A cheap lead from a search for your company name can be useful. It does not tell you whether the person needed the ad to become a customer.
Before cutting or defending that spend, define the change you are considering and the business outcome that would justify it. Then ask whether you can compare brand advertising with a credible alternative in which the selected ads are withheld. If you cannot, a pause may still produce observations, but it will leave important explanations unresolved.
This guide follows Ternbridge, a fictional Toronto company selling purchasing-approval software, through that decision. Its company details, reports and amounts are invented teaching examples. No advertising account was inspected, no campaign was changed and no lift study was run. The examples do not describe Canada Create clients or recommended budgets.
The question is about advertising on your own brand. If you are considering searches for another supplier, use the separate SaaS competitor-keyword guide.
Both routes end at the same product. Seeing which route a visitor used leaves open whether that visitor would have arrived under the alternative advertising policy.
Read the diagram
One buyer. Brand ad or organic result. Both lead to Ternbridge. What changes without the ad? The observed route alone cannot answer that question.
The report that starts the argument
Ternbridge’s growth lead brings a campaign report to finance. Brand Search has the lowest reported cost per demo. Finance asks why the company pays for visitors who already know its name. Sales worries that removing the ad will send buyers elsewhere.
Each person has a reasonable concern. They are asking different questions.
The growth lead is describing attributed results: outcomes credited to advertising under the account’s measurement rules. Finance wants to know what the advertising added. Sales is concerned about the route a buyer takes when returning to the product. One attractive campaign ratio cannot settle all three.
A brand search can follow a recommendation, a product demonstration, an event or a month of research. An ad might make the next step easier. It might help someone find the right page. It might also receive credit for a visit that would otherwise have reached an organic result. The visible journey does not reveal which alternative would have occurred.
Nor does a strong organic listing settle the matter. The results page, other advertisers, the person’s intent and the destination offered can all affect the visit. Seeing your company near the top once is a weak basis for a permanent spending policy.
Growth, finance and sales are looking for different kinds of evidence. Agree whether the spending decision concerns credited demos, additional new business or a dependable customer route before choosing a metric.
Read the diagram
Growth: credited demos; what got credit? Finance: additional demand; what did ads add? Sales: customer navigation; how do buyers return? Choose the question that will decide the spend.
Ternbridge replaces “Are brand ads worth it?” with a more useful question: “Would withholding our defined own-brand Search advertising materially change qualified new-business demand in the market we can test?”
That question leaves room for several outcomes. The ads could add valuable demand. The available evidence could support reducing the selected spend. The study could be too imprecise to decide. Or the company could discover that its original concern is customer navigation rather than acquisition.
Write down which decision is yours. A team paying for a dependable route to an urgent customer service has a different objective from a software company trying to acquire new accounts. Keep any navigation or customer-experience objective visible instead of claiming that every credited conversion represents new demand.
What would you actually stop?
The campaign named “Brand” is a convenient starting point. Its name does not define all the advertising a person might encounter after searching for your company.
Ternbridge lists its company name, product name and common variations. It separates product-evaluation searches from account-access and support requests where the available wording allows that distinction. Ambiguous terms stay ambiguous. A short product name can also have an ordinary meaning unrelated to the company.
Then the account owner checks where that traffic can be served. Another Search campaign or an automated campaign may still reach relevant brand demand. If the proposed test pauses one named campaign while another takes over, the change concerns routing and delivery across campaigns. It may not create the intended no-brand-ad comparison.
The red boundary marks the selected own-brand Search exposure. Other campaigns remain visible because their ability to reach the same demand must be checked when defining the comparison.
Read the diagram
Selected own-brand Search. Define campaigns and brand terms, products, country and study population. Other campaign routes may still serve relevant brand demand.
Google provides brand controls for Search and Performance Max, but their scope depends on the campaign and setting. Its brand-exclusion documentation describes inventory-specific behaviour, including options affecting Shopping. Do not interpret a brand-exclusion label as a switch that removes every possible exposure everywhere.
