The document discusses the evolving landscape of measurement and analytics in advertising, highlighting the dual pressures of consumer privacy demands and regulatory changes like GDPR. It outlines traditional and modern strategies for measuring marketing effectiveness, including econometrics, digital attribution, and controlled experiments, while addressing their respective strengths and weaknesses. The future of marketing measurement is suggested to involve a hybrid approach combining multiple methodologies to optimize insights across channels.

1. Alex
Maksimov
Measurement & Analytics Lead
Google UK
Measurement Strategy
©2020 INCUBETA. ALL RIGHTS RESERVED.

2. Measurement strategy
Measurement & Analytics Lead
Alex Maksimov

3. Why?

4. Privacy and personalisation
Consumers
User expectations are rising as
people demand more
transparency, choice, and
control over how their online
data is used
Advertisers
Companies want to provide
more personalised
messaging and retargeting
through customer data
vs

5. The industry is shifting
Regulatory Changes
New regulations such as GDPR
are impacting how data can
be collected
and used
User Privacy Controls
Users are demanding more
control and transparency over
data collected and used for ads
personalization
Browser Updates
Heightened controls are
impacting traditional data
collection (e.g. third party
cookies and device identifiers)

6. The predictive era
Broadcast Era
Target an audience in an
environment with the
understanding not everyone
is relevant. Reaching the
majority of your target
audience.
Precision Era
Target the right customers
at the right time with the
right media. Aspiration to 1:1
marketing.
Predictive Era
Striving for the best of both
worlds in a privacy safe way.
Doing more with less.
Sophisticated algorithms.
You are here

7. Implications for advertisers
How and what brands can measure
Crediting marketing activity with
customer or business outcomes
Getting to the right number
Marketers need to do more with less

8. The right number?
Econometrics/MMM
A regression model estimates
the effect of all advertising on
weekly sales, alongside all
other factors.
Digital
Attribution
Credit is assigned to
digital media
touchpoints based on
their involvement in
conversions
Controlled Experiments
Users or regions are matched to
differ only by their media
exposure. Uplift is inferred to have
been caused by media.
Three
common
tools

9. Lloyd Shapley 1923 - 2016

10. (in the precision era)
Digital Attribution
Television Radio
Social
Paid Search
Organic
Search
Email
Direct Mail
Display OLV
Content
Affiliate
Conversion

11. (in the prediction era?)
Digital Attribution
More data Less data
Deterministic Probabilistic
3P 1P

12. Strengths and weaknesses (not exhaustive)
Digital Attribution
Strengths
● Managing budgets within a channel
● Getting fast insight - can be close to real
time
● On the fly bidding decisions
● Valuing beyond the last click
Weaknesses
● Showing the incremental impact of the
advertising
● It can’t tell you anything about something
that is not in the system
● It assumes that the media in the tool is
what caused the outcome

13. Francis Galton 1822 - 1911

14. Simplified version
Econometrics for marketing
Sales ~ Base Sales + Advertising + Other Factors + Error
What would have
happened anyway
Paid EarnedOwned
Under Advertiser
Control
Outside Advertiser
Control
Because no model
is perfect
Traditional Digital
Products Distribution
Promotions
Price Trends
Weather
Competitor
Activity
EconomyService
Season
● TV
● Radio
● Outdoor
● Print
● Sponsorship
● Door Drops
● Paid Search
● Display
● Online Video
● Paid Social
● Website
● Owned Social
● Direct Mail
● PR
● Organic search
● Social engagement
● Word of mouth

15. Strengths and weaknesses (not exhaustive)
Econometrics for marketing
Strengths
● Estimating the contribution of marketing
and media to the overall business
● Explaining what (is likely to have) caused
short term sales
● Making budget recommendations
for advertising channels
● Showing how channels work together
● [Advanced] Looking at brand advertising’s
impact on sales
Weaknesses
● Recommending what to do when you
change price, product, strategy
● Making quick decisions - they require >2
years of historical data - and take a fair
time to do (most of this is collecting and
cleaning data)
● Explaining things that follow sales
(Example: always on media - often there
won’t be enough variation in the data for
the model to pick it up)

16. Louis Pasteur 1822 - 1895

17. The scientific method in the general form
Controlled Experiments
Make an
observation
Ask a
question
Form a
hypothesis
Conduct an
experiment
Accept the
hypothesis
Reject the
hypothesis

18. Strengths and weaknesses (not exhaustive)
Controlled Experiments
Strengths
● Showing the incremental impact
of a change on any metric
● Proving the value of a specific channel
● Testing new approaches
● Moderating the findings from
other tools
Weaknesses
● Dealing with many changes at once
● Allowing marketers to carry on with the
same level of spend
● Getting real-time results - normally
experiments take 4-12 weeks to run

19. Multiple solutions for a diverse set of insights
The future?
Incremental marketing mix attribution:
A blend of MMM, Experiments and Attribution, that works
across all channels, and controls for external factors.
Maybe it’ll do the creative too :-)

20. Thank you