Representation of building a predictive model to optimize the funnel to conversions.

1. 1 | P a g e M e a s u r e m e n t & A n a l y t i c s | D a t a B a c k b o n e T e a m
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Seresto | YTD | Channel - Media | Baseline Performance
Reviewing Seresto's baseline performance based solely on this Channel Performance data view, we can clearly
see Display outperforms all channels for impressions, clicks and ultimately conversions, whereas digital video
consumes a large share of the budget but underperforms for clicks and conversions.
This basic baseline view suggests the prime KPI and Conversion is likely different for each Channel and for each
Funnel Objective. Therefore, to find spend efficiencies for our media budget, we will need to run modeling at a
deeper level to consider Channel + Platform + Objective.
The net takeaway (which applies to virtually all modeling) is analysis of performance for a single dimension in
isolation without linking other dimensions becomes less meaningful with fewer insights. And in the case of
Elanco's media spends, it may be further complicated when Primary Channel and/or Platform KPIs are
considered.
Channel Percent Spend Percent Impressions Percent Clicks Percent Conversions
Digital Audio 2.01 % 1.38 % 0.09 % 0.20 %
Digital Display 31.78 % 71.72 % 79.12 % 58.90 %
Digital Search 6.87 % 0.14 % 3.28 % 25.71 %
Digital Social 4.55 % 2.17 % 4.12 % 0.78 %
Digital Video 35.13 % 20.71 % 6.27 % 8.30 %
N/A 1.86 % 0.42 % 0.88 % 0.77 %
Retailer Websites 17.80 % 3.47 % 6.25 % 5.32 %
Grand Total 100.00% 100.00% 100.00% 100.00%
Brand Media Cost Impressions Clicks Conversions
Galliprant Baseline $11,954,392.84 1,778,627,237 6,938,517 2,458,172
Digital Audio $240,603.50 24,500,192 5,992 5,033
Digital Display $3,799,591.61 1,275,567,798 5,489,447 1,447,804
Digital Search $820,806.88 2,487,697 227,654 632,106
Digital Social $543,991.78 38,519,375 285,523 19,275
Digital Video $4,199,175.83 368,283,840 435,157 204,125
N/A $222,549.79 7,545,040 61,068 18,954
Retailer Websites $2,127,673.45 61,723,295 433,676 130,875
To effectively build predictive models, we will need to build a linked dataset to include Objective, Channel and
Platform to analyze how baseline spends could be optimized by Objective for future consideration.
To represent what is possible, a Data Model proof case follows for Seresto to demonstrate how baseline
performance metrics can be used for predicting future media spend outcomes.
Seresto | Sample Predictive Modeling (Media Optimization)
Jan. 01 - Nov. 09, 2021

2. 2 | P a g e M e a s u r e m e n t & A n a l y t i c s | D a t a B a c k b o n e T e a m
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Brand: Seresto | BASELINE OPTIMIZATION TEST 1 Demonstration
Media Objective: AWARENESS
Data Model Technique: Evolutionary Algorithm
Goal: Analyze media performance by CHANNEL placement spend based on 2021 YTD benchmarks without changing spend
and/or channel inclusion parameters to compare legacy performance against the model's predicted outcome.
Objective: Optimize for Conversion by Channel Spend for the Brand AWARENESS Objective.
What would spend look like by Channel and Platform if we optimized for conversion without changing total media
budget allocation while maintaining all channels and platforms in the media mix.
Model Assumption: A correlation exists between channel spend efficiencies, and impressions, clicks, and CPC which
ultimately lead to greater conversions.
Modeling Setup Parameters
Constraint Criteria - Evolutionary Model - Baseline Optimization Test 1
Target Beta (Max Assumed CPC Risk) $3.63
Average CPC used as the assumed dynamic Beta (b) risk variable coefficient.
(Find an optimum solution with an average CPC at or below $0.62.)
Max Budget Share 0.25
Maximum spend allocation for a single dynamic variable (%)
(Maximum spend per channel placement.)
Min Target Pools Inclusion 16
Minimum inclusion of dynamic variables in model calculation.
(Channel placements.)
Target Pool Inclusion Count 16
Total count of all dynamic variables in model calculation.
(Channel placements.)
Budget Spend $5,694,516
Total budget to be applied in model calculation.