For Ternbridge, the proposed scope is deliberately narrow: its own-brand Search advertising in a defined country and study population. Category advertising, sales outreach and ordinary product availability remain part of the background. The study would estimate the effect of the selected advertising within that environment. It would not measure the value of every marketing activity or the brand itself.
Record the boundary in a short account map:
- Which campaigns and brand terms belong to the proposed change?
- Which other campaigns might still serve relevant demand?
- Which products, countries and customer groups are included?
- What will remain active, and why?
- Who will verify that the saved settings produce the intended exposure?
A closed gate in the campaign named Brand does not close every advertising route. Check whether another eligible campaign would deliver an equivalent exposure before treating the intended holdout as unexposed.
Read the diagram
Named Brand campaign: paused. Other eligible campaign: may still serve. Both concern the same buyer. Verify the intended absence of selected ads.
Resolve a known mismatch before treating the plan as ready. If a buyer would still see an equivalent ad through another campaign, either include that delivery in the defined scope or explain the narrower question you can answer. Do not quietly call the group unexposed.
The existing search-terms guide covers query review. Here, the purpose of that review is to define the treatment, not to build a longer keyword list.
Decide whose outcome counts
Ternbridge sells to organizations. Three employees from one prospective customer can attend separate demonstrations and submit several forms before procurement approves anything.
Counting every form as new demand would make the outcome sensitive to how those employees return. One might click a brand ad, another might use a saved link and a third might search organically. If the ad is withheld, a different route could collect the credit while the same prospective customer continues its evaluation.
The team therefore defines a qualified new-business organization for this example: one previously unqualified prospective customer that meets its agreed product-fit and active-evaluation criteria. Repeated contacts belong to that organization. Existing customers seeking support are recorded separately. These are Ternbridge’s fictional definitions, not universal sales stages.
Repeated contacts are grouped under the same prospective organization when they meet the agreed qualification definition. Existing customers asking for support stay in a separate outcome category.
Read the diagram
Three contacts and repeated forms converge to one qualified new-business organization. Existing-customer support is a separate outcome.
For a geographic design, the analyst also needs an outcome that can be assigned to the study’s regions under a consistent rule. A form’s office address, an account’s headquarters and the location where someone encountered an ad are different pieces of information.
A national company’s buying committee makes this particularly important. Its finance lead may work in Toronto, its evaluator in Calgary and its procurement team elsewhere. Assigning the opportunity to headquarters does not prove that all relevant advertising exposure happened there. The design needs to assess that mismatch rather than hide it in a tidy map.
Use only the information the business is permitted to collect and analyse. The worksheet accompanying this guide needs definitions and aggregate checks, not customer names or raw contact details. More intrusive tracking is not the automatic answer to an uncertain location.
For the business-outcome analysis, preserve the relevant total across acquisition routes. A record should not disappear simply because it no longer receives paid-search credit. Keep unknown geography, unresolved duplicates and incomplete qualification visible, with their consequences for the analysis.
Carry the counted business unit, qualification rule and geographic assignment into the aggregate analysis. An unresolved location stays visible as unknown rather than being guessed into a region.
Read the diagram
Counted unit: one organization. Qualification: agreed definition. Geography: consistent rule, with unknown location kept separate. Aggregate: preserve the relevant status.
Ternbridge can report qualified organizations sooner than signed customers. That may make the first outcome more informative within a feasible study. It also limits the eventual claim: an increase in qualified organizations would not establish the same increase in contracts or contribution.
If CRM definitions or imports are unreliable, use the Google Ads and CRM mapping guide to address that prerequisite. Changing the reporting model cannot repair a missing business definition.
Read paid and organic numbers together
The growth lead’s first proposal is to pause brand ads for a period and see whether organic traffic takes over. That can expose a useful pattern. By itself, it cannot isolate the advertising’s effect from everything else that changed over time.
