Customer case study 118

Lead Generation - Roofing Services Popunder Ads Case Study: 375% ROI in France

An anonymized FroggyAds customer campaign turned $7,194 in media spend into $34,171 in gross revenue. This analysis covers the setup, optimization controls, creative testing, measurement chain and scaling decisions recorded for the campaign.

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Customer identity and offer name are withheld. Figures and optimization observations are based on the customer-provided performance record.

Ad spend$7,194reported total

Gross revenue$34,1714.75x ROAS

Net profit$26,977after media spend

Total ROI+375%profit ÷ spend

Campaign performance summary for Lead Generation - Roofing Services Popunder Ads in France

Campaign overview

This anonymized customer campaign focused on qualified acquisition and measurable downstream value for the roofing services campaign in France. The buyer used Popunder Ads through FroggyAds and reported 149,443 generated clicks, 1,095 reported conversions, $34,171 in gross revenue and $26,977 in net profit. The resulting 375% ROI is calculated as net profit divided by media spend.

The headline result came from a campaign that had to operate across 941 active publishers with a reported average bid win rate of 73.5%. Those delivery figures describe market access, not automatic quality. The customer still needed to identify which combinations of source, device, creative and landing experience produced commercially accepted outcomes.

Lead volume is only useful when records are consented, contactable, eligible and accepted by the sales process.

French creative, app expectations and the post-click action were kept consistent so acquisition volume could be evaluated against completed user actions.

The campaign was managed as a chain of evidence. Delivery metrics explained exposure, conversion records explained user action, and downstream value determined whether the traffic was commercially useful.

What the record demonstrates

A controlled combination of offer fit, localization, tracking, creative testing and source-level optimization produced a profitable historical result. It does not guarantee the same outcome for another advertiser.

The acquisition challenge

The implied cost per verified conversion was $6.57, while gross revenue averaged $31.21 per verified conversion. Those two figures created the working space for media cost, operational variance and downstream quality. A source could not be judged only by its click price because a cheap click with weak conversion value would still consume the margin.

The campaign produced a calculated 0.73% click-to-conversion rate and $0.229 in gross revenue per click. The buyer used these values as reconciliation points, not universal targets. Device mix, source pricing and conversion delay could change the same ratios even when the offer stayed constant.

The record also shows why test design matters in France. A broad launch was useful for discovery, but a broad campaign left unchanged would have mixed high-value cohorts with segments that had not yet earned additional spend. The customer therefore moved from exploration into more isolated source and device groups.

Consent, privacy, data use, contact expectations and suppression requirements should be explicit.

Campaign setup and targeting

Popunder traffic supplied high-volume full-page visits that made landing-page speed, source controls and post-click qualification especially important.

The buyer treated the destination as the primary creative surface, tested clear pre-lander variants and separated source IDs so high-volume placements could be judged on mature outcomes.

The campaign preserved the supplied CID VOL2-FR-078 as the reference for the analyzed record. Source identifiers, creative identifiers and conversion feedback were treated as a connected measurement path. That allowed the buyer to compare the original traffic purchase with the customer outcome rather than optimizing from disconnected reports.

The starting structure gave Popunder Ads enough breadth to test delivery while keeping the acceptable loss explicit. Publisher segments that accumulated spend without a verified outcome were candidates for reduction, while segments with repeatable conversions could move into separate scale groups.

The reported 73.5% average win rate was interpreted together with source quality. Raising bids could improve access to a segment, but it could also change the auction mix. The buyer reviewed performance again after material bid or budget changes instead of assuming the original efficiency would remain constant.

GEO: France (FR)
Format: Popunder Ads
Primary objective: User acquisition
Campaign reference: VOL2-FR-078
Reported publisher coverage: 941
Average bid win rate: 73.5%

Tracking and data quality

Server-to-server conversion feedback connected the advertiser-side event with the campaign click and source identifiers. This reduced dependence on browser-only signals and helped the buyer distinguish a delivery problem from a tracking or landing-page problem.

The source document describes a fast, geographically distributed landing setup with a target time to first byte below 120 milliseconds. The practical lesson is not that one latency number guarantees conversion. It is that redirects, script weight and unstable mobile rendering can create apparent traffic-quality issues that begin on the advertiser side.

The customer reconciled the reported 1,095 conversions with the accepted business event for lead generation - roofing services. FroggyAds traffic-quality controls can reduce exposure to invalid activity, but they do not replace advertiser-side validation, duplicate handling or downstream acceptance rules.

The campaign’s calculated CPC of $0.048 was useful for budget planning, while the $6.57 cost per verified conversion remained the more important commercial boundary. The difference between those metrics shows why optimization stopped at neither impressions nor clicks.

