Outside Brand Data Audit

Data
Auditor:
Daniel McKean
DATA ANALYTICS NEEDS
ASSESSMENT AUDIT
Objective: Contracted as an outside auditor, the objective was to perform an analysis for
opportunities to enhance data analytics and measurement of campaign performance for
several Elanco pet brands from available data repositories including evolving dashboard
environments and brand websites.

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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OBJECTIVE & PURPOSE
Baseline Review | This initial outside audit, a simple snapshot in time, should be used to serve as a preliminary
step to begin an internal discussion to document what data is being collected, what data is available, what data
can be collected, and what data is missing to align the digital transformation to business impact.
• It should be used to be a preliminary discovery guide for what data can be explored for deeper insights and
business intelligence for which corporate executives, brand managers and other key stakeholders alike can
leverage data analysis for steering strategic marketing directions and business initiatives.
• It should be used to identify advance opportunities for applying data, data analytics and data process to
measure ongoing marketing and business performance as a measurable business process function for
impacting positive future business outcomes as part of the Digital Transformation initiative.
• It should be used to identify digital transformation needs and requirements which impact data frameworks,
methodologies and processes.
• It should be used to identify relevant and meaningful data dimensions and metrics to guide marketing
initiatives and the marketing/sales funnel.
• It should be used to optimize the user experience across digital touchpoints in the consumer journey.
• It should be used to help align data requirements to the BI dashboard beneficial for stakeholder groups at all
levels, while advancing build cycles linking meaningful measurable marketing and website performance
metrics to business outcomes across the entire digital ecosystem.
The audit is not intended to be expansive or an all-inclusive analysis. It's simply a baseline preview to demonstrate
the potential of data analytics when applied to digital marketing and the digital ecosystem. Many of the
recommendations made are for deeper analyses in specific operational areas. As priorities are established, these
recommendations can be pursued where value is placed.
The audit was performed by reviewing top-line organizational level and Brand progress for expanding its presence
into the digital sphere for the benefit of business operations using a data analytics lens. The scope includes a
baseline review of the marketing Datorama dashboard, as well as a review for active media campaign performance
and website performance for several leading key U.S. Brands including Galliprant and Seresto.
As a result of this review, considerations and opportunities for advancing Elanco's digital marketing planning and
measurement processes surfaced. Insights have been presented for marketing funnels, campaign goals, business
and marketing KPIs, stakeholder group level reporting needs, and generally data collection to optimize business
and marketing performance.
The information presented has been summarized by Key Insights and Findings with supporting detailed
information. Resolutions to areas of concern along with recommendations and opportunities have been identified
to complement the analysis conducted and to make this document as actionable as possible.
DATA BACKBONE TEAM CLARIFICATION
PREFACE

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Key Insights & Findings
• Marketing Funnel Considerations | Funnel ambiguity exists for objectives and prime KPI at each Funnel stage.
There may not be a consistent funnel view across Brand Groups as to stages and KPIs. What is required is universal
agreement as to what the Funnel looks like and the desired measurable goal (user action) at each stage.
Otherwise, it is hard to analyze marketing performance (as well as media performance) for funnel impact. We
simply need an organizational-wide adopted standard to effectively measure and optimize marketing/sales
performance.
Resolution: Adopt an organizational-wide Funnel model.
• Campaign Considerations | Brand Campaigns are loosely defined aligned to Funnel Stage as opposed to more
traditional Campaign planning aligned to time period goals, objectives or actions leveraging targeting and
messaging.
Campaign planning by Objective is workable, but still requires a naming and tracking convention to isolate
measurement for executable dimensional components including placement, targeting, creative, CTAs, and
messaging.
Resolution: Develop and implement a defined Campaign tracking process with adopted naming convention
standards to more accurately measure media and other marketing channel performance.
• Data Considerations | Data ingestion into the dashboard is a mix of near-real-time and time-delayed data feeds
based on source data. This in itself presents problems for the entire dashboard data ecosystem for building
confidence in dashboard views.
Resolution: Perform a formal audit of all data feeds to find discrepancies and any time delays in the data
ingestion process as well as missing datasets, and work with the Media Agency to enhance data feeds.
• Data Considerations | Silo 'ed single dimensional dashboard data views are good for Brand Managers, but less
useful for analysts. Currently, we do have the ability to link and cross tabulate multi dimensions for more holistic
deeper analysis of performance for optimization.
Resolution: Two dashboard data view levels are needed, and which may require separate dashboards. Brand
Manager data views need to be simple and provide the top-line Campaign performance linked to business
impact. Analysts require linked dimensional views which could be designed by leveraging the Jellyfish provided
data map to identify dimensions and metrics that need to be linked for more efficient analysis.
• Stakeholder Considerations | One set of KPIs does not fit all stakeholders, nor does a scheme of roll-up
dashboards which incorporate many different filterable metric views. (KISS)
Resolution: Evolve the dashboard into multi-level dashboard environment for stakeholder group levels and
provide the data views most appropriate and valued by stakeholder role and need (and as applicable linked to
marketing execution and/or business impact).
AUDIT FINDINGS SUMMARY | MEDIA

Key Insights & Findings (continued)
• Business and Marketing KPIs Considerations | Awareness > engagements > leads > unqualified leads > qualified
leads > conversion > sales/revenue > advocacy all impact the funnel and which specifically influence the business
bottom line. Linking the most relevant metrics and KPIs to each of these journey (funnel) steps and to business
impact is essential.
Resolution: The Prime KPIs for each Funnel (Objective) Stage needs to be clearly identified, otherwise we are
unable to truly analyze marketing and business ROI from our marketing spends. We need to adopt standardized
Primary KPIs for each Funnel Objective (stage) which is measurable and meaningful for Corporate Executives
and Brand Managers, and which would aid in more rapid digital transformation.
Primary KPIs: e.g., Awareness > CPL or CPC | Consideration > CPE | Conversion > CPA + ROAS
Also, being able to leverage planned revenue lift models and response curves will further aid in calculating CPA.
� Awareness > Impression > Bounce > CPC
� Awareness > Unqualified Lead > non-Bounce > CPL
� Consideration > Pre-Qualified Lead > Event Clicks > CPE
� Conversion > Qualified Lead > Conversion Pixel > CPA
� Business ROI > Revenue Impact > Revenue Lift > ROAS
� BOF Activism > Opt-In Comms + CTAs > Tracking + Measure TBD > Subscribers + ER
� BOF Loyalty > Accounts + Rebates + Coupons > Tracking + Measure TBD > CLTV
• Dashboard Considerations | In its current development state, the dashboard build process has progressed far
enough along to be able to review the build cycle from a stakeholder user perspective. Dashboard performance,
metric, views, layout - all needs a fresh set of eyes using the lens of an average Brand Manager or Corporate
Executive to refine the LAUNCH environment for maximum organizational adoption.
Resolution: Plan a staged, roadmapped approach to auditing the dashboard environment taking into
consideration the milestone date for launch. Foremost initiative may be to quickly realign dash views (layout
flows) for greatest benefit to Brand Managers.
• Media Spend Considerations | Media spend pacing across brands have anomalies month to month with a recent
negative monthly trend pattern. Accumulative monthly media spend lags behind planned budgeting with the gap
expanding over the last (4) months. If this were a re-optimization issue, platforms via their AI would typically re-
adjust the spend in a few weeks with additional human oversight which should have corrected the pacing.
Volatility in monthly spends in itself likely impact improving performance and platform gains as budgets are
increased and reduced (if this occurs too often).
Resolution: Set up dashboard alerts for notifying Brand Managers for pacing anomalies. May also consider
annotation features of dashboard environment to provide explanatory notes to anomalies as they occur.
.

