Prezentacja miała miejsce podczas Blueffect 2013 - II Ogólnopolskiego Kongresu Efektywności, który miał miejsce 26 czerwca w Warszawie. Organizatorem wydarzenia, w całości poświęconego wykorzystaniu analityki internetowej w biznesie, była agencja Bluerank. Więcej informacji: www.blueffect.pl Skuteczna segmentacja klientów w Google Analytics – przykłady i najlepsze praktyki dokładnej analizy danych oraz zasady zaawansowanej segmentacji w celu zwiększenia przychodów firmy. Pavel Jasek / Head of Analytics, Dobry Web Co sprawia, że źle oceniasz kampanie marketingowe? Czy wiesz co jest rzeczywistym współczynnikiem konwersji Twojej strony internetowej? Jak należy ustalać priorytety sekcji zawartości strony w obszarze e-commerce? Które segmenty klientów są najbardziej wartościowe?

1. Effective Customer
Segmentation
Blueffect: II Ogólnopolski Kongres Efektywności
Warsaw, July 26, 2013
Pavel Jašek

2. Agenda
1. How can a customer segmentation change
your usage of web analytics data
2. Why your marketing campaigns are evaluated
wrong
3. Customer cohorts
4. Actionable content segmentation and
prioritization

3. Why to Segment Your Customers
● Understanding the behaviour of different
customer segments on your website
● Evaluating acquisition and retention campaigns
properly

4. Important progress in understanding
website traffic
1
2

5. Customer Segmentation Options

6. Most Important Customer Segmentations (1/3)
● Customer Type
• Anonymous
• Registered users
• Customer with purchase history

7. Most Important Customer Segmentations (2/3)
● Customer Lifetime Status
• New Customer
• Frequent Customer
• VIP Customer
• Nonactive Customer

8. Most Important Customer Segmentations (3/3)
● Value segments for frequent customers
• <1 000 złoty in last 3 months purchases
• >1 000 złoty in last 3 months purchases
• > 10 000 złoty in last 3 months purchases

9. 3 Ways to Gather Data for Segmentation
1. Implementing Custom Variables
• _gaq.push(['_setCustomVar', 5, 'customer', 'VIP', 1]);
• _gaq.push(['_setCustomVar', 4, 'ID', '123', 1]);
• For GA: visit-level or visitor-level scope
• Similarly in Piwik, Adobe, Webtrekk and Webtrends
2. Backward integration to customer ID
• Universal Analytics: Dimension Widening
3. Without any implementation
• Ad-hoc segments based on pageview /login.html

10. Recommendation #1
Start with a simple segmentation of
logged-in visitors. Your view on web
analytics data will change forever
since than.
Pavel Jašek

11. Good Sources to Read
● Google Analytics Custom Variables
• https://developers.google.com/analytics/devguides/
collection/gajs/gaTrackingCustomVariables
● Ideas from Lunametrics
• http://www.lunametrics.com/blog/2012/08/28/20-
ways-use-custom-variables/

12. Demystifying the conversion rate

13. Demystifying the Conversion Rate
● CR = Conversions/Total Visits
● What if not all visits come to your website in
order to convert?

14. 70-90 % of bank homepage visits
come only to login to e-banking

15. Prospects Instead of Visits
● CRp = Conversions/Prospects
● CRp = Conversions/(Total Visits – Visits of
current customers)

16. Using Visits Using Prospects
• 100 total visits
• 2 new registrations
• CR = 2/100 = 2 %
• 100 total visits
• 2 new registrations
• 30 visits from customers
• CR = 2/(100-30) = 2,9 %

17. Your registered
users also come
via organic and
paid traffic
sources. This
impacts
campaign
evaluation.

18. Recommendation #2
Calculate conversion rates from
potentional customers only.
Pavel Jašek

19. Customer Cohorts

20. Understanding customer activation and
retention using cohorts based on registration
date
0
100
200
300
400
500
600
700
1 8 15 22 29
Numberofvisits
Days since registration
Registered in April Registered in May

21. What Cohorts Are Mostly Used
● Date, month or year of acquisition
● Most recent purchase date
● Traffic source of first visit

22. Recommendation #3
Don’t limit yourself to what your
web analytics tool offers. Make the
tool measure, process and visualize
what YOU want.
Pavel Jašek

23. Good Sources for Cohort Analysis
● Cohort Analysis 101 by RJ Metrics
• http://cohortanalysis.com/
● Cohort Analysis in Google Analytics
• http://cutroni.com/blog/2012/12/11/cohort-
analysis-with-google-analytics/
● Cohorts in Kissmetrics
• http://www.kissmetrics.com/demo/reports/cohort/

24. Content Segmentation Matrix

25. Content Segmentation Matrix Principles
● Group your content into sections of interest
● Compare pageviews by different types of visits
● Find significant differences as insights

26. Segmenting by Desktop and Mobile Traffic
Content section
Desktop
pageviews
Mobile
pageviews More on mobile?
Bank branches 2,0 % 5,4 % 174 %
Loan calculator 1,0 % 2,8 % 166 %
ATM Locations 0,4 % 1,8 % 333 %
Contacts 1,2 % 1,7 % 39 %
Currency exchange rates 0,6 % 1,0 % 70 %
First login 5,9 % 2,8 % -52 %
Mortgage products 3,6 % 1,7 % -52 %
Saving Accounts 1,5 % 0,8 % -47 %

27. What to Segment Content by
● Mobile traffic
● Traffic source (organic, PPC, affiliation, email…)
● Geolocation
● Customer segments

28. Recommendation #4
This segmentation allows you to
understand e.g. if your VIP
customers are visiting your new
products.
Pavel Jašek

29. Content-Content Matrix

30. Content-Content Matrix Principles
● Group your content into sections of interest
● Find number of visits that saw both sections
from each combination
● Using cross-selling principle „those who saw
section A also saw section B“
● Compute relative frequency of visits in such
combinations

31. Number of Visits in Section Combinations
Outdoor Clothing Shoes Light Sale
Outdoor 1 500
Clothing 540 2 500
Shoes 300 450 900
Light 655 600 477 1 450
Sale 732 777 151 633 1 780

32. Relative Visits to Site Sections
↓Outdoor ↓Clothing ↓Shoes ↓Light ↓Sale
Outdoor 100 % 22 % 33 % 45 % 41 %
Clothing 36 % 100 % 50 % 41 % 44 %
Shoes 20 % 18 % 100 % 33 % 8 %
Light 44 % 24 % 53 % 100 % 36 %
Sale 49 % 31 % 17 % 44 % 100 %

33. What Page Types Are Useful for Segmentation
● Homepage
● Product categories
● Contact pages
● User login
● Support pages

34. Recommendation #5
Sections cross-views give you
actionable insights about which site
sections should be better linked
together.
Pavel Jašek

35. Conclusion

36. Conclusions
1. Customer segmentation is important for
unfolding what really happens on your website
2. Evaluate campaigns only by the relevant traffic
3. Make your web analytics tool measure, process
and visualize what YOU want
4. Compare what different customers want on
your website
5. Segmentation gives you actionable insights

37. Action Steps
1. Start with the easiest segmentation possible
2. Make a draft output with a pencil
3. Think how would such output help you
4. Implement it
5. Analyze the data
6. Present your insights to your colleagues
7. Use those insights to improve your website

38. @paveljasek
pavel.jasek@dobryweb.cz