(Benchmark budget spend to represent potential gains in model prediction.)
Constant Model Variables CTR, CPM, CPA, CVR
Data Model Outcome on following page.
Seresto | Sample Predictive Modeling (Media Spend Optimization)

3. 3 | P a g e M e a s u r e m e n t & A n a l y t i c s | D a t a B a c k b o n e T e a m
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Brand: Seresto | Data Model | Predicted Outcome
Media Objective: AWARENESS | Based on Defined Constraint Criteria
Channel Platform Media Cost $$ Share Impressions Clicks CPC Conversions
Digital Audio Spotify $5.73 0.00000 583 0 $40.15 0
Digital Video Amobee $1.68 0.00000 42 0 $4,792.99 0
Digital Video Spotify $30.89 0.00001 536 3 $9.80 1
Digital Video Reddit $5.05 0.00000 1,169 1 $5.42 0
Digital Video DV360 $1,423,625.06 0.25000 194,999,916 287,877 $4.95 135,774
Digital Video ABC $2.97 0.00000 0 0 $0.00 0
Digital Video CBS Local $53.01 0.00001 0 0 $0.00 0
Digital Video
NBC
Broadband
$10.62 0.00000 0 0 $0.00 0
Digital Video Hulu $2.54 0.00000 89 0 $0.00 0
Digital Video
Pandora
Streaming
$1.00 0.00000 10 0 $0.00 0
Digital Social Facebook $7.76 0.00000 2,989 4 $1.96 0
Digital Display
Pandora
Streaming
$974.25 0.00017 61,120 100 $9.70 67
Digital Display Spotify $1,408.32 0.00025 97,946 198 $7.10 94
Digital Display WebMD $1,423,627.23 0.25000 89,851,200 214,184 $6.65 189,838
Digital Display Reddit $1,421,111.50 0.24956 322,647,358 555,057 $2.56 130,355
Digital Display DV360 $1,423,628.95 0.25000 984,665,008 1,748,429 $0.81 1,345,164
Model Prediction Core KPIs $5,694,496.54 1.00000 1,592,327,966 2,805,855 $2.03 1,801,293
YTD Comparative Baseline $5,694,515.94 1.00000 1,027,718,204 1,570,773 $3.63 1,058,139
Model Prediction Gain | Loss constraint constraint 55% 79% -44% 70%
This PREDICTIVE Model LEVERAGING BASELINE MEASURED PERFORMANCE DATA demonstrates significant
performance gains could be realized for each BRAND OBJECTIVE simply by adjusting media placement spends when
aligned to optimizing for conversion. (A final results table after demonstration results showcases final potential
impact.)
Without any other media buy consideration, Impressions, Clicks, CPC and Conversions all improve by optimizing
channels and platforms for Conversion to eliminate inefficiencies in media spend.
The Model's design is flexible and capable for adjusting to specified conditional media execution criteria.
Seresto | Sample Predictive Modeling (Media Spend Optimization)

4. 4 | P a g e M e a s u r e m e n t & A n a l y t i c s | D a t a B a c k b o n e T e a m
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Brand: Seresto | BASELINE OPTIMIZATION TEST 2 Demonstration
Media Objective: ACQUISITION
Data Model Technique: Evolutionary Algorithm
Goal: Analyze media performance by CHANNEL placement spend based on 2021 YTD benchmarks without changing spend
and/or channel inclusion parameters to compare legacy performance against the model's predicted outcome.
Objective: Optimize for Conversion by Channel Spend for the Brand ACQUISITION Objective.
What would spend look like by Channel and Platform if we optimized for conversion without changing total media
budget allocation while maintaining all channels and platforms in the media mix.
Model Assumption: A correlation exists between channel spend efficiencies, and impressions, clicks, and CPC which
ultimately lead to greater conversions.
Modeling Setup Parameters
Constraint Criteria - Evolutionary Model - Baseline Optimization Test 1
Target Beta (Max Assumed CPC Risk) $3.92
Average CPC used as the assumed dynamic Beta (b) risk variable coefficient.
(Find an optimum solution with an average CPC at or below $0.62.)
Max Budget Share 0.625
Maximum spend allocation for a single dynamic variable (%)
(Maximum spend per channel placement.)