• Media Baseline Benchmarking Considerations | Benchmarking can be performed at different intervals based on
analytical needs: e.g., YTD, 90-Day, MoM, seasonality, defined milestone periods, other. The dashboard currently
uses MoM as the baseline comparison which is good to show MoM lifts or losses.
Of note, 90-day benchmarks are most appropriate for optimizing paid media as it's an indicator for real-time
recent performance with relevance for longevity to consider common peaks and valleys in performance.
The metric consideration uncovered for our current benchmarking is the definition of Conversion. What is
Conversion for each Funnel Stage and as used for Campaign classification? The valued Conversion point for each
Funnel Objective is somewhat undefined, which when applied and reported as a CPA becomes misleading.
Resolution: Need to adopt a standard Prime KPI as the user conversion point as aligned to the strategic planning
behind media spend allocation and represent the Prime KPI for each Funnel Objective, e.g., CPL (awareness),
CPE (consideration), CPA (acquisition).
• Galliprant vs. Seresto Media Performance Considerations | Media performance analysis offers the ability to
monitor Agency directions for media placement and buys for oversight as Brand Managers fully rely on the Agency
to execute their Campaigns.
A baseline 90-Day comparative snapshot review versus YTD for Galliprant vs. Seresto media performance (below)
reveals inconsistent performance.
o 90-Day SOV for both Brands has decreased in media spend compared to YTD as part
of Group Brand overall spends.
o Galliprant impressions and CPM remained stable, while Seresto had negative
volatility.
o Seresto enhanced click performance (clicks and CTR) on fewer impressions, while
Galliprant remained stable.
o CPC remained stable (reasonably for Seresto) for both Brands.
o Conversion, CPA and CVR all underperformed for both Brands compared to YTD
baselines representing changing dynamics or parameters in the media buy.
Opportunity: Post Campaign Analysis | The opportunity via consistent media analysis is to become a key
strategic partner for Brand Managers to report on and to optimize their media performance. In essence, become
part of their team to provide valued support for steering strategic execution of their media efforts.
The direction to pursue is to use the 2021 Post Campaign Analysis (renamed Campaign Performance) to build an
Executive Summary Dashboard (Corporate Executives + Brand Managers) representing roll-up + filterable top-
line relevant Campaign Performance metrics linked directly to business and marketing funnel outcomes.

• Predictive Modeling Support | The demonstration provided within this audit (below) showcases how ongoing
media campaigns can be optimized for maximum performance. Although the Agency is contractually optimizing
the media buys, an overseer providing this modeling expertise afford the Brand Managers opportunities to be an
active participant in the execution of the Campaigns and not simply a bystander on the sidelines.
It further affords opportunities for strategic planning to consider the "what if" scenarios as to the impact of altered
media spend and performance on the Brand's Funnel Objectives and the Consumer Touchpoint Journey.
We can see from the modeling demonstration results for Seresto, and based on optimizing for Conversion at each
Funnel Stage, we potentially could realize significant gains across all leading performance metrics and ultimately
the Conversion KPI.
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.
How we leverage this modeling technique for benefit of Brand Managers is yet to be determined. However, it gives
Brand Managers insight into how media can perform which may assist in strategic planning. And if we were able to
integrate modeling into the dashboard environment, it may empower Brand Managers even more while upskilling
their knowledge and expertise in the area of paid media.
Opportunity: There may be an API or other data ingestion process for the dashboard to enable "live" modeling
for Brand Managers to aid in their strategic planning - all within the dashboard environment which may aid in
more rapid dashboard use and adoption.
As more expansive data becomes available and ingested into the dashboard environment including varied sales
and revenue performance metrics, the same process could be applied to gather additional business insights and
intelligence for maximizing marketing ROAS and Business ROI while steering 2022 strategic business and marketing
directions.
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 | Gains - Losses 0% 29% 19% -16% 95%

• Website Benchmarking | Overall, the snapshot website review of Google Analytics, the Branded websites, and the
Datorama dashboard reveals the sites may not be fully optimized for the click funnel path and general site
performance and traffic is somewhat volatile with up and down swing patterns.
Opportunity: Expansive Formalized Website Audit | As resources become available, or via a desired direction, a
full website audit of select brands could be performed. The value to Elanco and Brand Managers would be
added confidence the websites are designed for UX and the click path for maximum benefit. It would further
ensure Google Analytics is properly set up to capture the data points required for marketing and business
analysis.
It could further be an advance initiative to gather insights and benchmark performance prior to A/B testing
landing pages, which also affords the opportunity to activate heat mapping as another analytics view.
• Datorama Dashboard Views | Website performance representations use limited filtered views to convey
information that may not be fully actionable for the Brand Managers and Corporate Executives.
Baseline metrics are limited (new visits, bounces, bounce rate) which may not be the most relevant and are not
expansive enough to convey site performance and user influence. Site traffic views by Channel acquisition is a
useful view, but in its current state does not adequately attribute source traffic by Objective which may be the
main view filter for Brand Managers and which much of Campaign planning may be conducted.
Resolution: Ideally, for benefit of Brand Managers in particular, dashboard website views with expanded user
behavior metrics should convey attribution influence and user behaviors for leading Funnel Objectives
(awareness, consideration, acquisition/conversion) highlighting adopted Primary Business Impacting KPIs.
Furthermore, filtering should foremost align to Google default channels with deeper filterable views aligned to
dashboard setup (groups, brands, objectives, channels, platforms).
AUDIT FINDINGS SUMMARY | WEBSITES

• Galliprant Baseline Review | There is a high volatility in user traffic flow. Galliprant as a pain medication does not
have seasonality peaks, and therefore, what this may represent is the impact of inconsistent marketing campaigns,
spends and/or marketing optimization to continually fill the top of the funnel with new site visitors.
Default Channel Attribution is highly skewed to OTHER representing a basic primary need to define campaign
traffic sources which is being categorized into the OTHER Channel. Without clear attribution linking, analyzing site
performance for Funnel Objective and marketing and business ROI, including paid media, makes gathering useful
insights difficult at best.
Resolution: Recommend we work with the GA Admin and New Media Agency to develop a UTM parameter
naming convention that would ensure attribution is assigned properly to the most relevant Default Channel
groupings to give us a much clearer view of how traffic is coming to the website, and visitor behaviors once
arriving which we can then link to Campaign and Objective.
Landing Pages - [Why Galliprant] is the primary destination for Pageviews, which is assumed to be the primary
CTA link for paid media and marketing.
Due to the minimalistic design of the website, the [Why Galliprant] webpage overshadows all pages for site visits.
Assuming this is the primary CTA link in our marketing efforts, it would be expected to see this level of traffic to the
page. However, the flow map reveals visitors are not navigating deeper into the site for more information as less
than 1% of traffic advance to a second page.
Opportunity: If it has not been conducted recently in the past, A/B testing landing pages for greater
engagements might provide value in gathering insights into what content resonates at the highest levels prior to
defined conversion events. The landing page testing might incorporate testing by Campaign Objective in
particular to gauge user resonance of website content at each stage of the Funnel and to aid in enhancing the
click path.
AUDIT FINDINGS SUMMARY | WEBSITES | GALLIPRANT

Galliprant Baseline Review
User Devices | Platform - Of considerable note is the user device preference
for website visits. More than 97% of all traffic is coming via Mobile which has
significant consideration.
Recommended Action: To understand how mobile may impact user site
behaviors and conversion events compared to other device types, the
recommended expanded audit for analyzing site engagements should be
appended with a detailed lens view on mobile user behaviors.
The objective would be to understand insights such as:
� Are our marketing efforts over emphasizing mobile?
� Does the click path as currently designed work for mobile?
� How do mobile engagements differ from desktop?
� How does the mobile design impact conversion events and purchase
intent?

