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.

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.

5. https://www.hotjar.com

6. Worst case scenario.

7. Worst case scenario.

8. Google Analytics
implementation.

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

18. Enhanced eCommerce tracking.

19. Enhanced eCommerce tracking

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

31. Digital advertising &
re-targeting.

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.

35. Google Analytics –
Intelligence.
35

36. Intelligence

37. Analytics Intelligence: https://support.google.com/analytics/answer/7411707?hl=en&ref_topic=7346206&authuser=0

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

42. https://www.ascend.ai

43. http://www.leiki.com

44. https://frosmo.com

45. https://www.elementsofai.com

46. Contact information
Petri Mertanen
petri.mertanen@gmail.com
Mobile: +358 400 792 616