Min Target Pools Inclusion 5
Minimum inclusion of dynamic variables in model calculation.
(Channel placements.)
Target Pool Inclusion Count 5
Total count of all dynamic variables in model calculation.
(Channel placements.)
Budget Spend $2,853,038
Total budget to be applied in model calculation.
(Benchmark budget spend to represent potential gains in model prediction.)
Constant Model Variables CTR, CPM, CPA, CVR
Data Model | Predicted Outcome Based on Defined Constraint Criteria | Spend Allocation Optimization Test 2
Channel Platform Media Cost $$ Share Impressions Clicks CPC Conversions
Retailer Websites Amazon $1,072,172.66 0.37580 27,074,942 229,756 $4.67 72,700
N/A Amazon $15,701.99 0.00550 576,214 5,053 $3.11 1,626
Digital Search Google Ads $851,323.03 0.29839 2,530,667 252,822 $3.37 770,911
Digital Search Bing Ads $898,166.79 0.31481 5,236,841 274,462 $3.27 1,027,517
Digital Social Facebook $15,673.54 0.00549 518,480 5,972 $2.62 976
Model Prediction Core KPIs $2,853,038.02 1.00000 35,937,143 768,065 $3.71 1,873,729
YTD Comparative Baseline $2,853,038.02 1.00000 62,303,476 726,904 $3.92 725,649
Model Prediction Gain | Loss 0% 0% -42% 6% -5% 158%
Seresto | Sample Predictive Modeling (Media Spend Optimization)

5. 5 | P a g e M e a s u r e m e n t & A n a l y t i c s | D a t a B a c k b o n e T e a m
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Brand: Seresto | BASELINE OPTIMIZATION TEST 3 Demonstration
Media Objective: CONSIDERATION
Data Model Technique: Evolutionary Algorithm
Goal: Analyze media performance by CHANNEL placement spend based on 2021 YTD benchmarks without changing spend
and/or channel inclusion parameters to compare legacy performance against the model's predicted outcome.
Objective: Optimize for Conversion by Channel Spend for the Brand CONSIDERATION Objective.
What would spend look like by Channel and Platform if we optimized for conversion without changing total media
budget allocation while maintaining all channels and platforms in the media mix.
Model Assumption: A correlation exists between channel spend efficiencies, and impressions, clicks, and CPC which
ultimately lead to greater conversions.
Modeling Setup Parameters
Constraint Criteria - Evolutionary Model - Baseline Optimization Test 1
Target Beta (Max Assumed CPC Risk) $3.09
Average CPC used as the assumed dynamic Beta (b) risk variable coefficient.
(Find an optimum solution with an average CPC at or below $0.62.)
Max Budget Share 0.40
Maximum spend allocation for a single dynamic variable (%)
(Maximum spend per channel placement.)
Min Target Pools Inclusion 6
Minimum inclusion of dynamic variables in model calculation.
(Channel placements.)
Target Pool Inclusion Count 6
Total count of all dynamic variables in model calculation.
(Channel placements.)
Budget Spend $850,596
Total budget to be applied in model calculation.
(Benchmark budget spend to represent potential gains in model prediction.)
Constant Model Variables CTR, CPM, CPA, CVR
Data Model | Predicted Outcome Based on Defined Constraint Criteria | Spend Allocation Optimization Test 2
Channel Platform Media Cost $$ Share Impressions Clicks CPC Conversions
Retailer Websites Amazon $34,241.85 0.04026 1,660,987 5,120 $6.69 988
N/A Amazon $36,703.41 0.04315 857,483 3,506 $10.47 578
Digital Search Google Ads $340,238.59 0.40000 986,237 72,555 $4.69 106,873
Digital Search Bing Ads $340,238.59 0.40000 5,744,765 154,513 $2.20 410,244
Digital Social Pinterest $0.30 0.00000 51 0 $1.71 0
Digital Social Facebook $100,151.75 0.11774 5,073,596 72,560 $1.38 1,424
Model Prediction Core KPIs $851,574.49 1.00115 14,323,119 308,255 $2.76 520,107
YTD Comparative Baseline $850,596.47 1.00000 43,587,069 275,209 $3.09 75,494
Model Prediction Gain | Loss 0% 0% -67% 12% -11% 589%
Seresto | Sample Predictive Modeling (Media Spend Optimization)
$$ Share: decimal rounding digit representation

6. 6 | P a g e M e a s u r e m e n t & A n a l y t i c s | D a t a B a c k b o n e T e a m
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Brand: Seresto | BASELINE OPTIMIZATION TEST 4 Demonstration
Media Objective: REVENUE/SALES
Data Model Technique: Evolutionary Algorithm
Goal: Analyze media performance by CHANNEL placement spend based on 2021 YTD benchmarks without changing spend
and/or channel inclusion parameters to compare legacy performance against the model's predicted outcome.
