Case Studies

How We Approach Performance Challenges.

Before any engagement, it helps to see how we think. These frameworks describe common advertising problems, the signals that reveal them, and the methods we use to address and measure them.

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Performance Improvement Frameworks

Common Challenges, and How We Work Through Them.

Each framework below describes a situation many advertisers face, the signals that typically reveal it, the methods we apply, and how progress is measured.

About These Frameworks

These are illustrative scenarios that explain our methods. They are not completed client engagements, and they do not describe specific results. Verified client case studies will be published here only with documented results and client permission.

Framework 01

Unreliable Conversion Data

Platforms report very different numbers, and leadership is unsure which figures to trust when setting budgets.

Common Signals

Platform conversions far exceed CRM records
Sudden jumps or drops after site changes
The same lead counted by several platforms

Our Approach

Audit tags, events, and consent handling
Define one conversion taxonomy for all platforms
Remove duplicates and connect CRM outcomes

What We Measure

Agreement between platform and CRM data
Share of conversions with known source
Stability of reporting after releases

Framework 02

Rising Search Costs

Cost per lead in paid search has climbed steadily while lead volume has stayed flat or declined.

Common Signals

Irrelevant queries in search term reports
Brand and non-brand results reported together
Automated bidding with weak conversion signals

Our Approach

Restructure around intent and value
Tighten query coverage and negatives
Feed better conversion signals to bidding

What We Measure

Non-brand cost per qualified lead
Share of spend on relevant queries
Lead-to-opportunity rate by campaign

Framework 03

Programmatic Spend Without Clarity

Display and programmatic budgets are significant, but it is unclear what they contribute beyond impressions.

Common Signals

Heavy reliance on view-through conversions
Large share of spend on unfamiliar apps or sites
High frequency on a small audience

Our Approach

Placement and inventory quality review
Frequency and audience overlap controls
Holdout or geo tests where feasible

What We Measure

Spend on reviewed, suitable inventory
Reach and frequency against plan
Incremental lift in site visits or search interest

Framework 04

Single-Channel Dependence

Most results come from one channel, leaving the business exposed to cost increases or policy changes.

Common Signals

Rising costs in the dominant channel
Flat demand for branded search terms
Little testing budget for new channels

Our Approach

Define channel roles across the funnel
Run structured tests in new channels
Scale only what earns its place

What We Measure

Blended cost per acquisition
Branded search trend over time
Contribution of each tested channel

Framework 05

Traffic That Does Not Convert

Campaigns generate clicks at reasonable cost, but few visitors become inquiries or customers.

Common Signals

Weak message match between ads and pages
Slow mobile load times
Long forms or unclear next steps

Our Approach

Landing page and form review
Speed and mobile usability improvements
Controlled tests of key page elements

What We Measure

Conversion rate by device and source
Form completion and abandonment
Lead quality alongside volume

How We Report Results

Our Standard for Performance Claims.

Defined Baselines

Every comparison states the baseline period, the metric definition, and the data source.

Context Included

We note budget changes, seasonality, and external factors that may affect results.

Client Permission

Named results are published only with documented evidence and the client’s written approval.

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