Niels Reijmer (Data Analyst) and Tim Nooren (Data Engineer) at de Bijenkorf talked about how they used Snowplow Analytics with their online and offline data to get a centralised customer view and how they used this in personalising their newsletter, online marketing and their website. Presented on 5 April 2017.

1. Snowplow @ de Bijenkorf
05-04-2017

2. 2De Bijenkorf
▪ 145 year old high-end department store
▪ Focus on fashion
▪ Physical stores and a webshop

3. 3
To create the ultimate personalized
omnichannel shopping experience
for our customers
Our goal..

4. 4One user view
Radio
Abri / tv
Display
Social
Sea Seo
Affiliate
Retargeting
Nieuwsbrief
App push Triggers

5. 5
ApplicationsData
User identification
Models /
Selections
DMP
Storage
Email
SiteEmail
Etc.
OMS/ERP
Contents

6. 6
ApplicationsData
User identification
Models /
Selections
DMP
Storage
Email
SiteEmail
Etc.
OMS/ERP
Contents – Snowplow architecture

7. 7
ApplicationsData
User identification
Models /
Selections
DMP
Storage
Email
SiteEmail
Etc.
OMS/ERP
Contents – User identification

8. 8
ApplicationsData
User identification
Models /
Selections
DMP
Storage
Email
SiteEmail
Etc.
OMS/ERP
Contents – Models and applications

9. 9
ApplicationsData
User identification
Models /
Selections
DMP
Storage
Email
SiteEmail
Etc.
OMS/ERP
Snowplow architecture

10. 10Infrastructure

11. 11Infrastructure

12. 12Cloudformation

13. 13Cloudformation
Benefits
• Define stack once, set up in minutes
• Central stack definition
• Easy updates/deployments
• Rollback in case of failure
• Version control your infrastructure

14. 14Try it yourself
github.com/debijenkorf/snowplow-cloudformation
Snowplow streaming in minutes!

15. 15
ApplicationsData
User identification
Models /
Selections
DMP
Storage
Email
SiteEmail
Etc.
OMS/ERP
User identification

16. 16
Recognize the user

17. 17
Connect to store information
research online and buy
offline?
38% of the revenue attributed
to a customer Id

18. 18
Connect to store information
cd87785b18645cbc
81% of the revenue attributed
to a customer Id

19. 19
ApplicationsData
User identification
Models /
Selections
DMP
Storage
Email
SiteEmail
Etc.
OMS/ERP
Contents – Models and applications

20. 20
ApplicationsData
User identification
Models /
Selections
DMP
Storage
Email
SiteEmail
Etc.
OMS/ERP
Emails
Email

21. 21Trigger mails

22. 22Trigger mails

23. 23Triggers in email
Open rate Click to open rate Conversion rate
Newsletter 35.00% 19.00% 2.10%
Trigger 62.00% 40.30% 3.60%
0.00%
25.00%
50.00%
75.00%
Newsletter vs trigger mails
Newsletter
Trigger

24. 24Newsletter
▪ Newsletters are send out
every week
▪ Often focused on specific
brands and/or discounts
Hugo Boss
Not price sensitive

25. 25
Determining segments
User
Acne
Studios Barbour Barts Chantelle
Cheap
Monday Diesel Esprit Essenza
French
Connection
XYZ123 0 1 0 1 0 1 0 0 0
KADS546 0 0 1 0 1 0 0 1 1
ESD112 1 1 0 1 0 0 0 0 0
Segment 1 Segment 2
Barbour Barts
Chantelle Cheap Monday
Diesel Essenza
Acne Studios French Connection

26. 26Users are given a style
Give “points” for each event on a brand
1 point 20 points1 point
Brand Style Points
McGregor Classic Preppy 1
Ladress Business 1
Maison Scotch Denim 20

27. 27First analyses style and discount
Stylematches:
Discount effects:

28. 28
Fashionstyle
Discount
NBA model
Fashionstyle
Discount sensitivity
Browsing behavior
Average mail open rate
Average mail CTR
A
+ = no
B
+ = no
C
+ = yes

29. 29Only model results
▪ 100% means prefectly predicted
▪ 50% means just as good as randomly sending mails
▪ 70% is good and 80% is very good

30. 30
ApplicationsData
User identification
Models /
Selections
DMP
Storage
Email
SiteEmail
Etc.
OMS/ERP
DMP
DMP

31. 31DMP
Classic
Hugo Boss
Not price sensitive

32. 32DMP – trigger
Browsed
ugo Boss shirt without
buying

33. 33Results????

34. 34
ApplicationsData
User identification
Models /
Selections
DMP
Storage
Email
SiteEmail
Etc.
OMS/ERP
Questions?