The document discusses four case studies of companies that implemented end-to-end analytics solutions to better understand customer behavior and improve marketing ROI. The first case study found that 40% of offline purchases were preceded by online research. The second case evaluated how online channels contributed to 10% of offline revenue. The third case used cross-device data to improve keyword selection and increase PPC ROI by 17%. The fourth case built customer segments and increased SMS revenue by 19% through triggered messages. Overall, the case studies demonstrate how combining online and offline customer data can provide insights to optimize marketing.

1. How to Build
End-to-End Analytics

2. CASE 1:
ROPO Analysis Proves that 40%
of Customers Visit the Company’s
Website before Buying Offline

3. About Darjeeling
1. Founded by Group
Chantelle in 1995
2. 155 retail stores
3. 8.7 million offline visitors
per year
4. Turnover over 100M
Euros/year

4. Goals:
Evaluate the impact of online campaigns on the offline sales
Challenges:
1. Darjeeling uses different systems to collect, store and process data
2. They never combine online and offline users
3. The data in the system is in French, while the data collected in Google Analytics
is in English

5. Data Flow

6. Combine online & offline data
This is how the data was merged:
1. The analysts used the following
keys from the table: transaction_id,
user_id, and time.
2. Next, they selected the data about
all online user interactions before
the selected date, taking account of
the order completion rates.
3. Finally, they identified the channel
groupings for the sessions that
were closest in time to the
transaction date.
Table with Completed Orders

7. Customers’ Shopping Journey
It turned out that 85% of
all ROPO purchases are
made within 14 days.

8. How Does the Number of Online Transactions
Vary?

9. How Average Revenue per Order Changes
Over Time and Touch Points?

10. How Average Revenue per User Changes Over
Time and Touch Points?

11. Revenue from all the Online Channels

12. Results
1. Revenue from online channels
turned out to be 7 to 19% instead
of the 3 to 6% estimated before.
2. Optimized their ad budget
Complete success story – here

13. CASE 2:
M.video’s Success Story: Evaluating
the Impact of Online Advertising on
Offline Sales

14. About M.Video
1. Leading consumer
electronics and home
appliances retail chain in
Russia
2. M.video has more than 400
stores in 165 cities across
Russia
3. Reaching an annual
turnover of more than 200
billion rubles.

15. Goals
Challenges
Evaluate the impact online channels have on offline sales
M.video collects, stores and processes all the data in different systems:
● The data about user interactions with the website is collected in Google
Analytics 360.
● The data about offline purchases and order returns is collected in the company’s
CRM system (SAP).

16. Data Flow Chart

17. Data about each of the orders

18. Number of online, offline, and ROPO purchases

19. Additional revenue from ROPO purchases

20. Results
1. The company found out that
online channels contributed to
about 10% of offline revenue
2. Discover the reasons why these
customers choose to shop offline
Complete success story – here

21. CASE 3:
How to find efficient keywords
using cross-device analytics
and raw data

22. About Hoff
1. Omnichannel
hypermarket retailer of
furniture & household
items
2. 25 stores in 11 cities
3. 17+ M unique users

23. Business Objective
Improve online
advertising ROI

24. Analytical Objective
1. Collect necessary data
2. Evaluate the revenue
driven by each keyword
3. Automate bid
management for PPC
advertising

25. Marketing always means large business investments that influence brand
awareness and revenues.
10%
Of orders get lost
on the way to GA
50%
Of orders and more, are not considered
when evaluating advertising efficiency
in multi-channel companies
Problems on the way

26. Problems on the way
82%Of orders are
made after 2+
visits
83%Of sessions don’t
get evaluated when
using Last-Click
2+Devices are used
on a way to a
purchase
Position based attribution models don’t consider the influence of each
session on a customer’s way to a purchase.

27. Anna’s Cross Device Adventure

28. Nick’s Cross Device Adventure

29. Nick Keeps Surfing

30. Hoff’s Attribution Logic
1. Determine the channel that introduced the customer to the brand
2. Determine the channel that converted the customer (Last Click)
3. First session gets 20% of the credit
4. Last session – 30%
5. Everything else – 50%
6. Consider pageviews during the session and hours to purchase

31. Results
1. 2.4 times more keywords
2. PPC advertising ROI +17%
Complete success story – here

32. CASE 4:
How to measure the influence of
display advertising and increase
SMS-driven revenue

33. About Answear
1. A multi-brand online
fashion retailer
2. Founded in 2010
3. Offers more than 200K
products
4. Operates in Czechia,
Poland, Ukraine, Hungary,
Slovak Republic

34. 1. New markets: increase sales, encourage repeat purchases and expand
customer databases;
2. Current markets: customer retention and re-engagement.
1. Collect the necessary data
2. Build segments
3. Launch triggered SMS for the relevant segments
Business Objective
Analytical Objective

35. Data Flow

36. Reporting

37. 1. Efficient automated end-to-end
analytical system
2. Evaluated marketing channels
3. Increased revenue from SMS
marketing by 19%
Complete success story – here
Results

38. Takeaways

39. 1. Set up KPIs and goals
2. Develop a taxonomy, naming conventions, etc., to be on the same page
3. Bring the data together (on-premise or cloud)
4. Test and apply the the best option
5. Measure the impact
What’s Next

40. 1. Consumer barometer from Google
2. GCP free tier
3. Google Data Studio (free)
4. Google Optimize (free tier)
5. Guide on end-to-end analytics
6. Cost Data import (free)
Useful Links

41. Questions?
Mariia Bocheva
m.bocheva@owox.com

42. www.owox.com/reports/
MeasurecampLondon