This document discusses metrics and key performance indicators (KPIs) that can be used to predict marketing profitability. It explains that leading metrics like click-through rate are less useful for predictions than tier 1 KPIs like return on ad spend (ROAS) and lifetime value (LTV) that are directly tied to business outcomes. The document shows how to analyze relationships between early metrics and later KPIs using scatter plots and trendlines in Excel. It provides examples of using this approach to predict cost per installation, user retention rates, and return on ad spend at different time periods after initial user acquisition.

1. Which KPIs to Use to Predict
Marketing Profitability
A Framework for Predictions

2. My Background
• Microsoft, Incipia, Felix Analytics
• Senior marketer: $2MM/month
• 250+ articles, 1 book
• Co-lead data team

3. Metrics vs
KPIs
Metric
(Tied to marketing model, e.g. CTR)
KPI
(Tied to business model, e.g. LTV)

4. Leading
Indicators
(Metrics)
• CPC
• CTR
Tier 2
• CPI
• Retention rate
Tier 1

5. Early
Benchmarks
(Tier 2 KPIs)
CAC
(Customer Acquisition Cost = Spend /
Total Paying Users)
Cost Per Day x Retained
User
(Cost / Retention Rate at Day X)

6. Confident
Predictors
(Tier 1 KPIs)
ROAS
(Return on Ad Spend = Revenue /
Spend)
LTV
(Lifetime Value of a User = Retention
* Monetization)

7. Excel Scatter Plot Trendline Analysis
Linear – rate of
change is constant
Exponential – rate
of change increases
Logarithmic – rate
of change increases,
then levels out
Polynomial – rate of
change fluctuates
Power – rate of
change increases at
a specific rate

8. Linear
Trendline
y = 9.2695x - 0.0936
R² = 0.8492
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00% 18.00%
6-MonthROAS
Week0 ROAS
Linear Trendline Week0 ROAS vs 6-Month ROAS

9. Exponential
Trendline
y = 0.306e9.5159x
R² = 0.8695
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00% 18.00%
Exponential Trendline Week0 ROAS vs 6-Month ROAS

10. Logarithmic
Trendline
y = 0.8794ln(x) + 2.9205
R² = 0.731
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00% 18.00%
Logarithmic Trendline Week0 ROAS vs 6-Month ROAS

11. Polynomial
Trendline
y = 72.346x2 - 7.1419x + 0.7509
R² = 0.9246
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00% 18.00%
Polynomial Order 2 Trendline Week0 ROAS vs 6-Month
ROAS

12. Power
Function
Trendline
y = 7.2412x0.9343
R² = 0.8016
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00% 18.00%
Power Function Trendline Week0 ROAS vs 6-Month
ROAS

13. Apply the
formula to
predict
profit
(Linear)
Solve for y = 100%
When 6-month ROAS is 100%, what is Week0 ROAS?
X = 11.80%
R-Squared = 84.92%
(fair accuracy)
y = 9.2695x - 0.0936
y = 6-month actual ROAS
x = Week0 actual ROAS

14. Model Training &
Selection
• 80-90% of total data for training
• 10-20% of data for prediction
• Calculate mean absolute
percentage error (MAPE)
• Lower is better
• Least complicated is better for
non-statisticians
• Linear
• Exponential

15. How Scenarios Affect Models
• What ifs:
• Low: 5% Week0
matures to 99% 6-
month
• High: 13% Week0
matures to 70% 6-
month
The best model given normal
data can fail to predict
unexpected data

16. Check Your Data
Do you have enough
observations?
•Rule of thumb: Shoot for
60+ total and ~10 of the
goal level
01
Do you have too much
variation or overfit?
02
Are you continually
feeding your model
with the latest data?
03
Did you track actual
results vs prediction
hit rate?
04

17. Check Your
Data
Did you use
the right
confidence
predictor?
Did metrics
experience
significant
change?
Did you apply
segmentation?
Are you
considering
time
influences
(seasonality,
holidays,
lifecycle)?

18. Applications:
Predicting CPI
Which data point is most correlated
with Facebook Ads CPI?
• CTR
• Impression Conversion Rate
• Click Conversion Rate
• Relevance Score
• CPC

19. Answer: CPC
47%
3%
-2% -1%
0%
-10%
0%
10%
20%
30%
40%
50%
~CPI R Squared
y = 0.5248x + 0.9599
R² = 0.4724
$-
$0.50
$1.00
$1.50
$2.00
$2.50
$- $0.50 $1.00 $1.50 $2.00 $2.50
CPI
CPC
CPC ~ CPI
y = -5.058x + 1.5782
R² = 0.0058
$-
$0.50
$1.00
$1.50
$2.00
$2.50
0% 0% 0% 1% 1% 1% 1% 1% 2%
CPI
CTR
CTR ~ CPI
No
Correlation
Some
Correlation

20. Applications:
Predicting
D30 Retention
What is the fewest number of days
after install to reach 80% R-squared
correlation with Day 30 retention?
• Day 1
• Day 2
• Day 3
• Day 4
• Day 5
• Day 6
• Day 7

21. 12%
23%
21%
21%
16%
14%
11%
6%
36%
54%
0%
10%
20%
30%
40%
50%
60%
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 14 Day 21 Day 28
X Days After Install Retention ~ Day 30 Retention R-Squared
Answer: None!
Even D28 is a
coin toss
D2 is the best
in first week

22. Applications:
Predicting
ROAS
How many weeks after install ROAS
data do we need to accurately
predict 12-month ROAS?
• First week
• First 2 weeks
• First 3 weeks
• First 4 weeks

23. 9%
17%
24%
28%
0%
5%
10%
15%
20%
25%
30%
First Week Only First 2 Weeks First 3 Weeks First 4 Weeks
X Weeks After Install ROAS ~ 12-Month ROAS R-Squared
Answer: The More
the Merrier Even 4 weeks is not
highly correlated
Week0 is a
pretty poor
predictor

24. 0
0.2
0.4
0.6
0.8
1
1.2
0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% 40.00% 45.00% 50.00%
100%+ ROAS in 12 Months?
A Deeper Dive: Why is
Week0 so Poorly
Correlated?
The range for reaching 100% in 6-months (1) is
from 22.22% - 44.81% (mean 32.495%)
The range for failing to reach
100% in 6-months (0) is 20.24%
to 44.48% (mean 29.4832%)