Conversion maturity was included in the decision process. A segment that appeared weak before delayed events arrived could be removed too early, while a strong early cluster could look better than it was if later quality or refund data had not matured.

Optimization controls

How the campaign was reviewed without exposing customer or publisher identifiers

The customer supplied aggregate campaign results. The public version deliberately omits publisher IDs, device and operating-system combinations, carrier rows and other granular data that cannot be independently validated from the public record.

Source-level review

The buyer separated placements by downstream conversion quality, not click volume alone, and used source controls to isolate weak and promising traffic pockets.

Tracking reconciliation

Campaign IDs and conversion events were reconciled between the advertiser tracker and the FroggyAds reporting view before budget changes were approved.

Controlled allocation

Budget changes were made in measured steps with rollback thresholds. Public case studies do not publish customer source IDs or unverified device-level combinations.

Creative process

Creative testing was managed as a controlled learning cycle

The original public draft included precise creative-level CTR figures that were not independently auditable. The revised case keeps the supportable operating method and removes those granular claims.

One variable at a time

Each test changed one meaningful element, such as the hook, image, call to action or landing-page transition, so the buyer could interpret the result.

Conversion quality first

Click response was treated as an early signal. A creative advanced only when downstream conversions and accepted customer events remained economically useful.

Separate test and scale pools

New variations stayed in a controlled test allocation while mature winners retained stable budgets. This reduced the risk of replacing a proven asset too quickly.

Document the decision

The buyer recorded what changed, the observation window and the next action. Public summaries describe the process without publishing unverifiable creative-level precision.

Campaign results

The reported totals and calculated efficiency metrics

Raw customer totals are paired with transparent calculations so the headline ROI can be evaluated in context.

These are customer-reported historical campaign results supplied to FroggyAds. The public summary is anonymized and has not been independently audited. Results are not a forecast, guarantee or universal benchmark — The reported totals and calculated efficiency metrics.

Clicks generated149,443941 active publishers

Reported conversions1,0950.73% calculated CVR

Cost per conversion$6.57$0.048 cost per click

Revenue efficiency4.75x$0.229 revenue per click

At the final reported totals, $7,194 in media spend generated $34,171 in gross revenue. Net profit was $26,977, which corresponds to the reported 375% ROI and a calculated 4.75x return on ad spend.

The 149,443 clicks produced 1,095 reported conversions. This creates a calculated conversion rate of 0.73%, a revenue-per-click figure of $0.229 and a revenue-per-conversion figure of $31.21.

These figures should be read as one historical campaign record. They show that the offer, format, market and optimization process worked together during the measured period. They do not establish a guaranteed rate card or a forecast for another advertiser.

The scale result also depended on the customer’s ability to recognize accepted value. For lead generation - roofing services, that meant looking beyond the front-end event toward accepted lead, contact rate, qualification and revenue-stage value. Without that connection, the same campaign could have appeared profitable while sending weak or ineligible outcomes downstream.

Outcome funnel for the Lead Generation - Roofing Services campaign

Reading the outcome funnel

The funnel connects the traffic total with the verified action and the financial result reported for the campaign.

149,443 clicks produced 1,095 reported conversions.
Each verified conversion cost $6.57 in media spend.
Gross revenue averaged $31.21 per verified conversion.
Net profit was $26,977, equal to 375% of media spend.

Metric	Calculated value	Interpretation
Cost per click	$0.048	Media spend divided by generated clicks
Conversion rate	0.73%	Reported conversions divided by clicks
Cost per conversion	$6.57	Media spend divided by reported conversions
Revenue per click	$0.229	Gross revenue divided by clicks
Revenue per conversion	$31.21	Gross revenue divided by reported conversions
Return on ad spend	4.75x	Gross revenue divided by media spend

Scaling process

How the campaign moved from exploration to controlled budget growth

The customer used source separation, creative evidence, loss limits and rollback points instead of treating scale as a single budget increase.

Source exclusions were based on spend relative to the target acquisition cost and enough completed visits to support a decision. The team avoided judging the format from bounce rate alone.

The team protected the original control structure while creating higher-budget copies of the winning combination. That made it easier to distinguish more volume from a real improvement in efficiency.

The broad test campaign remained a discovery environment. Once a source and creative combination developed enough evidence, the buyer moved it into a more controlled structure with its own budget and bid logic. This protected proven segments from the volatility of continued exploration.

Stop rules were defined before each meaningful increase. If cost per accepted outcome moved outside the declared boundary, if source composition changed sharply or if downstream quality weakened, the campaign could return to the previous budget level rather than waiting for the full test budget to disappear.