• Galliprant_VET Baseline Review | There's a consistent, steady user traffic flow which aligns with the sessions view.
Visitors appear to be highly pre-qualified where user engagement baseline metrics outpace the consumer website.
Of particular note is Bounce Rate which further indicates site visitors are qualified and are finding content they
expected.
An interesting note within the metrics is the percentage of new users versus returning users. This suggests (with all
considerations in mind for how Google tracks new vs. returning users) that to grow and maintain the Galliprant
business requires a need for continually expanding Brand awareness among this influential community.
Unlike the Consumer website, Organic Search + Direct traffic leads all other channels for traffic attribution. This
infers there is high Brand awareness.
Primary Landing Pages - [Dosing Administer] is the primary destination for Pageviews, with the [Index] page a
leading secondary landing page destination.
The site design is also much more complex than the Consumer site as to be expected. The volume of content is
much more extensive and pageviews and engagements are dependent based on visitor informational needs.
Opportunity: If it has not been conducted in the past, a full content audit should be conducted to understand
what content is of highest value to visitors and is securing the most engagements. Gathering this level of insights
may aid marketing and sales with their communications messaging as well as development of marketing/sales
materials.
AUDIT FINDINGS SUMMARY | WEBSITES | GALLIPRANT_VET

Galliprant_VET Baseline Review
Landing Page | Page Consumption | User Flows - The Users Flow map clearly reveals the "Dosing" topic and
information is of highest visitor value.
What's of note is the Index page has high website entries, but also high drop-offs (69% of landing page arrivals). The
adjusted Bounce Rate (25-seconds as applied on the Consumer site) may not be as applicable here on the professional
site. It may in fact misrepresent a classic Bounce if users are not advancing beyond this landing destination.
Opportunity: A User Behavior Content Analysis would
allow us to dig deeper into site and page engagements
for the impact on Funnel objectives with an analytical
lens of attribution, new vs. returning visitor, landing
destination, page consumption, user engagements
including event triggers, and the conversion events as
defined for business impact.
User Devices | Platform
Unlike the Consumer website, site traffic from desktop
vs. mobile is roughly apportioned equally for device
use, which is sensible. All leading performance metrics
are equally similar with the caveat that Bounce Rate is
slightly lower on desktop than mobile.
Opportunity: As part of the proposed content audit, apply an analytical lens on device usage to extrapolate any key
nuances and trends that might be represented in the data.
AUDIT FINDINGS SUMMARY | WEBSITES | GALLIPRANT_VET

• Seresto Baseline Review | There's a seasonal user traffic flow trend as expected for product. Visitors appear to be
pre-qualified where select baseline user engagement metrics represent reasonably average performance
(pages/session, avg. session duration, bounce rate). Of the Brands reviewed so far, measured by site visits, Seresto
by far represents a leading consumer Brand within the product portfolio.
And, as it is becoming a recurring theme with all sites under review, the percentage of new users versus returning
users is high indicating a need to continually fill the top of the funnel with new consumers via awareness
campaigns (especially during in-season needs).
Traffic Attribution - As a baseline, Display + Direct + Paid Search + Social + Organic Search are working in parallel
to represent the Top Default Channels stimulating traffic to the website.
Paid Media and Paid Search campaigns appear to be quite active for the Brand (representing 79% of all traffic) with
notably email as an active digital channel for those opting into communications via the website.
Opportunity: Since the Brand is quite active in Campaigning, recommend we work the Brand Manager to fully
understand their digital marketing plans to be able to use the Brand as a proof case for how we can measure
and optimize their digital efforts to demonstrate to the Pet Health Group as a whole the possibilities for how
Digital can be leveraged using dashboard data analytics to drive positive business outcomes.
Campaign Tracking - Campaign Landing Pages and
UTM tracking is in place, but does require an adopted
UTM naming convention using all parameters to
better be able to track Campaign attribution and
performance via Google Analytics without extensive
data cleaning and grooming.
Of note, the Brand has historically used Campaign
landing pages which is a good strategic direction.
Deeper analysis would be required to understand how
well these landing pages performed for defined time
periods and conversions, but it's great to see this is an
active strategic direction.
Where applicable for Campaign periods designed to
run indefinitely, A/B testing landing pages at the
forefront of Campaign launch would provide enhanced
value to align click paths for greatest performance.
Opportunity: If it has not been conducted recently in
the past, A/B testing campaign landing pages for greater engagements would provide value in optimizing click
paths and conversion. Additionally, implementing a UTM naming convention with the Brand could be used as
the model for organizational-wide adoption.
AUDIT FINDINGS SUMMARY | WEBSITES | SERESTO

Primary Landing Page | Page Consumption - The [campaigns/seresto-experience-the-difference] page is the primary
landing destination, with the Overview [our-products/seresto] page a leading secondary landing page destination.
The PetBasics.com site design is complex designed with a number of URL redirects and more than 400 separate URLs
containing Seresto content. The ability to consolidate page performance metrics across page URLs becomes an
extensive exercise when analyzing page performance but with the acknowledgement Campaign Landing Pages do
isolate site visitors arriving from promoted efforts.
Opportunity: A full content and user behavior audit would
understand what content is of highest value to visitors and is
securing the most engagements. Gathering this level of insights
may aid in all aspects of marketing and sales strategically
aligning communications and messaging.

User Devices | Platform - A large percentage of users prefer
their Mobile device when visiting the site. As with the
Galliprant review, this warrants deeper exploratory
investigation.
Recommended Action: To understand how mobile may
impact user site behaviors and conversion events, and
when compared to other device types, an expanded audit
recommended for analyzing site engagements should be
appended with a detailed device analysis using a specific
mobile lens view.
The objective would be to understand insights such as:
― Does the click path as currently design work for
mobile?
― How do mobile engagements differ from desktop?
― How does the mobile design impact conversion
events and purchase intent?
Events Overview - Event tracking is active on the site. Of note is the ~200K users who have clicked on 'Where to Buy'.
This is a core measurable conversion event and represents a large percentage of visitors are active shoppers.
Opportunity: Verification and validation of where
conversion pixels are placed on what website events
will give us the ability to track the final conversion of
our campaigns.
Opportunity: To understand at deeper levels user
engagements and the impact on desired conversion
events, a separate analysis for which events are being
triggered linked to page, page position and attribution
would provide insights for site UX, click paths, content
value and resonance, and potentially motivational
consumer triggers for advancing further down the
funnel.

AUDITDETAILS
INITIAL INSIGHTS, FUTURE CONSIDERATIONS,
& REQUIRED EXPANDED AUDIT DISCOVERY

Top of Funnel
Awareness
Consideration
Conversion
Activation
Loyalty
Advocacy
Bottom of Funnel
Middle of Funnel
Prime KPI: CPL or CPC
Prime KPI: CPE
Prime KPI: CPA
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Marketing Funnel | Journey Stage Considerations | Classifications
Ideally, marketing funnel objectives are clearly identified into organizational-wide adopted descriptions and stages without
ambiguity. It may be a simple 3- or 4-step funnel or a more expansive 6- or 7-step funnel. In either case, without knowing
how the funnel is internally viewed and adopted, and what the desired measurable goal (user action) is at each stage, it is
hard to analyze marketing performance (as well as media performance) for funnel impact.
Of note, paid media is typically Top of Funnel (Awareness) activity but can support deeper Funnel objectives leveraging
greater degrees of messaging, CTAs, and audience targeting and/or retargeting.
Dashboard Objectives Filters
• Awareness | Impressions Reach, Frequency
TOF | Discovery (Impressions, Clicks, Website Visits)
• Consideration | User Site Engagements
MOF | User Actions (Events, Goals, Content, Engagements)
• Acquisition or Conversion | Defined [Qualified Lead] Action
MOF | CTA Actions (Find, Subscribe, Coupons, Trials, Sales)
• N/A + Leads (Unclear)
• Remarketing
MOF + BOF
• Revenue/Sales
MOF + BOF | Typically Conversion
• Participation + Engagement
MOF + BOF
Journey | Funnel Stage Expanded Discovery Questions
� Team | Do we have a vision to track, measure and optimize the entire marketing funnel?
� Team | What progress have we made for tracking other digital channels and marketing touchpoints?
� Team | Has consideration been given to the difference between marketing and sales funnels?
� Brands | How are (campaigns, targeting, CTAs, creative) designed to influence deeper funnel objectives?
� Brands | More expansively, are desired user CTAs clearly defined for each funnel objective and stage?
� Team | What is the Elanco difference between acquisition vs. leads vs. participation vs. engagement?
� Team| Generally, how is a paid media user click given credit in the funnel? Is it currently the measured conversion?
� Team | What is the model for revenue/sales linked and tracked by marketing attribution? Dashboard timeline?
� Team | What, if how, have we leveraged landing pages as a CTA, e.g., Campaign Landing Page?
RECOMMENDED ACTION | Funnel Clarification | We need to verify adopted Elanco funnel semantics (potentially creating a
touchpoint user journey map) including marketing channel + roles + objective with identified desired user action by funnel
objective to allow us to better design and build dashboard views and reporting for greater insights. Ideally, we adopt the
Prime KPIs as identified above for the Conversion Point for each Funnel stage for greater meaning to Brand Managers.
AUDIT | INSIGHTS, FUTURE CONSIDERATIONS & QUESTIONS