Objective: Optimize for Conversion by Channel Spend for the Brand REVENUE/SALES Objective.
What would spend look like by Channel and Platform if we optimized for conversion without changing total media
budget allocation while maintaining all channels and platforms in the media mix.
Model Assumption: A correlation exists between channel spend efficiencies, and impressions, clicks, and CPC which
ultimately lead to greater conversions.
Modeling Setup Parameters
Constraint Criteria - Evolutionary Model - Baseline Optimization Test 1
Target Beta (Max Assumed CPC Risk) $0.59
Average CPC used as the assumed dynamic Beta (b) risk variable coefficient.
(Find an optimum solution with an average CPC at or below $0.62.)
Max Budget Share 0.88
Maximum spend allocation for a single dynamic variable (%)
(Maximum spend per channel placement.)
Min Target Pools Inclusion 2
Minimum inclusion of dynamic variables in model calculation.
(Channel placements.)
Target Pool Inclusion Count 2
Total count of all dynamic variables in model calculation.
(Channel placements.)
Budget Spend $2,556,242
Total budget to be applied in model calculation.
(Benchmark budget spend to represent potential gains in model prediction.)
Constant Model Variables CTR, CPM, CPA, CVR
Data Model | Predicted Outcome Based on Defined Constraint Criteria | Spend Allocation Optimization Test 2
Channel Platform Media Cost $$ Share Impressions Clicks CPC Conversions
Digital Display Epsilon $311,830.33 0.12199 48,373,832 27,451 $11.36 20,019
Digital Display DV360 $2,249,493.32 0.88000 597,810,180 4,344,914 $0.52 579,857
Model Prediction Core KPIs $2,561,323.65 1.00199 646,184,012 4,372,365 $0.59 599,876
YTD Comparative Baseline $2,556,242.41 1.00000 645,018,488 4,365,631 $0.59 598,890
Model Prediction Gain | Loss 0% 0% 0.2% 0.2% 0% 0.2%
Of note: model found no significant gains based on current channel/platform selection indicating spend is already
aligned for best optimization outcomes for the Revenue/Sales objective.
Seresto | Sample Predictive Modeling (Media Spend Optimization)
$$ Share: decimal rounding digit representation

7. 7 | P a g e M e a s u r e m e n t & A n a l y t i c s | D a t a B a c k b o n e T e a m
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Brand: Seresto
BASELINE OPTIMIZATION TEST ROLLUP COMPARATIVE RESULTS
Media Objectives: AWARENESS, ACQUISITION, CONSIDERATION, REVENUE/SALES
Data Model Technique: Evolutionary Algorithm
Predictive Modeling Impact
When we model each campaign objective separately, we can begin to find deeper inefficiencies in media spend. The model
demonstration reveals significant performance gains can be reasonably realized simply by optimizing to benchmarked
platform performance.
Of note, the benchmarking period used within this demonstration was longer than would be recommended, but was used
to demonstrate the potential of performance gains using a sizeable budget allocation. Typically, it would be advised
optimization and modeling occur no frequent than in 90-day cycles. This allows for media to perform to the best possible
outcomes leveraging platform AI algorithms. Optimizing too frequent in shorter time cycles can have a reverse intended
effect with poorer performance as the platforms are continuously attempting to re-optimize based on changing spends
aligned with selected objectives and performance metrics.
The real power behind this demonstration is that we can show the baseline "what if" scenario if we didn't change media
buy parameters. Larger gains, much larger gains, could potentially be realized if we were to purely optimize to the best
performing channels and platforms.
And, for consideration, we did not apply audience targeting to the model or any other performance dimension such as
creative, format, messaging, CTA et al - all of which could potentially allow us to optimize deeper for each objective,
channel and platform.