Consider this separate, fictional pair of equal-length reporting periods. Assume the same defined brand-query scope and compatible click definitions are available in both.
| Observation | Period A | Period B |
|---|---|---|
| Paid-brand clicks | 90 | 25 |
| Organic-brand clicks | 110 | 175 |
| Combined clicks in this query scope | 200 | 200 |
| Qualified new-business organizations, all acquisition routes in the selected market | 62 | 61 |
We build and run Google Ads campaigns measured on calls, forms and booked jobs, not clicks.
Paid clicks decrease by 65 while organic clicks increase by 65, leaving the same combined total. These equal-length, fictional reporting periods describe a change in route mix; they do not isolate its cause.
Read the diagram
Period A: 90 paid plus 110 organic equals 200 clicks. Period B: 25 paid plus 175 organic equals 200 clicks. Paid change: minus 65. Organic change: plus 65. Clicks are not unique people or causal business outcomes.
Paid clicks fall by 65 and organic clicks rise by 65. Combined clicks remain 200. The qualified-organization count falls by one, about 1.61% of the original 62.
Those calculations are descriptive. The click rows do not count unique people. The organization row covers a different business population across acquisition routes, so dividing it by the 200 branded clicks would not produce a valid conversion rate. The table does not connect individual clicks to organizations or establish what caused the difference.
Perhaps demand changed between the periods. Perhaps a product announcement affected branded searches. Perhaps another campaign picked up some exposure. Even with a perfectly reconciled report, the missing counterfactual remains: what would the same business have experienced during period B under the alternative advertising policy?
The all-route qualified-organization count declines from 62 to 61, a difference of one. That business population is separate from the branded-click population, so dividing organizations by the click total would not give a valid conversion rate.
Read the diagram
Reporting period A: 62 qualified new-business organizations across acquisition routes. Period B: 61. Demand and product changes remain possible explanations. The outcome under the alternative advertising policy is unobserved.
Google’s paid and organic report can help review text-ad and organic visibility together. It requires a Search Console connection, and organic history begins when that data starts being imported. Organic results are not restricted by the ad campaign’s geographic targeting, so apparently aligned rows can cover different reach.
Use that report to investigate how people find the site. Do not treat it as a causal sales report. Likewise, changing attribution models reallocates credit among observed interactions; it does not reveal which customers would have vanished without an ad. The attribution-model guide explains that separate reporting task.
Ternbridge keeps the pause-and-compare proposal as an observational option if no stronger design is feasible. It stops calling it proof before the design has earned that description.
Choose the comparison before touching campaigns
A familiar Google Ads experiment may compare two bidding approaches while both groups continue to receive advertising. That can answer a bidding question. The own-brand question needs a design that can withhold the selected exposure from a suitable comparison group.
Google documents user-based and geographic Conversion Lift approaches. Access is not universal, and supported campaign types, conversion requirements and study conditions differ. The user-based setup guidance is a separate reference from the geographic route. A feature described in documentation is not evidence that Ternbridge, or your account, can use it for the exact proposed scope.
The current geographic Conversion Lift documentation includes Search among supported campaign types, requires single-country campaigns and provides a feasibility review. It also directs advertisers to their Google account representative regarding access. Confirm those details for the actual study instead of borrowing eligibility from an unrelated campaign.
Changing a bid strategy while both groups receive ads answers a different question from withholding selected brand exposure. The comparison must match the actual policy the business is considering.
Read the diagram
Bid strategy comparison: strategy A with ads on versus strategy B with ads on. Selected-ad holdout: ads on versus selected ads withheld. Different questions require different exposure.
A specialist-designed geographic experiment is another possible route. Google’s primary geo-experiment research describes assigning non-overlapping regions to conditions and implementing those conditions through geographic advertising. The research establishes a methodology to investigate; its existence does not validate an improvised regional comparison.
Ask the person designing the study to explain three things plainly: what is assigned, what differs between the groups, and how the measured outcome supports the business question. They should also be able to explain where contamination, missing data or ordinary variation could weaken the answer.