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Campaign Considerations
Primary dashboard data filters by Campaign Objective are linkable to Channels and Platforms.
Understanding the Campaign landscape and how campaigns are designed and executed will enhance data analysis by
reviewing and analyzing attribution performance trends based on specific campaign goals, funnel objectives and execution
parameters including placement, targeting, creative, CTAs, and messaging.
Brand Managers removed, for analysis considerations, single dimensional dashboard views including Agency Primary KPIs
(optimization metrics) offer limited value and do not afford the ability to easily link multi-dimensions together into a
clearer, more holistic performance review and analysis.
Without this capability, media buyers and analysts may be restricted to analyzing and optimizing performance at a
top-line level which may not discover performance inefficiencies hidden within the data, and especially when linked to
business ROI (sales revenue).
Data modeling will show we do have some capability to analyze and optimize performance at this top level, but it does not
currently allow for deeper analysis for finding waste in the details within media spending without data grooming and
cleaning.
Ideally, media analysis starts at the Campaign Level, then breaks it down into executable segments such as channel,
platform, audience targeting, creative, CTAs, messaging, et al.
Campaign Expanded Discovery Questions
� Team | How are Groups defined and managed? Verify Pet Health vs. PAIN, PARA, what else?
� Team | Who within Groups are the primary stakeholders requiring our support, e.g., US Pet Health Group?
� Brands | How are campaigns typically designed, classified (categorized) and executed, i.e., objective only?
� Brands | Are media buys based on funnel objective specifically as opposed to more specific goals?
� Brands | Is there a naming convention for Campaign identification? Is there standardization in naming conventions?
� Brands | Does a Campaign list and marketing calendar exist for each Brand? Are media plans available for review?
� Brands | How are campaigns currently optimized? Verification needed - Are campaigns optimized by the single identified
Primary KPIs in the dashboard? If not, how then?
� Brands | How is media purchased? At a corporate, brand or campaign level? Who defines the campaign goals and
objectives? Do we do A/B testing and who has responsibility?
� Brands | Does each US brand have their own internal media team or rely on Gabe and/or the media agency?
� Brands | Are there advance opportunities to work with the Agency for campaign tracking and tracking conventions
(UTMs, et al) to track media attribution for CTAs, website behaviors and conversion, and sales revenues?
RECOMMENDED ACTION | Campaign Planning & Tracking | Ideally, we need to begin collaborating with Brand Managers
and/or the Media Agency in advance of Campaign and/or Media launches to effectively implement better tracking and
build out of the dashboard data analytics and reporting.
AUDIT | NEXT STEPS: INSIGHTS, FUTURE CONSIDERATIONS & Q/A

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Data Considerations
Data ingestion into the dashboard is a mix of near-real-time and time-delayed data feeds based on source data. This in
itself presents problems for the entire dashboard data ecosystem for building confidence in dashboard views. As pointed
out, if the Brand Manager and/or a dashboard user finds the dashboard data views are different than Agency performance
or dashboard reporting, the Datorama dashboard adoption becomes more difficult.
RECOMMENDED ACTION | An audit of all data feeds needs to be conducted to find discrepancies and any time delays in
the data ingestion process while engaging the Media Agency to overcome any limitation discovered. Based on the media
spend, it should not be acceptable that real-time or timely data feeds are not available and should be a condition within the
contractual SOW.
Even when the Agency may not want to provide API access to their own buying platform due to sensitive company and/or
client information contained within the platform, security firewalls and protocols can likely be developed. The fallback
solution is the Datorama automated csv/email ingestion process, but when used should be set up for the Agency to
automate delivery of data each 24 hours as opposed to longer (weekly) periods of time. If data grooming is required on the
Agency end, then the Agency should factor this into their cost structure to fulfill the contractual agreement.
RECOMMENDED ACTION | It is not an unreasonable industry request to mandate an Agency deliver real-time or timely
data feeds for client dashboards. Experience reveals API barriers is a common position of agencies and with client pushback,
it can be resolved. It is advised this is addressed with the most appropriate corporate manager responsible for the Agency
relationship and selection (Gabe?).
Table data and filterable associated graphical views within dashboard is many times parceled into silo 'ed dimensions
creating the inability to link and cross tabulate multi dimensions for more holistic as well as deeper granular cross-tabulated
views for analysis of performance and optimization, e.g., channels, platforms, campaigns, audiences et al are not linked and
provide silo dashboard views by dimension. A data map linked to dimensions and metrics will identify areas of need where
multi-dimensional filtered views with exportable data would be beneficial (Jellyfish has been assigned this task).
Data Expanded Discovery Questions
� Team | What does the raw data look like? Can it be blended as required for cross tabulation? Where is it hosted? What
major datasets are we missing? How much data cleaning and grooming are we doing?
� Team | Is there a data map which shows what data, dimensions and metrics is being imported or linked to the
dashboard? [data map available from Datorama]
� Team | Are all digital owned assets linked to the dashboard, e.g., websites, portals, social, email, et al?
� Team | Side data topic: what does our Tool Chest look like? Subscriptions to 3rd party tools such as SEM tools,
heatmapping, other - which might be useful on occasion?
� Team | Heatmap Table data exports provide the most value for extracting data for deeper analysis and modeling. But it
doesn't allow for multi-dimension filtering. Has this come up in conversation?
RECOMMENDED ACTION | Data Mapping | Pinpoint when Jellyfish will complete the data mapping exercise and use it to
conduct the data feed audit for finding discrepancies and time delays in the data ingestion to address resolution with the
Agency. Audit will also identify multi-dimensional needs as well as additional data needs which a process for securing such
data for dashboard ingestion could be initiated.

-------------------------------------------
Stakeholder Considerations
Stakeholders vary by executive role and/or managerial responsibility, business division and/or brand unit. Depending on
group level, different KPIs become relevant for driving the business and areas of responsibility.
One set of KPIs does not fit all stakeholders, nor does a scheme of roll-up dashboards which incorporate many different
filterable metric views.
Top-level stakeholder groups may include the following: executive, sales, corporate marketing, category and/or brand
marketing, channel-platform marketing, affiliate marketing, digital squad teams, data analysts by area of responsibility.
Therefore, when reviewing a Brand's paid media and marketing performance in general, we must consider the various
stakeholders and their unique organizational level needs aligned to business goals and underlying objectives.
Furthermore, funnel perspectives, objectives, and stages may vary by marketing versus sales funnels. Each has their own
purpose and use and which we may need to track and analyze somewhat differently.
But of high value for both marketing and sales is the ability to link the funnel to sales/revenues so that we may optimize
against Business ROI and Marketing ROAS.
Stakeholder Expanded Discovery Questions
� Team | What is the consideration for building dashboards by stakeholder group level?
� Team | If our first identified primary stakeholder group for dashboard adoption is the Brand and Assistant Brand
Managers, can we begin to prioritize dashboard builds based on these specific role needs by simplifying and
condensing views into more simplified views for easier insight takeaways?
� Team | What is the status of linking channel sales/revenues and the CRM to marketing attribution?
RECOMMENDED ACTION | Org Chart + Stakeholder Needs Mapping | Survey stakeholders and develop a guiding
Stakeholder Group Needs Map as may be represented below to aid in dashboard development and overall data analysis
needs.
Group Stakeholders Primary Data Needs Insights Reporting Campaign Insights Core KPIs | Metrics
US Brand
Managers
Nicole Fox
Catherine Matthews
Justin Goedecker
Gabe Zubizareta
David Medina
Business Impacting
Campaign Insights to
steer strategic planning
and execution
Milestone
Campaign + Funnel
Performance
Top-Line Business
Performance Impact -
Brand Awareness,
Engagements, Leads,
Sales/Revenue Lift,
Brand NPS (Lift) score
Campaign Pacing,
Reach, Frequency,
CPC, CPE, CPL, CPA

