Representation for designing, building and running a simple, yet powerful predictive optimization model for varying dimensions.

1. Elanco Marketing
Amazon Media
Performance Optimization
Slide 1
Measurement & Analytics | Data Backbone Team | v 1.0

2. Data Analysis
Leveraging Predictive Modeling for Greater Business Outcomes
Optimizing Paid Media
Predictive Model Demonstration
Slide 2
Measurement & Analytics | Data Backbone Team | v 1.0

3. Slide 3
Paid Media Budget Optimization Predictive Model
Predictive modeling provides a tool to use historic benchmark performance to optimize real-time performance. Optimization
ideally is conducted in 90-day optimization cycles. Paid Media modeling adds value for managing the media buy as well as
delivering “what if” scenarios and insights which can be used for strategic planning.
 Model is designed for analyzing, managing, optimizing, and steering strategic execution of paid media campaigns optimizing for maximum
ROAS and Business ROI.
 Model has been tested and validated aligned with AI algorithms used within many media serving platforms.
 Model uses Amazon baseline media campaign and sales performance to establish benchmarks and for the model’s predictive algorithms.
 Model’s constraint criteria guides the underlying algorithm whch can be constructed from varying and multiple campaign dimensions and
considerations.
 Model can be run in one of three linear or non-linear algorithm modes: GRG Non-Linear, Simplex LP and Evolutionary depending on
defined constraint criteria.
 Model has been designed with flexibility to adopt to media campaign needs and requirements aligned with budget, channel, platform,
placement et al media spend guiding parameters.
Measurement & Analytics | Data Backbone Team | v 1.0

4. Slide 4
Paid Media Benchmark Performance (Amazon US_2021)
Predictive modeling uses past business performance to forecast (predict) future outcomes based on changing variables.
The optimization model which is represented in the slides to follow is designed to optimize Paid Media performance based on a defined, desired and measurable outcome.
In the case of optimizing the Amazon Paid Media Campaign, the desired outcome is enhanced e-commerce revenues on a greater Return On Ad Spend (ROAS).
Modeling starts with benchmarking past campaign performance to establish a baseline to compare against predictive modeled results.
Amazon 2021 Paid Media Campaign Results
Campaign
Ad Format
Placement
Cost (Spend) CPM Impressions Clicks CTR CPC
Conversions
(Orders)
CVR Gross Revenue AOV ROAS CPO Net Revenue
Banner $2,894,415.25 $3.68 785,947,008 747,649 0.10% $3.87 166,864 22.32% $9,001,210.66 $53.94 311% $17.35 $6,106,795.41
NA $1,249,191.00 $7.19 173,725,752 93,809 0.05% $13.32 31,790 33.89% $1,837,035.00 $57.79 147% $39.30 $587,844.00
Product Targeting $51,364.19 $2.83 18,138,654 19,291 0.11% $2.66 2,859 14.82% $112,061.94 $39.20 218% $17.97 $60,697.75
Sponsored Brand $883,664.18 $24.09 36,687,046 267,099 0.73% $3.31 89,157 33.38% $6,083,742.56 $68.24 688% $9.91 $5,200,078.38
Sponsored Display $559.58 $9.31 60,128 470 0.78% $1.19 173 36.81% $6,418.23 $37.10 1147% $3.23 $5,858.65
Sponsored Product $6,356,668.72 $30.54 208,131,092 1,263,951 0.61% $5.03 477,292 37.76% $25,675,469.59 $53.79 404% $13.32 $19,318,800.87
Video $1,680,542.73 $10.80 155,547,673 1,427,093 0.92% $1.18 10,941 0.77% $521,164.49 $47.63 31% $153.60 ($1,159,378.24)
Totals | Averages $13,116,405.65 $9.52 1,378,237,353 3,819,362 0.28% $3.43 779,076 20.40% $43,237,102.47 $55.50 330% $16.84 $30,120,696.82
Note: Benchmarking past performance allows us to begin analyzing the performance data to spot trends in both performance and spend. We can quickly see here an emphasis in 2021 was placed on Sponsored Product Ad
Formats. And although it generated a significant percentage of our e-commerce revenues, it was also the most expensive Ad Format media buy. Was this an efficient media spend? The objective of modeling is to help us identify
where efficiencies and inefficiencies are in the media spend.
Measurement & Analytics | Data Backbone Team | v 1.0

5. Slide 5
Paid Media Predictive Model Design (Amazon_US 2021)
The objective of our model is to find spend efficiencies as measured against revenue attribution to find the best optimal predicted outcome.
Observation: The first model test highlights that media spend efficiencies may be realized by re-allocating media spend
based on best performing Ad Format placements.
This first model test reveals we could potentially realize 100% improvement in ROAS representing a $40M revenue lift
from the same media spend budget simply by adjusting where and how the media is placed. Conducting a series of
additional tests based on changing dynamic constraint criteria will find the optimal best solution to our media spend
problem.
Measurement & Analytics | Data Backbone Team | v 1.0