Verify the account, outcome data and proposed question before choosing a route. An available platform feature or a specialist proposal still needs to be suitable for this specific comparison.
Read the diagram
First verify access, data and the study question. Possible routes: native study if eligible and suitable; specialist-led geographic study if feasible; defer or revise when evidence would be insufficient.
For Ternbridge, a study that withholds the defined brand advertising is relevant. A study that merely replaces the bid strategy in half the traffic is a different proposal. A study covering all Search spend is broader than the agreed brand question. Either broader proposal might be worthwhile, but it needs its own rationale and spending decision.
Do not select a method solely because it is available in a menu. Establish whether it can answer the question with the account’s campaigns, outcome data and operating constraints. The existing campaign-experiment guide covers ordinary supported comparisons without pretending they all create an advertising holdout.
Make the geography fit the question
Ternbridge is based in Toronto and sells across Canada and the United States. Someone suggests turning brand ads off in Canada while leaving them on in the US.
That would change advertising in two markets with different demand, competitors, customer mixes and business conditions. Calling one the control does not make those differences disappear. A national before-and-after comparison may help describe what happened; it does not become randomized evidence by using two countries.
For the geographic platform route described above, single-country scope is also an eligibility condition. Ternbridge starts by assessing a Canadian design and would evaluate a US study separately. The company’s office address does not determine the only useful test population, but neither does international reach guarantee enough suitable regions.
Ternbridge’s Toronto headquarters identifies the company location, not a control group. Assess a candidate Canadian study within one country and evaluate US work separately instead of assuming the countries are comparable.
Read the diagram
Toronto headquarters: company location. Canada: candidate single-country study. United States: assess separately. Countries are not a ready-made control.
A regional design needs more than two similar-looking totals. The analyst should examine pre-study behaviour, outcome frequency, concentration in a few large accounts and the relationship between regions. A region dominated by one unusual contract can behave very differently from another with many smaller opportunities, even when their totals match.
Ask what historical evidence supports the comparison and what the method assumes. How were regions selected or assigned? Can the outcome be measured consistently there? What size of business effect could the design reasonably distinguish within the proposed exposure? Those answers should precede the campaign edits.
A company serving only a small local area may lack a useful geographic design. A national SaaS company may also struggle if its buying committees span regions or qualified outcomes are sparse. Neither situation is fixed by drawing smaller polygons until the spreadsheet has more rows.
A buying committee can connect several regions while producing only one organization-level outcome. Adding map tiles does not create independent observations or solve sparse outcomes.
Read the diagram
One buyer organization. Committee spans region boundaries. Sparse outcomes. More tiles do not create independence.
The practical outcome can be a no-go decision on the proposed study. Ternbridge’s team would rather learn that the design is weak before withholding advertising than spend weeks collecting data that cannot settle the argument.
If the best feasible evidence remains observational, label it accordingly. The business can still combine those observations with costs, operational needs and a cautious spending policy. It should not present the resulting judgement as a measured causal effect.
Check what could cross the boundary
Imagine the account map looks clean: one set of regions retains the selected brand ads and another has them withheld. The next question is whether delivery and business activity will actually respect that plan.
One risk is substitution through another campaign. A second is budget movement. If withholding spend in some places causes a shared or constrained budget to be spent more aggressively in comparison regions, the comparison has changed in two ways.
Google’s geo-experiment implementation guidance specifically discusses budget redistribution in go-dark designs and separating campaign resources across experimental conditions. This makes budget behaviour part of study design, not a bookkeeping task to inspect only after a surprising result.
Withholding selected exposure can change where a shared budget is spent. Review delivery and spending together so the comparison does not silently include greater advertising elsewhere.
Read the diagram
Shared budget connected to selected exposure and other campaign activity or comparison regions. Redistribution is a potential mechanism, not a measured flow. Verify how spending actually behaves.