-------------------------------------------
Business and Marketing KPIs
Awareness > engagements > leads > unqualified leads > qualified leads > conversion > sales/revenue > advocacy
all impact the funnel and business bottom line. Linking the most relevant metrics and KPIs to each of these journey (funnel)
steps is essential.
The Prime KPIs for each Funnel (Objective) Stage needs to be clearly identified, otherwise we are unable to truly analyze
marketing and business ROI from any marketing spend. These KPIs may be different by Channel and Platform, but they still
need to align to the Funnel.
Review suggests for Paid Media, the Prime KPIs for the core funnel stages and which can be applied to Post Campaign
Analysis may be as follows:
• Awareness = impressions and clicks so CPC is the valued monetary KPI (CTA landing page visit)
• Consideration = site visits and desired content consumption so (CPE) engagements is the valued monetary KPI
(CTA landing page content consumption)
• Acquisition and/or Conversion (in the absence of no e-commerce = pre-qualified or qualified lead so defined
button clicks (CPA) may be the valued monetary KPI (CTA landing page click on Where to Buy)
This storyline will work well for Brand Managers if we can attribute collected data to each of these levels.
KPI Expanded Discovery Questions:
� Team | Do we need clearly understand what is conversion at each stage of the funnel (awareness, consideration,
acquisition)?
� Team | Do we have a metric and KPI map for building out dashboards and overall data backbone?
� Team | Even before e-commerce is fully activated, does data exist to be able to track such leading marketing and
business KPIs such as:
Marketing ROAS, Lead Generation, Business ROI, CLTV, Channel Sales Influence, Sales Growth, Sales Revenues, %
Sales Digital, Brand awareness, SOV, Customer Acquisition Cost, Customer Retention Rate, Customer
Complaints, NPS, Trial Rate, Net Profit Margin, Market Share.
� How far along are we at processing the linking of revenue lifts via MMM + Response Curve Analysis?
RECOMMENDED ACTION | Data Platform + Metric Mapping | A useful tool is to develop a guiding Data Map as may be
delivered by Jellyfish as a first step. Depending on the what may be delivered, it may require additional work to map
expansive data sources by platform and relevant KPIs.
Mapping should include Primary, Secondary and Tertiary Data Platforms (what platforms can we source data) aligned to
Funnel Stage (awareness, consideration, acquisition), and all the metrics which can be obtained from the data (and aligned
to Funnel stage).
Further, this mapping exercise for maximum dashboard and insights benefit would include all owned digital assets and
marketing touchpoints (both online and offline) with affiliate and partner (retailer) platforms as may be assessable.

-------------------------------------------
Dashboard Considerations (requires deeper stand-alone audit and review)
• Dashboard load times and visual builds are slow especially with filtering. This may be due to the number of views built
for each dashboard. Exporting data and its speed is also impacted (slow).
• Funnel objective (stage) performance is not easily linked to all dimensions in a single view. (Heatmapping table is closest
view but is not multi-dimensional even with available filters.)
• Benchmark table dashboard data exporting is silo'ed into separate filters for each dimension. For analysis purposes,
would be helpful to blend dimensions for more efficient performance analysis. Would also be beneficial to include all
filterable dimensions for optional multi-level views and for greater snapshot review.
• Conversely, when combining multi-dimensions, they should be balanced for takeaway insights, e.g., ganging the
multiple pacing dimensions into same view (e.g., actual + planned by budget + pacing + planned MoM) can be visually
confusing and hard to extract key data points and takeaways. The solution may be an interactive view filters for each
view with the capability for selecting and deselecting chosen dimensional filters.
• Objective-based dash views are alpha ordered and do not follow the funnel path. Needs corrected to remove
confusion.
• Brand Managers and Key Executive Stakeholders (non-analysts) should not be overwhelmed by the number of deep
metric views and should be able to easily navigate to dashboards that are aligned to their own interests and business
needs.
e.g., Representative Analogy | media performance metrics are meaningless for executives without clear context or
linkage to value for marketing ROAS and business ROI linked to revenues. Leading marketing KPIs are not really the
actionable KPIs for executives acknowledging they do have high value as KPIs for media buyers and marketing
manager-type stakeholders.
• Metric description pulldowns may require deeper consideration for greater clarity and visibility with maybe a separate
text board with greater information and potentially even formula calculations to ensure no ambiguity.
• Changing filters on occasion will remove another filter previously applied, e.g., channel or objective filter changes
remove Brand filter - dangerously annoying.
• Heatmap Tables - prime source of exportable multi dimension data cannot gang multiple dimensions into table views
or for exporting - building a blended dataset is time consuming without the raw data.
• Elanco Media Spend View: Trend Overtime (sic) views should have independent interactive filters for each view (turn
on and off) for visual graph build.
• General: data exploration table views should have expansive interactive linked dimension performance filters for
greater insights in a single view - remove silo'ed dimensional views as much as possible, >> also filters for inclusion of
daily or weekly performance with addition of platforms and baseline media metrics plus pacing (budget) linked for
expansive data table view.

-------------------------------------------
Dashboard Considerations (continued)
Dashboard Expanded Discovery Questions
� Team What can be done to optimize view visual build times when altering filters?
� Team | How is the BETA being rolled out to the organization? Are we gathering stakeholder/user feedback?
� Team | Now that the BETA version is rolled out with expansive views, what is the consideration for a full design
re-review using a stakeholder map for re-aligning dashboards and views by stakeholder group need which would
lead to more rapid and deeper organizational adoption and use?
� Team | What does the raw data look like? Can it be blended via automation as required for cross tabulation in specific
dash views? Use cases TBD. Who would have responsibility?
� Team | How do we currently leverage the internal resources of IAC? Do we have their full support? Are they
responsive?
� Team | What does the Elanco expansive tool chest + data sources look like for building out dashboard data views, e.g.,
Google Trends Search Indices, more?
� Team | What does the roadmap look like for ingesting and linking data and activating all Channel Ecosystem Filters
which include: addressable TV, (activated - digital audio, digital display, digital search, digital social, digital video), direct
traffic, email, n/a, organic search, press, print magazines, referral, retailer websites, SMS, and television.
RECOMMENDED ACTION | Dashboard Audit | Understanding the desire is to launch in early 2022, it may be beneficial to
conduct a top-line audit of the dashboard and make slight modifications for view organizational flows (re-order views from
top to bottom for benefit of Brand Managers in particular).
As time permits, a more expansive audit could be conducted for planning evolutionary build stages to make adjustments
based on audit findings and incoming stakeholder feedback (once launched) for use and value to re-align future build
cycles.
Further, initiating conversations with affiliate and retail partners might open the door to additional data inclusion and
insights collection of retailer/sales branded performance on individual channels and platforms.
AUDIT | INSIGHTS, FUTURE CONSIDERATIONS & QUESTIONS

