The document discusses limitations of traditional attribution modeling approaches and proposes using third-party behavioral data and customer metrics to build a behavioral attribution model. This involves segmenting customers by their receptivity to different channels like digital, magazines, newspapers, etc. based on past response rates. The model then attributes a purchase across channels based on the customer's measured receptivity to each channel, providing more customized and insightful attribution than traditional approaches. Visualizing results in different ways helps optimize media spend based on customer behavior and value.

1. Exploring New Methodologies for Attribution in a data sparse world.
Marketing Attribution Modeling
Michael Reese

2. Traditional Attribution Approaches and
Shortcomings
Positional
The customer purchase was the results of the (first, second, last) tactic used.
Linear
Everything we did had equal impact on the decision to purchase.
Time Decay
Our tactics accumulated to create a critical mass that triggered a purchase.
- Fine for digital activities, but mutes the effects of other channels.
- Digital fails if customer looks, decides but is not identified.
- It’s arbitrary to assign 1st, 5th or 100th tactic as the deciding factor.
- Data often lacking or does not validate the numbers.
- Difficult to really control the path the customer takes in digital.
- Defies credibility to believe all tactics and collateral have equal impact.
- If change is mandated, where does the model identify opportunity?
- Outdated approach, few in business believe all influences are equal.
- Renders data you do collect as irrelevant.
- If all touch points are equal, why didn’t they convert on earlier ones?
- Better credibility, but still difficult to identify opportunity.
- If change is mandated, where does the model identify opportunity?
- Still assumes all customers follow the same path, difficult to effect change.

3. Problem: None of the approaches take into
account the behavioral history of the customer.

4. Using 3rd party behavioral metrics
to understand the
mind of the customer.
Response rate to digital
Response rate to magazine
Response rate to newspaper
Response rate to radio
Response rate to TVResponse rate to email

5. Digital Receptivity: Measured by
metrics gathered on the amount of
times a person takes an internet
offer.
Classification: Highly Unlikely

6. Digital Receptivity: Measured by
metrics gathered on the amount of
times a person takes an internet
offer.
Classification: Highly Likely

7. This creates a series of measurable
outcomes:
Extremely Likely = “X”%
Highly Likely = “Y” %
Likely = “Z”%
Unlikely = “A”%
Highly Unlikely = “B”%
Extremely Unlikely = “C”%

8. There are multiple segments:
-Digital Receptivity
-Magazine Receptivity
-Newspaper Receptivity
-Radio Receptivity
-Television Receptivity
-Email Receptivity

9. We get an overall picture of the grower:
-Digital Receptivity – Highly Likely
-Magazine Receptivity – Highly Unlikely
-Newspaper Receptivity – Extremely Unlikely
-Radio Receptivity - Likely
-Television Receptivity – Highly Likely
-Email Receptivity – Extremely Likely

10. 2015 Order:
$125,000 Total
Customer Metrics
Joe Grower -
Influences:
Digital = .25
Mag = .13
News = .12
Radio = .12
TV = .13
Email = .25
Analytical Attribution Modeling
Digital Mrkt Magazines
Information and Insight Cycle
Newspapers Radio Television Email
$31,250 $16,250 $15,000 $15,000 $16,250 $31,250
Customer Journey / Purchase
Behavioral Attribution Modeling: Making sense of
consumer behavior.

11. Turning data into insight: Visualization approaches.
Dig
TV
Rad
Email
Mag
News
Dig
TV
Rad
Email
Mag
News
By comparing rolled up income by channel, you can compare against your media mix
spend at various cuts (geography, customer segment, customer share) to gain insight
Into optimizing your media spend dollars.
Income Expense