The final 375% ROI reflected the complete measured period, including exploration and scaling. The customer did not remove the learning cost from the headline result. That makes the reported number more useful than a narrow screenshot of only the best-performing day.

A practical replication plan would begin with the decision logic, not the final bid. Another advertiser should recalculate conversion value, source maturity, creative requirements, legal eligibility and maximum acceptable loss before borrowing any part of the campaign structure.

Test

Launch enough source and creative breadth to learn, while keeping the maximum acceptable loss explicit.

Refine

Separate weak source, creative and destination combinations. Preserve the combinations that produce repeatable accepted outcomes.

Scale

Increase budget in measured steps and return to the previous level if cost, quality or source composition leaves the declared boundary.

Practical takeaways

What advertisers can learn from this Lead Generation - Roofing Services case

The transferable value is the decision process, not the assumption that another campaign will reproduce the same ROI.

What supported the historical result

One measurement path connected the source, creative, destination and accepted customer event.
Localization covered the complete post-click journey.
Source-level data remained visible during optimization.
Creative tests were interpreted with conversion and revenue data.
Budget increases were staged and reversible.

What must be recalculated for a new campaign

The accepted conversion and its real value.
The maturity window for delayed or adjusted outcomes.
The legal and policy eligibility of the offer and GEO.
The maximum test loss and rollback rule.
The source, device and landing-page mix available at launch.

Method and source note: Performance totals, campaign format, GEO and optimization observations come from a customer-provided FroggyAds campaign record in FROGGYADS SCALING INDEX – VOLUME II. Calculated metrics are derived from those totals. Customer identity, offer identity and dates are withheld. Results reflect one historical campaign and do not guarantee future performance.

Case study FAQ

Questions about the campaign

For Lead Generation - Roofing Services Popunder Ads Case Study, what context frames the France roofing popunder case study?

Interpret the reported result alongside campaign dates, French roofing offer, target locations, device mix, popunder inventory, landing route, lead definition, and sales process. The title's percentage is meaningful only within those conditions.

For Lead Generation - Roofing Services Popunder Ads Case Study, which objective should the French roofing campaign make explicit?

The case should distinguish a page visit, enquiry, contactable homeowner, qualified project, appointment, and completed job. Cost and reported return need the same accepted lead stage and observation period.

For Lead Generation - Roofing Services Popunder Ads Case Study, how should the audience be defined in a France roofing case?

Look for service areas, property or project eligibility, language, device, schedule, source context, exclusions, and evidence of local need. Country targeting alone is too broad for a location-bound roofing service.

For Lead Generation - Roofing Services Popunder Ads Case Study, what creative rationale belongs in the roofing popunder study?

Explain how the message introduced the service, location, homeowner need, proof, terms, and next action within the popunder context. Record versions and approvals so response changes can be tied to an actual treatment.

For Lead Generation - Roofing Services Popunder Ads Case Study, how does the quote page affect the reported roofing result?

The page carries French wording, service coverage, trust evidence, contact details, form fields, consent, response expectations, and lead routing. Document page changes and test its operation on campaign devices.

For Lead Generation - Roofing Services Popunder Ads Case Study, what spend sequence should the France roofing case reveal?

Show the initial source caps, media rate, creative cells, review intervals, lead-validation delay, and reasons for each budget move. This makes it possible to separate measured expansion from rapid volume buying.

For Lead Generation - Roofing Services Popunder Ads Case Study, which evidence supports the return reported for roofing leads?

Reconcile paid delivery and confirmed visits with accepted enquiries, sales contact, qualified projects, closed work where disclosed, rejected leads, campaign cost, and value basis. Publish the formula inputs and period with the percentage.

For Lead Generation - Roofing Services Popunder Ads Case Study, what attribution limits apply to the French roofing case?

Account for other advertising, repeat visitors, offline referrals, call tracking gaps, delayed jobs, cancellations, seasonality, and the chosen value window. Observed return does not establish that popunder exposure caused every sale.

For Lead Generation - Roofing Services Popunder Ads Case Study, which operational lesson can local lead buyers use?

The case can show how source controls, French localization, service-area rules, quote-page continuity, lead screening, sales follow-up, and loss caps interact. A buyer should test the most relevant link rather than copy the reported rate.

For Lead Generation - Roofing Services Popunder Ads Case Study, when could this roofing popunder lesson apply elsewhere?

Transfer requires a comparable service area, homeowner need, permitted inventory, localized message, functioning quote path, matching lead criteria, and prompt sales handling. Recheck season, competition, regulation, and economics in a capped test.

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