-------------------------------------------
Media Spend Considerations
YTD | Accumulative Spend - Media | Digital Media Budget Baseline
Media spend pacing across brands have anomalies month to month with a recent negative monthly trend pattern.
Accumulative monthly media spend lags behind planned budgeting with the gap expanding over the last (4) months.
If this were a re-optimization issue, platforms via their AI would typically re-adjust the spend in a few weeks with additional
human oversight which should have corrected the pacing.
Volatility in monthly spends in itself likely impact improving performance and platform gains as budgets are increased
and reduced (if this occurs too often).
Galliprant Brand View
Month
Actual Spend
(Acc By Month)
Media Budget
(Acc By Month)
Spend Pacing
(Acc By Month)
Jan 2021 $26,614.45 $26,617.99 -0.01%
Feb 2021 $51,847.93 $51,857.46 -0.02%
Mar 2021 $670,700.80 $735,537.96 -8.81%
Apr 2021 $1,202,390.57 $1,304,773.51 -7.85%
May 2021 $1,724,752.88 $1,861,276.13 -7.33%
Jun 2021 $2,706,403.66 $2,878,667.22 -5.98%
Jul 2021 $3,749,836.77 $3,891,812.65 -3.65%
Aug 2021 $4,608,105.54 $4,803,248.73 -4.06%
Sep 2021 $5,585,942.58 $5,970,803.06 -6.45%
Oct 2021 $6,380,162.45 $7,067,972.97 -9.73%
Media Spend Expanded Discovery Questions
� Agency | What impacts planned versus actual monthly spend? How is budget allocation determined by Brand?
� Agency | What dynamics are in play which is impacting full utilization of budget?
� Agency | Is this a manual override in some sense based on a strategic decision, or is this a problem related with the
platforms?
� Agency | Are Brand Managers or Media Buyers involved whatsoever adjusting spends based on month over month
performance or with more frequent recency?
� Agency | When budget spends are reduced, is it by all platforms or single platforms?
� Agency | How is media being optimized, i.e., platform AI or manual adjustments aligned to identified Primary KPI?
� Agency | How is the media budget planned by funnel objective? Are there budgeted spend percentages aligned
with objective? Can we extrapolate based on pacing performance data?
RECOMMENDED ACTION | Agency Introduction | A background discussion with Agency should provide clarity.
BASELINE | NEXT STEPS: GROUP BRAND SOV BENCHMARKS & Q/A

-------------------------------------------
US Pet Health Group Brands
Galliprant, Interceptor Plus, Credelio, Credelio Cat, Seresto, Advantage II, K9 Advantage II
-------------------------------------------
Baseline Benchmarks
Benchmarking can be performed at different intervals based on analytical needs: e.g., YTD, 90-Day, MoM, seasonality,
defined milestone periods, other.
Interval benchmarks are useful for understanding how well a marketing effort is performing against comparison time
periods or milestones. 90-day benchmarks are most appropriate for optimizing paid media as its an indicator for real-time
recent performance with relevance for longevity to consider common peaks and valleys in performance.
Comparing selected Brands (Galliprant and Seresto), baseline benchmarks against the U.S. Elanco Pet Health Group
provides insights as to media SOV and comparative performance. This review can be useful to ensure prioritization is
aligned to business goals and objectives.
2021 YTD | SOV Benchmarks
Pet Health Group Digital Media Baseline Brand Performance
Comparing YTD baseline benchmarks against the Group Brands reveals deeper analysis needs such as:
� YTD, Galliprant Brand performs well compared to Group Brands and the Group Benchmark for
leading KPIs, yet underperforms for CVR and CPA.
� YTD, Seresto Brand performs well compared to Group Brands and the Group Benchmark for three
leading KPIs, but underperforms for CPM.
Brand Media Cost SOV Impressions Clicks CTR CPC CPM CPA Conversions CVR
Galliprant $6,427,779.73 12% 2,074,651,819 10,374,723 0.50% $0.62 $3.10 $6.26 1,027,188 9.90%
Interceptor Plus $10,041,309.59 19% 1,248,307,638 2,024,276 0.16% $4.96 $8.04 $5.78 1,736,018 85.76%
Credelio $10,649,723.36 20% 1,401,238,372 3,477,503 0.25% $3.06 $7.60 $6.71 1,586,714 45.63%
Credelio Cat $3,348,612.07 6% 522,765,682 1,143,387 0.22% $2.93 $6.41 $6.04 554,839 48.53%
Seresto $11,878,274.16 23% 1,776,904,863 6,922,059 0.39% $1.72 $6.68 $4.85 2,447,265 35.35%
Advantage II $5,030,755.42 10% 781,694,643 2,442,329 0.31% $2.06 $6.44 $4.32 1,165,601 47.72%
K9 Advantage II $5,402,370.19 10% 726,845,231 2,346,062 0.32% $2.30 $7.43 $4.54 1,189,881 50.72%
YTD Benchmarks $52,778,824.52 100% 8,532,408,248 28,730,339 0.34% $1.84 $6.19 $5.44 9,707,506 33.79%
Conversion Expanded Discovery Question
� Agency | What is Conversion? Is it different by funnel objective? Can revenue be linked?
RECOMMENDED ACTION | Agency Introduction | A background discussion with Agency should provide clarity on
Conversion.
BASELINE | GROUP BRAND SOV BENCHMARKS

-------------------------------------------
90 Day | Aug-Oct 2021 | SOV Benchmarks
Pet Health Group Digital Media Baseline Brand Performance
Taking the baseline review one step further, comparing 90-Day baseline benchmarks against the Group Brands reveals:
� Galliprant Brand still performs well for leading KPIs, yet is underperforming at greater levels for CVR and CPA
compared to YTD benchmarks - yet as a percentage of Brand Group budget spend is increased.
� Seresto Brand performs well for only one leading KPI (three when reviewing YTD) compared to the Group Brand
benchmarks, as Group SOV budget spend percentage decreases.
Brand Media Cost SOV Impressions Clicks CTR CPC CPM CPA Conversion CVR
Galliprant $2,630,356.36 21% 855,576,004 4,258,446 0.50% $0.62 $3.07 $11.10 236,892 5.56%
Interceptor Plus $3,877,175.09 31% 558,356,620 1,061,893 0.19% $3.65 $6.94 $3.03 1,280,551 120.59%
Credelio $143,261.40 1% 4,422,316 20,337 0.46% $7.04 $32.40 $1.58 90,787 446.41%
Credelio Cat $2,477,766.04 20% 412,691,156 740,178 0.18% $3.35 $6.00 $6.39 387,741 52.38%
Seresto $1,889,204.90 15% 97,923,555 1,136,484 1.16% $1.66 $19.29 $6.34 298,161 26.24%
Advantage II $1,023,504.67 8% 150,948,130 606,613 0.40% $1.69 $6.78 $4.36 234,958 38.73%
K9 Advantage II $584,861.06 5% 34,106,747 144,389 0.42% $4.05 $17.15 $7.36 79,513 55.07%
90-Day KPI
Benchmarks
$12,626,129.53 100% 2,114,024,528 7,968,340 0.38% $1.58 $5.97 $4.84 2,608,603 32.74%
90-Day Benchmark Expanded Discovery Questions
� Agency | What had changed in the Galliprant media buy over the last 90 days which has caused lesser performance for
CVR and CPA?
� Agency | What has changed for Seresto's media but that CPM has increased likely leading to lesser performance for
CPC and ultimately CPA?
RECOMMENDED ACTION | Media Buy Clarities | A background discussion with Agency should provide clarity on nuances in
media buy which would allow us to dig deeper into the data for understanding changes in performance.
Leading Digital Media KPIs: CTR, CPC, CPM, CPA, CVR
BASELINE | GROUP BRAND SOV BENCHMARKS