As the data transformation effort begins to be adopted, data analysis and performance optimization does
become a highly strategic and valued direction for greater marketing performance and business ROI.
Modeled Objective Media Cost Impressions Clicks CPC Conversions
Awareness Model $5,694,496.54 1,592,327,966 2,805,855 $2.03 1,801,293
YTD Awareness Baseline $5,694,515.94 1,027,718,204 1,570,773 $3.63 1,058,139
Acquisition Model $2,853,038.02 35,937,143 768,065 $3.71 1,873,729
YTD Acquisition Baseline $2,853,038.02 62,303,476 726,904 $3.92 725,649
Consideration Model $851,574.49 14,323,119 308,255 $2.76 520,107
YTD Consideration Baseline $850,596.47 43,587,069 275,209 $3.09 75,494
Revenue/Sales Model $2,561,323.65 646,184,012 4,372,365 $0.59 599,876
YTD Revenue/Sales Baseline $2,556,242.41 645,018,488 4,365,631 $0.59 598,890
Model Objective Totals $851,574.49 2,288,772,240 8,254,540 $0.10 4,795,005
YTD Comparative Baseline Totals $850,596.47 1,778,627,237 6,938,517 $0.12 2,458,172
Optimization Impact 0% 29% 19% -16% 95%
Seresto | Sample Predictive Modeling (Media Optimization Impact)

8. 8 | P a g e M e a s u r e m e n t & A n a l y t i c s | D a t a B a c k b o n e T e a m
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CHANNEL | PLATFORM | OBJECTIVE | AUDIENCE | CAMPAIGN NAME | MEDIA BUY SIZE | CREATIVE FORMAT | PRIMARY KPI
Data Modeling Impact
We can use data modeling in an assortment of ways across digital marketing. For paid media, if we have the data points
linking attribution dimensions to metric performance, we can likely build a model to optimize future spends.
In the examples just demonstrated, significant performance gains were predicted for all leading KPIs. In the case of
allowing the model to optimize spend by Objective and Platform, we have demonstrated double digit gains for impressions
and clicks, reduced CPC by as much as 16 percent, and improved conversion gains by as much as 96%.
The data used which was linked and cross-tabbed into a separate dataset for exploration as shown on the following page.
Upon request, we can explore the data using data modeling to demonstrate to interested key stakeholders how we can
predict "best buy" scenarios for future media spends.
Deeper multi-dimensional ACTIONABLE analyses based on a 90-day benchmarks which can be explored include:
� channel (as demonstrated),
� channel + platform,
� channel + platform + objective (as demonstrated),
� channel + platform + objective + audience, or
� any combination of dimensions which can be linked to data metrics.
As more expansive data becomes available and ingested into the dashboard environment including rebates and revenues,
the same process could be applied to gather additional business insights and intelligence for maximizing marketing ROI and
steering 2022 strategic business and marketing directions.
With adoption, Data Modeling Best practices include:
1. Clearly define and understand the desired optimization objective.
2. Identify the dimensions and metrics that are directly correlated to the objective's performance.
3. Define a baseline benchmark period to measure and compare predicted results (most recent 90 days optimal).
4. Collect the required data and groom as needed into a workable dataset.
5. Build, test and validate the data model.
6. Define the model's constraint criteria (applicable considerations, limitations, restrictions).
7. Develop a list of the "what if" scenarios based on the constraint criteria.
8. Run and log the "what if" scenario results to find the best possible outcome.
9. Implement the parameters defined for the best chosen optimization outcome.
10. Allow the applied changes sufficient time to re-adjust and optimize by platform and where applicable for AI
algorithms.
