Representation for designing an optimization paid media model to optimize media spend by ad group.

1. Flexible GRG Nonlinear vs. Simplex LP vs. Evolutionary Statistical Modeling
non-smooth non-linear problems
GRG Nonlinear
linear problems
Simplex LP
morecomplex,non-smoothnon-linearproblems
Evolutionary
Proprietary Design of Daniel McKean | linkedin.com/in/danielmckean
DESIGNED FOR OPTIMUM PREDICTIVE RESULTS
Method is limited in its application because it
can only be applied to problems containing
linear functions.
However, it is very robust. With a linear
problem, the method’s algorithm will ensure
the solution obtained will always find a global
optimum solution.
Method looks at the slope of the objective
function as the decision variables change and
determines that it has reached an optimum
solution when the partial derivatives equal
zero.
The solution obtained is highly dependent on
the initial conditions and may not always be
the global optimum solution, but is
statistically valid as a local optimum solution
(one of several possible optimum solutions).
Method is more robust than GRG Nonlinear
as it is seeks to find a local optimum solution
with greater statistical probability representing
a global optimum solution.
The method starts with a random set of input
variables and the results are evaluated
relative to the target value. The sets of input
variables that result in a solution that’s
closest to the target value are then selected
as the optimum solution.

2. Predictive Modeling Design
Goal: data modeling uses a four-step design to analyze and
optimize different subsets of linear or nonlinear variable
constraints to find the optimal combination of decision variables
that meets the model’s defined objective function.
Design: the model’s core design is built to meet varying
demanding objectives that align to improving business and
marketing performance across digital channels and measurable
executable tactics.
Objectives: the model can be used for leading business and
marketing objectives including optimizing budget spends,
conversions, ROI, revenue, CLV, engagements, customer leads,
and much more by linking the model to a mix of relational KPIs
and metrics benchmarked from past business performance.
Flexibility: the predictive model will work with most (if not all)
digital marketing channels including paid advertising, PPC, SEO,
email, SMM, and content marketing.
Integrity: the model’s optimization insights are based on the
business or marketing objective you define leveraging relevant
variable goal constraints against real historic business or
campaign performance benchmarks.
Statistical Outcomes: the model optimizes to your leading
business objectives by statistically minimizing risk or executing
within an acceptable target level of risk using a relevant Beta
coefficient which takes into consideration actual real-world
business performance dynamics and volatility.
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4 Steps to Business | Marketing Optimization
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Track and measure business performance.
Benchmark leading KPI and metrics.
Define a business objective for optimization.
Map relevant KPIs to business objective.
1. Define the Problem
Identify business performance to
decision variables and constraints.
Link variable constraints to the model.
Test and validate the model.
2. Align the Objective
Execute a course of action based on a
chosen acceptable model outcome.
Continue to test and measure
optimized performance.
Re-run the model and adjust execution
accordingly over specified time periods.
4. Execute & Optimize Run the model to find the
baseline localized optimal
solution using the appropriate
statistical method.
Continue to model acceptable
upper and lower thresholds for
the best outcome and optimal
solution.
3. Run the Model
4-STEP
OPTIMIZATION
PROCESS
1. Define the Problem 2. Align Objective to Model
3. Run the Model 4. Execute & Optimize

3. Representation
Marketing Channel
Paid Media (Display Advertising)
Benchmarked Business Performance
A 90-day programmatic campaign period with a modest media budget using
10 Ad Groups to target the online sale of commercial DTC products.
Business Optimization Objective
Improve campaign’s return on media spend (ROAS)
Campaign Leading KPIs
Media spend, budget allocation, impressions, clicks, CPC, revenue, ROAS
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Predictive Analysis Example
ROAS Optimization Problem Specifics
Maximize ROAS (Return On Ad Spend)
To find the optimal predicted maximum ROAS, the model uses a historic
business benchmark and variable constraints to control the statistical
analysis.
Find the optimum Ad Group budget spend mix for maximizing ROAS within
an acceptable assigned eta (maximum assumed risk for CPC performance
volatility).
Solution should include THE MAXIMUM % of total budget spend applied per
any single Ad Group (if applicable) for managing budget distribution aligned
to campaign objectives.
Solution should include A MINIMUM NUMBER OF Ad Groups (if applicable)
for managing budget diversification to meet defined campaign objectives.
Solution should optimize to the assigned budget allocation.
Variable constraints should allow for the analysis of the acceptable
upper/lower optimization thresholds to find the global optimal solution
which meets the business objective.
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Represented Benchmarked Model Data: An actual 90-day campaign period with a
modest media budget for a programmatic media buy using 10 Ad Groups to target
the online sale of commercial DTC home video content across multiple genres is used
to demonstrate the model’s functionality.
Proprietary Design of Daniel McKean | linkedin.com/in/danielmckean
Sample 90-Day Campaign Benchmark Performance PivotTable

CPC Risk Volatility ROAS = Revenue / Media Spend
The Predictive Model represented uses the benchmarked correlation between CPC and ROAS to determine the optimal spend by Ad Group. As
a first step to analyze campaign optimization, the model will calculate the optimal budget spend by Ad Group for maximum returns if overall
campaign CPC performance continues to perform at or below the maximum threshold of the historic CPC benchmark performance enabling a
more efficient media buy and budget.
Of Note: the model’s statistical analysis assumes if average CPC rises, ROAS will decrease, and conversely if average CPC decreases, ROAS will
increase. Therefore, the model by changing the defined variable constraints will find the baseline optimization solution with upper and lower
thresholds for a campaign’s acceptable CPC performance as measured against acceptable ROAS gains to fully analyze strategic alignment with all
required (as applicable) dynamic campaign performance considerations measured against desired outcomes (e.g., minimum Ad Group inclusion,
budget share spend, minimum/maximum budget, etc.).
Upper and lower thresholds can be used in ongoing campaign analysis and optimization and be further used as alerts for replicating best
practices or refreshing campaign executable dynamics such as Ad Groups, placement, creative, messaging, imagery, targeting, and more.

4. Model Baseline Optimization Results
Solving Method #1: GRG Nonlinear Statistical Algorithm
Represented Marketing Channel
Paid Media (Display Advertising)
Represented Business Objective
Improve return on media spend (ROAS)
Variable Constraints
No change to benchmark variable constraints
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(Baseline Analysis #1)
ROAS Optimization Problem Specifics
Maximize ROAS (Return On Ad Spend)
1. Find the best Ad Group budget spend mix to maximize ROAS.
2. Optimize current campaign performance using the current eta
benchmark of 6.71 (maximum assumed risk for CPC performance
volatility).
3. Allocate no more than 30% of total budget spend to any single
Ad Group per historic campaign performance.
4. Maintain current media buy diversification by including all (10)
Ad Groups in final model outcome.
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Model Optimization Outcome
Using historical benchmarked performance constraints:
(10) Ad Groups, 6.71 Beta (campaign CPC performance), 30% max.
budget share per any single Ad Group…
…a 40% lift in ROAS performance can be realized by simply
reallocating budget spend across the Ad Groups.
Proprietary Design of Daniel McKean | linkedin.com/in/danielmckean
The Predictive Model represented uses the benchmarked correlation between CPC and ROAS to determine the optimal spend by
Ad Group. As a first step to campaign optimization, the model will calculate the optimal budget spend by Ad Group for maximum
returns if overall campaign CPC performance continues to perform at or within a minimal threshold below the historic benchmark
enabling a more efficient media budget.