-------------------------------------------
Galliprant vs. Seresto
Side by Side 90 Day vs. YTD Performance Comparisons
Select Brands Digital Media Baseline Brand Performance
Comparing 90-Day versus YTD baseline benchmark performance reveals:
� 90-Day SOV for both Brands has decreased in media spend compared to YTD as part of Group Brand overall spends.
� Galliprant impressions and CPM remained stable, while Seresto had negative volatility.
� Seresto enhanced click performance (clicks and CTR) on fewer impressions, while Galliprant remained stable.
� CPC remained stable (reasonably for Seresto) for both Brands.
� Conversion, CPA and CVR all underperformed for both Brands compared to YTD baselines representing changing
dynamics or parameters in the media buy.
Baseline
Performance
Media Cost SOV Impressions Clicks CTR CPC CPM CPA Conversion CVR
Galliprant YTD $6,427,779.73 28% 2,074,651,819 10,374,723 0.50% $0.62 $3.10 $6.26 1,027,188 9.90%
Galliprant 90-Day $2,630,356.36 21% 855,576,004 4,258,446 0.50% $0.62 $3.07 $11.10 236,892 5.56%
90-Day Share
or | Delta Δ |
40.9% -26.0% 41.2% 41.0% -0.5% -0.3% -0.8% 77.4% 23.1% -43.8%
Seresto YTD $11,878,274.16 22% 1,776,904,863 6,922,059 0.39% $1.72 $6.68 $4.85 2,447,265 35.35%
Seresto 90-Day $1,889,204.90 15% 97,923,555 1,136,484 1.16% $1.66 $19.29 $6.34 298,161 26.24%
90-Day Share
or | Delta Δ |
15.9% -31.2% 5.5% 16.4% 197.9% -3.1% 188.6% 30.5% 12.2% -25.8%
Brand Paid Media Expanded Discovery Execution Questions
� Agency | Is there seasonality in play for either Brand to change spend percentages as part of the Brand Group?
� Agency | What may have changed in the media buy for executable parameters including channels, platforms, targeting,
creative et al within the 90-Day benchmark period which impacted positive or negative change for leading metrics
compared to YTD baseline performance?
RECOMMENDED ACTION | Media Buy Clarities | A background discussion with Agency should provide clarity on nuances in
media buy which would allow us to dig deeper into the data for understanding changes in performance.
BASELINE | SELECT BRANDS BENCHMARK COMPARISONS

-------------------------------------------
Data and data analysis on past media campaign performance can be used within data models to predict (represent)
future outcomes based on changing media buy parameters. In simple terms, the model involves finding the values for a set
of decision variables that maximize or minimize an objective function.
The following media optimization model demonstration is built within Excel Solver and can use three different data
sampling techniques (non-linear, simplex linear, evolutionary) depending on the input data and criteria. The data model has
been thoroughly tested and validated with varying DSP media platform AI optimization algorithms and represents high
probability of future performance based on parameter changes.
The model's inherent value is its use in strategic planning allowing media buyers as well as marketing and brand
managers to run "what if" scenarios to optimize media campaigns and budgets for maximum marketing campaign
performance and business outcomes.
The level of analysis is only limited to the available data and the ability to benchmark past performance leveraging
measured baseline performance dimensions which create the underlying foundation to the modeling. The Model's non-
linear design is flexible and capable for adjusting to specified conditional media execution criteria.
A business proof case follows.
Galliprant 2021 Baseline Channel Performance Benchmark (Jan. 01 thru Nov. 02, 2021)
Brand Media Cost $$ Share Impressions Clicks CTR CPC CPM CPA Conversions CVR
Galliprant Baseline $6,427,779.73 1.00000 2,074,651,819 10,374,723 0.50% $0.62 $3.10 $6.26 1,027,188 9.90%
Digital Audio $201,746.48 0.03139 17,155,303 94,288 0.55% $2.14 $11.76 $5.46 36,931 39.17%
Digital Display $1,261,833.32 0.19631 1,342,465,046 1,054,996 0.08% $1.20 $0.94 $1.68 752,123 71.29%
Digital Search $538,539.20 0.08378 1,982,212 89,835 4.53% $5.99 $271.69 $8.97 60,045 66.84%
Digital Social $1,511,248.85 0.23511 343,612,666 8,281,021 2.41% $0.18 $4.40 $653.29 2,313 0.03%
Digital Video $2,914,411.87 0.45341 369,436,592 854,583 0.23% $3.41 $7.89 $16.58 175,775 20.57%
YTD KPI Benchmarks $6,427,779.73 1.00000 2,074,651,819 10,374,723 0.50% $0.62 $3.10 $6.26 1,027,188 9.90%
Baseline Metric Expanded Discovery Question
� Agency | The primary question is verification for how the organization is classifying each funnel objective and
the user conversion point as aligned to the strategic planning behind media spend allocation.
PAID MEDIA | PREDICTIVE DATA MODELING OPPORTUNITIES

-------------------------------------------
Galliprant | YTD | Channel - Media | Baseline Performance
Reviewing baseline performance, we can clearly see Display is outperforming all channels for impressions and ultimately
conversions, whereas, social media outperforms all channels by clicks and underperforms for conversions which is to be
expected due to the user engagement patterns of the platform.
This basic baseline view would in isolation suggest more budget should be applied to Display if CONVERSION is the prime
KPI.
Channel Percent Spend Percent Impressions Percent Clicks Percent Conversions
Digital Audio 3.12% 0.89% 0.92% 3.59%
Digital Display 19.51% 64.68% 10.12% 73.22%
Digital Search 8.51% 0.10% 0.87% 5.92%
Digital Social 23.79% 16.63% 79.99% 0.23%
Digital Video 45.07% 17.71% 8.11% 17.03%
Grand Total 100.00% 100.00% 100.00% 100.00%
Brand Media Cost Impressions Clicks Conversions
Galliprant Baseline $ 6,466,681.92 2,102,527,024 10,553,494 1,034,899
Digital Audio $ 201,776.20 18,699,108 96,690 37,197
Digital Display $ 1,261,827.30 1,359,828,708 1,067,852 757,780
Digital Search $ 550,312.91 2,016,101 91,405 61,293
Digital Social $ 1,538,367.55 349,654,194 8,441,948 2,342
Digital Video $ 2,914,397.96 372,328,913 855,599 176,287
However, CONVERSION (depending on how it's defined for funnel objective) may be different based on Channel and/or
spend allocation. Typically, paid media is a Top or Middle Funnel marketing strategy. In order to reconcile how Channels are
performing and how they should be optimized, it is clear we must have agreement on paid media's primary goals by funnel
objective with a clear definition for Conversion which would allow us to then optimize per the defined funnel stage.
When this is clearly understood, available performance data would allow us to demonstrate opportunities for optimizing
media spends and campaigns by data modeling based on specific dimensions linked to funnel objectives and as aligned to
historic baseline performance metrics.
To represent what is possible, a very simplistic Data Model Representation using Channel Placement ONLY follows to
demonstrate how baseline performance metrics can be used for predicting future media spend outcomes.
(Ordinarily, the model would be built using multi-dimension performance metrics to ascertain with granular details how
budget is performing, but the simplistic representation is used for demonstrating how models can be developed.)
GALLIPRANT|SAMPLEPREDICTIVEMODELING(CHANNELOPTIMIZATION)

-------------------------------------------
Brand: Galliprant | BASELINE OPTIMIZATION TEST 1 Demonstration
Data Model Technique: Non-Linear Evolutionary Algorithm
Goal: Analyze media baseline 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. What would spend look like by Channel if we optimized for
Conversion without changing total media budget allocation and maintaining all channels in the original media mix.
Model Assumption: A correlation exists between channel spend efficiencies and impressions, clicks, and CPC which
ultimately lead to greater objective conversions.
Modeling Setup Parameters
Constraint Criteria - Evolutionary Model - Baseline Optimization Test 1
Target Beta (Max Assumed CPC Risk) $0.62
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 0.45
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.
Budget Spend $6,427,780
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 | Baseline Optimization Test 1
2021 YTD Brand Media Cost $$ Share ** Impressions Clicks CPC Conversions
Digital Audio Channel $661,964.39 0.10237 61,345,905 317,210 $2.09 122,032
Digital Display Channel $2,907,548.73 0.44962 3,133,367,170 2,460,584 $1.18 1,746,104
Digital Search Channel $0.00 0.00000 0 0 $6.02 0
Digital Social Channel $2,910,006.86 0.45000 661,412,875 15,968,958 $0.18 4,430
Digital Video Channel $0.00 0.00000 0 0 $3.41 0
Model Predictions $6,479,519.99 1.00199 3,856,125,950 18,746,751 $0.35 1,838,348
YTD Comparative Baseline $6,466,681.92 1.00000 2,102,527,024 10,553,494 $0.61 1,034,899
Predicted Gains | Losses 0% 0% 83% 78% -44% 78%
Galliprant | Sample Predictive Modeling (Channel Optimization)
** $$ Share: decimal rounding digit anomaly representation