6. Slide 6
Paid Media Predictive Model Outcomes (Amazon_US 2021)
Simple Representation | Predictive Modeled Log Outcomes | Problem Optimizes at 167% ROAS + $77M Revenue Lift (Increase)
Note: Removing the model variable: minimum target pools allows for running a Simplex LP or Evolutionary statistical model to find the absolute best optimization scenario. A GRG Non-Linear statistical model is used in this
demonstration with a minimum pool count which provides statistically valid results but may not be the absolute best scenario. The model does has flexibility using constraint criteria for guiding media buy requirements based
on campaign and business needs.
Measurement & Analytics | Data Backbone Team | v 1.0

7. Slide 7
Paid Media Predictive Model Outcomes (Amazon_US 2021)
Best Optimal Solution | Media Spend Problem Optimizes at 167% ROAS + $77M Revenue Lift (Increase)
Observation: Using the constraint criteria specified, the demonstration shows the model hits a ceiling optimization
threshold at 50% maximum spend per single Ad Format placement using the same 2021 media spend. If we were
optimize our media spend without any consideration to the number of Ad Format placement type inclusions, we can
see from past observations the 2021 emphasis on Sponsored Products is not in the optimized solution reinforcing spend
inefficiencies could have been in place in the previous media buy.
Model results can be used for media planning or real-time media optimization in cycled time periods to execute media
buys with the greatest spend and/or conversion efficiencies. Different media spend optimization outcomes could be
predicted by changing any of the constraint criteria variables, as well as, building the model to optimize against varying
dimensions based on guiding media buy needs and requirements.
(Note: Representation above and the model’s expansive mathematical calculations and rounding does hide (3) Ad Format placements with fractional spend allocations which could be revealed by expanding number decimal counts
and is not a model error based on minimum inclusion parameters.)
Measurement & Analytics | Data Backbone Team | v 1.0

8. Paid Media Predictive Model Methodology | Setup
Model Parameters | Assumptions
Objective Maximize Campaign ROAS leveraging associated assumed risks for fluctuation of campaign CPC performance values.
Assumption 1
Beta (b) Coefficients are blended historical cost performance averages per each dimension and segment REPRESENTING THE ASSUMED CPC RISK within the model forecast's volatility which is linked to the campaign's baseline requisite click-thru performance and a predicted
conversion event. Example: IF the Campaign variables remain the same and if the average Campaign CPC goes up by a $1.00, Net Gains will decrease. Conversely, if the average Campaign CPC goes down by a $1.00, Net Gains will increase. The model uses a blended average
Campaign CPC as a leading variable to predict constrained outcomes.
Assumption 2 View-Thru conversions (when applicable) do not impact budget spend optimization for maximizing ROAS.
Assumption 3 Net Profit = Gross Attributed Revenues - (COGS + Media Cost). Operating Costs are modeled. Media Costs are actual reported costs in media performance data.
Acronyms + Formulas
Cost Per Mille
(thousand impressions)
CPM CPM = (Ad Spend / Ad Impressions) x 1000
Click Thru Rate CTR CTR = (Clicks / Impressions) x 100
Cost Per Click CPC CPC = (Media Cost / Clicks) x 100
Conversion Rate CVR CVR = (Conversions / Clicks) x 100
Click to Conversion Rate CTC CTC = (Click Conversions / Clicks) x 100
View-Thru Rate VTR VTR = (View-Thru Conversions / Impressions) x 100
Cost per Acquisition CPA CPA = (Spend / Conversions) x 100
Average Order Value AOV AOV = (Revenue / Conversions) x 100
Return On Advertising Spend ROAS ROAS = (Revenue / Media Cost) x 100
Return On Investment ROI ROI = ((Gross Profit - (COGS + Media Spend) / (COGS + Media Spend)) x 100
Gross Profit -- Gross Profit = Gross Revenue - Media Spend
Net Profit -- Net Profit = Gross Revenue - (COGS + Media Spend)
Optimization Model Platform
Platform Microsoft Excel
Add-On Solver
Decision Variables An input value chosen by the decision maker in a problem represented by a model, e.g., CPC or ROAS.
Objective Function A mathematical formula that calculates the result the decision maker uses to determine how well their choices work out. Examples include profit, NPV, or cost. The objective function is either maximized or minimized. Thus, one would want to maximize profit but minimize cost.
Constraints A restriction or requirement that decision variables must meet. Typical constraints include a budget that cannot be exceeded, or a requirement that at least 25 percent of budget spend is spent on specific audience targets.
Linearity Refers to the mathematical form of the objective function and constraints. Linear functions in this context involve nothing more complex than multiplying decision variables by constants and adding up.
Algorithms
Solving Methods
GRG Nonlinear | works on linear problems, but it is designed for problems with nonlinear objectives, nonlinear constraints, or both. (A problem is considered nonlinear for Solver if the objective function or even one constraint is not linear.) May not be as accurate as the Simplex
LP method as it looks at the problem in randomly selected staged outcomes as opposed as a whole to find the optimal solution.
Simplex LP | used strictly for problems in which the objective function and all constraints are linear. Essentially, it solves different subsets of constraints to fi nd the combination of decision variables that optimizes the objective function.
Evolutionary | used to solve more complex and non-smooth non-linear problems. It looks for a global optimal solution, which makes it take longer to run compared to GRG Nonlinear. Uses test and re-test algorithm approach to find the optimal or near - optimal solutions.

9. For more information, contact:
Daniel McKean | daniel.mckean@elancoah.com