The document discusses the transition from traditional web analytics to Marketing Mix Modelling (MMM) for understanding user behavior and the effectiveness of marketing efforts. It outlines data requirements, typical questions MMM can answer, and highlights the importance of insights gained regarding overall advertising investment, pricing impacts, and competitive factors. Additionally, it addresses the pros and cons of MMM while providing optimization recommendations and expected outcomes from varied marketing strategies.

1. From Digital Attribution to MMM
#MeasureCampNL – April 21, 2018
@mertanen

2. Using machine
learning for
explaining and
predicting user
behavior from web
analytics data.
2

3. Traditional Web
Analytics is dying...

4. Optimizing Promotions
4

5. Cookie-based 1:1 attribution produces superior
understanding of the conversion journey
Tradional measurement:
Customer journey:
Time Value
These clicks are ignored
Time Value
The last click gets all the credit

6. Assisted conversions

7. Attribution model comparison

8. Cookie-based 1:1 attribution produces superior
understanding of the conversion journey
Algorithmic attribution:
Customer journey:
Time Value
Model estimates value of each touchpoint
ValueTime

9. Attribution model comparison
Organic search Paid search Email Social media
Total conversion value - example
First touch
Last touch
Linear
Advanced Attribution

10. The 4-7 Ps of Marketing
10

11. The total effect of marketing actions over time
Direct Buy
Short Term Effect
Create Curiosity,
Seek online info
Intermediate
Effect
Change attitude
towards brand
Long Term Effect
Time
Total Sales Effect
+ + =
Campaign
Digital Analytics Digital Analytics + Research Marketing Mix Modelling

12. What is MMM?
Marketing Mix Modelling
Media Mix Modelling
Econometric Modelling
Media Attribution Modelling

13. It is used to understand all drivers of business
performance Offline Investment
Brand image
Market Dynamics
Operational factors
Digital media
TV media
Pricing / Promotions
PR
Competitors
Business
KPI
SALES!

14. Typical Data Requirements
KPI
Sales / Bookings / Quotes
Web visits / calls
Regional
Store level
By source (e.g. retail vs online sales)
Internal
Pricing
Promotions
Store level info
Launches / product changes
Retention
Media
Offline media: spend/ratings by
channel
Online media:
Impressions/clicks/spend by channel
Social media: Facebook and Twitter;
Likes/Clicks/Impressions
Media spend for all competitors by
channel
DM / Email etc volumes
Other
Competitor info
Weather
CCI, RPI, Inflation etc.
Legislation
Brand metrics
Data needs to cover a
wide range of factors
Data needs to be over
a long enough period
Data needs to be
weekly for best results

15. Modelling projects follow a similar process
Insights
Debrief writing
Results
Delivery
Kick Off
Data
Collection
Data
Processing
Analysis /
Modelling
Pulling
together
results
Several months

16. Typical questions MMM can answer
How effective is our
marketing and
advertising
investment overall?
How important is
pricing to my sales
and what is the likely
impact of future
changes I might
make?
Do I need to be
‘always on’ or are
there seasons when
investment is more
effective?
How are my
promotions
performing and which
ones deliver the most
revenue?
Which communication
channels are most
effective? Return on
Investment?
How do current
events and market
dynamics impact my
KPIs?
How does competitive
media impact my
business?
Should I spend more?
How many more
sales would that give
me?

17. Sales drivers - weekly basis
What is exactly causing the sales?
Degree of explanatory power: 58%
-1,000
-500
0
500
1,000
1,500
2,000
2010-1
5
9
13
17
21
25
29
33
37
41
45
49
2011-1
5
9
13
17
21
25
29
33
37
41
45
49
SALES
Base Seasonality Distribution Price TV Sales

18. 88% 8% 8% 6% 2% 0% -1% -1% -10%
857.447
79.646
75.566
61.520 22.250
-0 -6.000 -13.366
-102.223
974,841
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
BASE Price & Promotion Tracking Media Distribution &
Launches
Unexplained Seasonality Competitor - Media Competitor - Price
& Promotion
Total
Revenue('000)
Promotions are the
biggest driver of sales.
Negative impact from
price increases
Media investments
drive almost as
much sales as
promotions
Successful product
launches have
grown the brand
Competitor price
reductions and
promotions have
had the biggest
negative impact
Sales drivers - weekly basis
What is exactly causing the sales?

19. Optimization recommendations
Changes in media allocations
TV Spot
Web-TV
Desktop Display
Mobile Display
Programmatic Display
Programmatic Mobile
Facebook
Chart Title
2015 budget
2016 optimized budget
Media Total effect
TV
ROI:
Sales effect:
3,3  3,7
+2 500 000 €
+20%
-37%
+20%
+20%
Change %Investment level
Mobil
Display
Web-TV
Programmatic
Mobil
Programmatic
Display
Facebook
-100%
+20%
-100%
000’

20. What kind of results you can expect?
+6%
ROI/CPA
10 % of clients are
making small
changes on media.
+12%
ROI / CPA
50 % of clients are optimizing media
mix and timings. They get benefits
from media synergies.
+28%
ROI / CPA
40 % of clients makes significant
strategic decisions regarding to
marketing actions and media budgets.

21. Pros & Cons of MMM
What’s good
• We can quantify objectively & fairly
- What is driving our KPI
- When it affects sales (short/medium term)
- How much it affects sales
- Factors outside of your control
• We can provide a starting point
- For strategic planning purposes
- For investment allocation
• Explains and quantifies your historic KPI trend
What’s difficult
• Data Limitations
- Incorrect/missing data
- Lack of variation
- Multi-collinearity: all media happens at the
same time
- Long data series required
• We can’t always tell you a positive story
• We can’t tell you the impact of something you
haven’t done before
- Or the impact of activity beyond levels of
experience
• Long project timeframe

22. Contact information
Petri Mertanen
Director, Digital Analytics
petri.mertanen@annalect.com
Mobile: +358 400 792 616
@mertanen