-------------------------------------------
Brand: Galliprant | CONSTRAINT VARIABLE TEST 2 Demonstration
Goal: Analyze media performance by CHANNEL placement spend based on 2021 YTD benchmarks by modestly increasing
maximum spend allocation to 50% per single channel to compare the model's predicted outcome against legacy baseline
performance.
Objective: Optimize for Conversion by Channel Spend. What would spend look like by Channel if we optimized for
Conversion and allowed Channels to optimize spend up to allocating 50% of the budget on a single channel?
Model Assumption: Aligning channel spend to channel click performance and CPC would offer efficiencies for CPC and re-
align spend for greater conversions.
Constraint Criteria - Evolutionary Model - Spend Allocation Optimization Test 2
Average CPC used as the dynamic Beta (β) assumed risk variable coefficient. (Find an
optimum solution with an average CPC at or below $0.62.)
Maximum spend allocation for a single dynamic variable (%) - 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.
Total budget to be applied in model calculation - benchmark budget spend to represent
potential gains in model prediction.
Data Model | Predicted Outcome Based on Defined Constraint Criteria | Spend Allocation Optimization Test 2
2021 YTD Brand Media Cost $$ Share ** Impressions Clicks CPC Conversions
Digital Audio Channel $11,013.62 0.00170 1,020,659 5,278 $2.09 2,030
Digital Display Channel $3,233,340.96 0.50000 3,484,462,464 2,736,293 $1.18 1,941,756
Digital Search Channel $0.00 0.00000 0 0 $6.02 0
Digital Social Channel $3,233,340.96 0.50000 734,903,195 17,743,287 $0.18 4,922
Digital Video Channel $0.00 0.00000 0 0 $3.41 0
Model Predictions $6,477,695.53 1.00170 4,220,386,318 20,484,858 $0.32 2,008,791
Predicted Gains | Losses 0% 0% 101% 94% -48% 94%
** $$ Share: decimal rounding digit anomaly representation

-------------------------------------------
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 in isolation 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 Objective + Channel + Platform.
The net takeaway (which applies to virtually all modeling) is performance analysis 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 if Channel Conversion (not optimization metrics) is not clearly defined and aligned for each Funnel
objective.
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 cross-tabulated 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 multi-dimensional baseline
performance metrics can be used for predicting future media spend outcomes.
SERESTO | SAMPLE PREDICTIVE MODELING (SPEND OPTIMIZATION)
Jan. 01 - Nov. 09, 2021

-------------------------------------------
Brand: Seresto | BASELINE OPTIMIZATION TEST 1 Demonstration
Media Funnel Objective: AWARENESS
Goal: Analyze media performance for Funnel Objective 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 original media mix.
Model Assumption: A correlation exists between channel spend efficiencies, and impressions, clicks, and CPC which
ultimately lead to greater conversions.
Average CPC used as the assumed dynamic Beta (b) risk variable coefficient.
(Find an optimum solution with an average CPC at or below $3.63.)
Data Model Outcome on following page.

-------------------------------------------
Brand: Seresto | BASELINE OPTIMIZATION TEST 1 Demonstration | 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
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 the presented 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.

-------------------------------------------
Media Objective: ACQUISITION
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 and maintaining all channels and platforms in the original media mix.
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%

-------------------------------------------
Media Objective: CONSIDERATION
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 Spend $850,596
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%
$$ Share: decimal rounding digit representation

-------------------------------------------
Media Objective: REVENUE/SALES
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
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.
$$ Share: decimal rounding digit representation

-------------------------------------------
Brand: Seresto
BASELINE OPTIMIZATION TEST ROLLUP COMPARATIVE RESULTS
Media Objectives: AWARENESS, ACQUISITION, CONSIDERATION, REVENUE/SALES
Predictive Modeling Impact
When we model each campaign objective separately, we can begin to find deeper optimization opportunities as well media
spend inefficiencies in the 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 against YTD 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 frequently 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 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 - which the model can accommodate and 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 tool for realizing 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 (OPTIMIZATION IMPACT)

-------------------------------------------
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 groomed and cross-tabulated into a separate dataset for exploration is shown on the following
page. Where applicable, we can explore 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 varied sales and
revenue performance metrics, the same process could be applied to gather additional business insights and intelligence for
maximizing marketing ROAS and Business ROI while steering 2022 strategic business and marketing directions.
With adoption, Data Modeling Best practices would 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 (OPTIMIZATION IMPACT)

-------------------------------------------
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%
SAMPLEDATASETFORDATAMODELING |DEMONSTRATION

Objective - GA Website Benchmarks, Channel Attribution, User Behaviors, Click Funnels
-------------------------------------------
The intent is to review for to audit Traffic Patterns, Channel Attribution, User Behaviors and Engagements, and the Click
Funnel Paths for galliprantfordogs.com/ + galliprantvet.com + petbasics.com/our-products/seresto/.
Overall, the snapshot audit review of Google Analytics, the Branded websites, and the Datorama dashboard reveals the
sites may not be fully optimized for the click funnel path and general site performance and traffic is somewhat volatile
with up and down swing patterns.
RECOMMENDED ACTION | Opportunity | As resources become available, or via a desired direction, a full website audit of
select brands could be performed. The value to Elanco and Brand Managers would be added confidence the websites are
designed for UX and the click path for maximum benefit. It would further ensure Google Analytics is properly set up to
capture the data points required for marketing and business analysis.
As a starting point, I was able to gather some very brief introductory insights from top-level reviews of the analytics. Here is
what I have seen in the data and the GA (+Datorama dashboard) views so far.
GALLIPRANT | WEBSITE SNAPSHOT REVIEW

Datorama Dashboard Views
-------------------------------------------
Dashboard Filters | Date, Groups, Brands, Objectives, Channels, Platforms
Limited attribution linking for Objectives leveraging (GA Campaign Manager) as primary data source.
Website performance representations use limited filtered views to convey information that may not be fully actionable.
Baseline metrics are limited (new visits, bounces, bounce rate) may not be the most relevant and are not expansive enough
to convey site performance and user influence. Site traffic views by Channel acquisition is a useful view, but in its current
state does not adequately attribute source traffic by Objective which may be the main view filter for Brand Managers and
which much of Campaign planning may be conducted.
Dashboard Linking (Keyword) Expanded Discovery Questions
� Team | How Is attribution linked in the dashboard? Are we linking views via established UTM parameters? Other?
� Team | Have we ever considered cleaning and grooming the historic UTM data for naming conventions to make it more
useful and meaningful?
� Team | What is the general opinion for the need to develop a UTM naming convention for use with paid media and
other journey touchpoints?
RECOMMENDED ACTION | Expanded Views | Ideally, for benefit of Brand Managers in particular, dashboard website views
with expanded user behavior metrics should convey attribution influence and user behaviors for leading Funnel Objectives
(awareness, consideration, acquisition/conversion) highlighting adopted Primary Business Impacting KPIs. Furthermore,
filtering should foremost align to Google default channels with deeper filterable views aligned to dashboard setup (groups,
brands, objectives, channels, platforms).
DASHBOARD | WEBSITE SNAPSHOT REVIEW

Google Analytics
-------------------------------------------
Baseline Traffic Review
https://www.galliprantfordogs.com/
Top-level view: there is a high volatility in user traffic flow which aligns with the sessions view. Galliprant as a pain
medication does not have seasonality trends, and therefore, what this may represent is the impact of inconsistent
marketing campaigns, spends and/or marketing optimization to continually drive traffic to the website and to fill the top of
the funnel with new site visitors.
-------------------------------------------
Default Chanel Attribution
Default Channel Attribution is highly skewed to OTHER
representing a basic primary need to define campaign
traffic sources which is currently categorized into the
OTHER Channel.
Without clear attribution linking, analyzing site
performance for Funnel Objective for marketing and
business ROI, including paid media, makes gathering useful
insights difficult at best.
GALLIPRANT | WEBSITE SNAPSHOT REVIEW

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