11. Continue to measure ongoing performance.
12. Measure, adjust and/or re-optimize periodically per scheduled timelines.
Final Notes | Predictive Modeling (Media Optimization)

9. 9 | P a g e M e a s u r e m e n t & A n a l y t i c s | D a t a B a c k b o n e T e a m
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2021 YTD Multi-Dimensional Baseline Paid Media Performance x Channel, Objective, Platform
Brand Media Cost Impressions Clicks CTR CPC CPM CPA Conversions CVR
Galliprant Baseline $6,466,681.92 2,102,527,024 10,553,494 0.50% $0.61 $3.08 $6.25 1,034,899 9.81%
Digital Audio $201,776.20 18,699,108 96,690 0.52% $2.09 $10.79 $5.42 37,197 38.47%
Awareness $201,776.20 18,699,108 96,690 0.52% $2.09 $10.79 $5.42 37,197 38.47%
Pandora Stream $125,302.61 12,219,029 94,727 0.78% $1.32 $10.25 $3.38 37,074 39.14%
Spotify $76,473.59 6,480,079 1,963 0.03% $38.96 $11.80 $621.74 123 6.27%
Digital Display $1,261,827.30 1,359,828,708 1,067,852 0.08% $1.18 $0.93 $1.67 757,780 70.96%
Awareness $1,261,827.30 1,359,828,708 1,067,852 0.08% $1.18 $0.93 $1.67 757,780 70.96%
DV360 $1,147,274.66 1,338,535,865 992,910 0.07% $1.16 $0.86 $1.55 740,516 74.58%
Spotify $114,552.64 9,112,188 57,410 0.63% $2.00 $12.57 $8.00 14,316 24.94%
Kargo $0.00 1,974,467 552 0.03% $0.00 $0.00 $0.00 756 136.96%
PadSquad $0.00 1,851,648 5,640 0.30% $0.00 $0.00 $0.00 322 5.71%
Reddit $0.00 8,354,540 11,340 0.14% $0.00 $0.00 $0.00 1,870 16.49%
Digital Search $550,312.91 2,016,101 91,405 4.53% $6.02 $272.96 $8.98 61,293 67.06%
Acquisition $274,609.07 1,328,249 56,027 4.22% $4.90 $206.75 $11.42 24,054 42.93%
Google Ads $220,192.06 828,768 31,531 3.80% $6.98 $265.69 $9.20 23,937 75.92%
Bing Ads $54,417.01 499,481 24,496 4.90% $2.22 $108.95 $465.10 117 0.48%
Consideration $275,703.84 687,852 35,378 5.14% $7.79 $400.82 $7.40 37,239 105.26%
Google Ads $269,723.33 632,913 33,801 5.34% $7.98 $426.16 $7.25 37,201 110.06%
Bing Ads $5,980.51 54,939 1,577 2.87% $3.79 $108.86 $157.38 38 2.41%
Digital Social $1,538,367.55 349,654,194 8,441,948 2.41% $0.18 $4.40 $656.86 2,342 0.03%
Awareness $754,419.89 265,848,211 788,532 0.30% $0.96 $2.84 $1,022.25 738 0.09%
Facebook $467,501.70 140,810,782 225,065 0.16% $2.08 $3.32 $834.82 560 0.25%
Instagram $286,918.19 125,037,429 563,467 0.45% $0.51 $2.29 $1,611.90 178 0.03%
Consideration $783,947.66 83,805,983 7,653,416 9.13% $0.10 $9.35 $488.75 1,604 0.02%
Facebook $62,795.65 2,463,208 68,625 2.79% $0.92 $25.49 $119.16 527 0.77%
Instagram $721,152.01 81,342,775 7,584,791 9.32% $0.10 $8.87 $669.59 1,077 0.01%
Digital Video $2,914,397.96 372,328,913 855,599 0.23% $3.41 $7.83 $16.53 176,287 20.60%
Awareness $2,914,397.96 372,328,913 855,599 0.23% $3.41 $7.83 $16.53 176,287 20.60%
Hulu $460,383.98 17,208,111 2 0.00% $230,191.99 $26.75 $46,038.40 10 500.00%
Amobee $1,198,827.04 45,855,741 1,019 0.00% $1,176.47 $26.14 $4,610.87 260 25.52%
DV360 $1,255,186.94 301,520,325 835,013 0.28% $1.50 $4.16 $7.22 173,965 20.83%
Reddit $0.00 7,744,736 19,565 0.25% $0.00 $0.00 $0.00 2,052 10.49%
Channel Benchmarks $6,466,681.92 2,102,527,024 10,553,494 0.5019% $0.61 $3.08 $6.25 1,034,899 9.81%
Objective Benchmarks $6,466,681.92 2,102,527,024 10,553,494 0.5019% $0.61 $3.08 $0.00 1,034,899 9.81%
Sample Dataset for Data Modeling (Media Optimization)