The document discusses designing a multichannel attribution model using clickstream data. It aims to create an exponential model that calculates weights for different marketing channels based on user interactions like impressions, clicks, and conversions. The model would then help determine the optimal campaign mix to achieve a target number of conversions at the lowest cost and predict future performance.

1. Design of multichannel
attribution model using click-
stream data
MeasureCamp Prague 2015
Lucie Šperková

2. Everything you need to know
(about me)
I used to work in bank.
The only language I can use is SQL.
I have never worked directly with GA, just extract the data from it.
Somebody said:
“Without data you are just a person with an opinion”
I say in addition:
“… but with data, which are messy nad shitty, you are a clear liar.”

3. Data overload?
Lack of data -> incomplete decisions
Too much data -> overload and still lack of knowledge (What I should focus on?!)
Basement / garage problem
I store big volume of data just for case, but will probably never use it.
-> Ask yourself why you will need them (have a target)

4. Why?
costs and revenues
expenses and benefits
income and spending
profit and loss
customer loyalty/satisfaction

5. Target
Create exponential model that takes into account all the inputs into
the conversion funnel.
With the use of AdForm metadata: for every cookie (user) on the
particular trackingpoint calculate number of interactions for the
particular time period and assign weights to campaign channels.

6. What I do / will do with the data...
- calculation of the weights and share of channels in conversions
- budgeting the total cost to the individual channels according their share
- visualize the shares of the channels
- drill down the channels - to medium, campaign,...
- slice according to refferer type, device type, customer segments …
- find the right campaign mixture (how to achieve particular number of conversions for the lowest price)
- prediction of the future development and setting the right campaign mixture
- observe the conversional / non-conversional rates (how many interactions didn’t lead to conversion)
- intregration of data from other sources (GA, sklik, CRM, budgets, etc.)
- revenues from conversions
- customers data
- ...

8. seen the
banner 1
seen the
banner 2click
PR
click
PPC
click
Organic
click
banner1
Web - Conversion
1point 1point2points 2points2points 3 points
Weights assigned according to:
basic division:
conversion click (triggered the trackingpoint)
last impression (triggered the trackingpoint)
direct entry
click
impression
refining the weights:
● by mouse overs, mouse over time, visibility time,
refferer type, medium etc.
● on the web there are
many trackingpoints
cookie has visited (not
interested about the
move through websites)
● focus on conversion
points or points
foregoing conversions
(e.g. where customer
left the action)

10. Trackingpoint A

11. Trackingpoint B

12. Trackingpoint C

13. Trackingpoint D

14. Trackingpoint E
conversion

15. metadata
calculations
extract
extract
transform

16. Process of basic transformation
data cleaning
- delete robotic transactions
- transactions, which happened in less than 30 minutes from the last transaction (same cookie, same
trackingpoint, same session) - avoid refresh
joins
- for every cookie at the trackingpoint find all interactions which happened during the time between
trigger of the last trackingpoint and today’s trackingoint (for more conversions of single cookie)
- every cookie can have interaction with different campaign: calculation for every campaign (avoid
multipletimes counting of the same add - banners etc)
- the campaign of the conversion interaction is known (higher weight)
weights calculation and refining

17. !

18. Predictions
costs
conversions
(revenues)
more investments to this campaign mix won’t help
right campaign mix for acceptable price
100 300 330

19. Thanks. Let’s talk!
mail: lucie.sperkova@gmail.com
linkedin: https://cz.linkedin.com/in/luciesperkova
twitter: https://twitter.com/pihatka