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Personal Brand Statement:
As an Army veteran dedicated to lifelong learning, I bring a disciplined, strategic mindset to my pursuits. I am constantly expanding my knowledge to innovate and lead effectively. My journey is driven by a commitment to excellence, and to make a meaningful impact in the world.
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2. Who am I?
G’day, I’m Peter...
I am Australian – with a strong aussie accent
Founder of L3 Analytics
Also Founder of MeasureCamp
Page 2 @peter_oneill 28th Nov, 2012
3. I intend to cover 4 areas*
1. Review visitor behaviour & touch points
How campaign attribution treats them
2. My debate with campaign attribution fans
3. Always focus on the business questions
4. My recommended approaches**
* Using an extended version of Matthew Tod’s football (soccer)
analogy to make my points
** I don’t have a case study to prove these work ***
*** Yet...
Page 3 @peter_oneill 28th Nov, 2012
6. Who gets the credit for a goal (conversion)?
Page 6 @peter_oneill 28th Nov, 2012
7. 1. Last Click Attribution
Final touch
scores goal &
gets all credit
RIO (location of the game)
8. Who gets the credit for a goal (conversion)?
The “Goal Scorer”
Last click attribution
Page 8 @peter_oneill 28th Nov, 2012
9. 2. First Click / Weighted Attribution Models
Midfield play
set up the goal
for the striker
Multiple players
contributed to the
goal & may deserve
some credit
RIO
10. Who gets the credit?
The “Goal Scorer”
Last click attribution
Midfield passes
First click / weighted attribution / descending
attribution / whatever allows you to pick the winner...
Page 10 @peter_oneill 28th Nov, 2012
11. 3. Ad Tracking Models
Ad tracking
networks don’t
capture all online
touch points
RIO
12. Who gets the credit?
The “Goal Scorer”
Last click attribution
Midfield passes
First click / weighted attribution / descending
attribution / whatever allows you to pick the winner...
Ignoring players
An issue with traditional ad tracking tools
Page 12 @peter_oneill 28th Nov, 2012
13. 4. Multiple Devices
Play started on
the other side of
the pitch & these
players deserve
credit too
Work (or smart Home (computer)
RIO
phone, tablet, etc)
14. Who gets the credit?
The “Goal Scorer”
Last click attribution
Midfield passes
First click / weighted attribution / descending
attribution / whatever allows you to pick the winner...
Ignoring players
An issue with traditional ad tracking tools
Initiators of the passage of play
You simply can’t ignore the issue of multiple devices
Page 14 @peter_oneill 28th Nov, 2012
15. 5. Offline Touch Points
Ball forced out by
defender & other
players provided
alternative
attacking options –
also deserve credit
RIO
16. Who gets the credit?
The “Goal Scorer”
Last click attribution
Midfield passes
First click / weighted attribution / descending
attribution / whatever allows you to pick the winner...
Ignoring players
An issue with traditional ad tracking tools
Initiators of the passage of play
You simply can’t ignore the issue of multiple devices
Defenders caused the error & ran in support
Offline touch points can’t be captured in any tool,
however powerful (or big the data is)
Page 16 @peter_oneill 28th Nov, 2012
17. Let’s look at a scenario
Kevin Hillstrom (MineThatData) wrote this scenario &
asked – How should this purchase be attributed?
http://blog.minethatdata.com/2012/10/your-opinion-wanted-attribution.html
Nine varied answers, most very precise
Lets have a flick through
Page 17 @peter_oneill 28th Nov, 2012
19. Defining the Debate
Campaign attribution tools only capture a part of
the visitor journey
All, half, most, a minority of the journey – it depends...
If all visitors login, you will get more (multiple devices)
But it is just not possible to get the full visitor
journey, however powerful (expensive) the tool is
or how big the data is
Let’s clarify what I believe the debate should be
“Is the data being captured sufficient to provide the
intelligence for informed business decisions?”
Page 19 @peter_oneill 28th Nov, 2012
20. Current Majority Opinion
Invest in the right tools &
throw enough resources at
the problem – and it will
can be solved!!
Image from SmartInsights blog
This is Digital Analytics – we
work with the data we have Image from Adobe SiteCatalyst blog
In sporting terms
“Point to the
Scoreboard”
Quotes from Tagman case study
Page 20 @peter_oneill 28th Nov, 2012
21. My Viewpoint - No
This is not a sampling size issue
e.g. We don’t have 100% accuracy but we can use the
trends
It is incomplete data
Scale of the problem is unknown
Data available can be misleading
Risk of wrong decisions is too large
Paul Postance: “it could be done better” is not an
insult. It’s a mindset to make things better.
Change the conversation from which model is
most accurate to how to optimise spend
Page 21 @peter_oneill 28th Nov, 2012
23. What do we really need to know?
Do we really need campaign attribution models?
No – they are simply the most obvious solution to the
business problem
Let’s focus on the business problems, not the
technology
There are three business intelligence requirements...
1. What do I report to the business?
2. How much do I pay agencies for the last period?
3. How do I optimise future marketing spend?
Why hunt for a single solution – use the best
approach to answer each requirement
Page 23 @peter_oneill 28th Nov, 2012
24. A 4th Question – from the CEO
Did we win?
RIO ROI (only location they care about)
26. Business Performance reporting
Pick a single approach & stick to it
I don’t care which (last click is simplest)
What is important is that
The business understands the approach
A change in performance can be easily identified
and reacted to!!
Football analogy – who scored the goals
Page 26 @peter_oneill 28th Nov, 2012
27. Evaluate Agency Performance
Define business objective for each campaign
Is it awareness, research, conversion, etc
Define what success looks like
KPIs & targets
Agree payment on this basis
Football analogy – performance based payments
Goals, tackles, minutes played, crosses, accurate
crosses
Business
Impressions, TVRs, visits, non bounce visits, visits that
create a basket, leads, sales, increase in NPS
Page 27 @peter_oneill 28th Nov, 2012
28. Don’t say this can’t be done...
Not saying it would be easy
But wouldn’t a campaign
equivalent of this be incredibly
useful/actionable?
Page 28 @peter_oneill 28th Nov, 2012
29. Optimise Marketing Spend
Remind me – how do we optimise again?
Objectives | Evaluation | Hypothesis | Test
Football analogy – simultaneous games
Page 29 @peter_oneill 28th Nov, 2012
30. Optimise Marketing Spend
Business equivalent – test campaigns
How to test campaigns
Different campaigns in different geographical regions
Hold out tests
Switch off/on keywords
Pick similar trending products & promote half
Measure impact from offline on online & vice versa
Learn what impact of campaign really is
Optimise spend using these learnings
Page 30 @peter_oneill 28th Nov, 2012
32. Summary
Campaign attribution is broken
Simply can’t capture all touch points
Instead of wasting money on impossible, invest
resources in the difficult
Focus on the real business intelligence
requirements
Be consistent in internal reporting
Evaluate performance against predefined
objectives & targets specific to campaign
Test to discover what works best
Adjust spend to maximise profitability
Page 32 @peter_oneill 28th Nov, 2012
33. THANK YOU
I can be found at
• peteroneill@l3analytics.com
• @peter_oneill
• +44 7843 617 347
• www.linkedin.com/in/peteroneill
Page 33 @peter_oneill 28th Nov, 2012