v=eyltEFyZ678
- 23. New Cross Device reports Learn more: https://support.google.com/analytics/answer/7532985
- 24. Combine sales data to get holistic view on customers About userID: https://support.google.com/analytics/answer/3123662
- 25. Segments.
- 26. Segments.
- 27. Segments.
- 28. Segments.
- 29. Segment like hell Picture credits to: http://gabrielajohnsons.blogspot.com Use segments: • Products • Product category • Traffic source / medium • Landing pages • Device type • Gender • Age • And combinations, such as: product / category and traffic source
- 30. Segments. Massage revenue by source / medium
- 32. Tarkista ettei löydy ”hassuja” Gmail-käyttäjiä Aktivoi user-metrics ja demografinen data Aktivoi mainonta ja re- marketing ominaisuudet. Sulje pois tietyt viittaavat liikenteen läheet Google-työkalujen linkitys Analyticsin kanssa Define re-marketing audiences for Google Ads (search and display network)Datan tuonnin määrittely Google Analyticsiin Näkymien asetukset: aikavyöhyke, valuutta, sisäinen haku, bottiliikenne Tavoitteiden määrittely Suodattimien määrittely Verkkokauppaseurannan asetukset Henkilökohtaiset työkalut ja asetukset sekä niiden jakaminen muille
- 33. Bring Google Analytics goals to Google Ads in order to use automatic optimization towards conversion.
- 34. Conversion tags for other digital advertising systems.
- 36. Intelligence
- 38. AI and Machine Learning.
- 39. Product development: Marketing comms: Automatic content creation Guiding and enhancing the product development Analysing customer feedback Market research In (digital or IoT) product web or mobile application itself Finding customer segments / clusters based on behaviour Automatic advertising optimisation Automatic data analysis, anomaly detection. Attribution modelling, for example Markov model 39 Possibilities of AI and Machine Learning Automatic conversion optimisation
- 40. Additional products related People who saw this product watched also these products Product Manager’s recommendations
- 41. Products related to your shopping cart
- 46. Contact information Petri Mertanen petri.mertanen@gmail.com Mobile: +358 400 792 616