People and buying groups can cross the boundary too. Someone may see an ad while travelling and convert from another region. Colleagues may share a product link across offices. The geographic lift documentation discusses contamination between experimental areas. Actual exposure and outcome mapping deserve review even when the location settings were saved correctly.
Ternbridge adds a short integrity plan. The account owner checks whether the intended campaigns serve in the intended places. The analyst checks outcome completeness and geographic assignment. Sales records material changes in qualification or availability. Marketing logs promotions and product announcements that might affect regions differently.
Keep campaign delivery, outcome coverage and material incidents in the same review process. Fix a customer-facing fault when necessary, then record what changed and assess the effect on the study.
Read the diagram
Campaign delivery: actual ad exposure. Outcome coverage: complete, assigned records. Material incidents: fix, log and review impact.
This is not an invitation to continually improve only the advertising-on group. Routine changes can alter the comparison. If a customer-facing fault requires correction, fix the problem and document its effect on the study rather than preserving harm for experimental neatness.
The initial plan should also identify the ordinary operating state to restore after the study. Paused placeholders, duplicated campaigns, exclusions and temporary budgets can otherwise become an accidental long-term setup.
Agree the campaign changes, budget limits and verification steps with the account owner before implementation. The planning deliverable should make the proposed change and its boundaries clear.
Set the evidence and spending conditions together
Ternbridge’s finance lead wants to know how much uncertainty the business is paying to reduce. That question belongs before launch.
Begin with the decision’s scale. Is the company considering a modest adjustment to one campaign, or removing a substantial part of its acquisition programme? What loss of useful demand would make the change unacceptable? What improvement would be large enough to justify implementation and ongoing administration?
Those conditions come from the business. A universal percentage or fixed test budget would ignore customer economics, outcome frequency and the consequences of a mistake.
The business sets the acceptable demand loss, the effect worth acting on and the cost it will bear to learn more. Those commercial limits guide the design but do not themselves establish statistical precision.
Read the diagram
Agree before launch: acceptable demand loss; useful effect size; cost of learning. Commercial limits and statistical precision are separate inputs.
Then ask whether the proposed method can answer at that level of precision. A long record of clicks does not necessarily supply enough new qualified organizations. Replacing the primary outcome with page views may make the report fill faster while abandoning the decision that mattered.
Ternbridge can use qualified organizations as a nearer-term business outcome if the team agrees that this answers a narrower question. It would still need separate evidence or a clearly labelled model to connect that result with signed customers. A historical close rate is an assumption when applied to additional opportunities; it is not a guarantee about them.
The analyst should state the planned analysis and uncertainty standard before reviewing the preferred result. If the platform provides feasibility or study-power information, preserve its definition and limitations. A favourable feasibility estimate is not an assurance that the ads will produce positive lift or that the final answer will justify their cost.
A large volume of clicks can coexist with too few qualified organizations to inform the chosen decision. Assess feasibility for the business outcome and keep the question intact if measurement needs revision or the study must be deferred.
Read the diagram
Many clicks measure activity volume. Qualified organizations are the chosen business outcome. Assess feasibility: feasible, revise measurement or defer. Keep the business question intact.
Agree on the exposure boundary, financial limits, planned end state and incident conditions. Stopping further exposure and completing outcome analysis may happen at different times. A tracking failure might invalidate part of the study without revealing whether the campaign was valuable.
Do not extend a fixed-horizon study repeatedly until it produces the desired label. Any extension needs a defensible analysis approach and a fresh assessment of the value and cost of more information. Ending with an unresolved answer can be preferable to paying indefinitely for precision the available design cannot provide.
Know when the outcomes are ready
Brand searches often occur late in a buying journey, but that does not mean every associated opportunity is ready to classify when the ad stops serving.
For Ternbridge, a product evaluator may return to a pricing page, request a technical review and take weeks to reach procurement. A demo completed yesterday can be a genuine step without yet being a qualified organization or a signed customer under the chosen definition.
Separate the period when advertising differs from the period needed to observe the relevant outcomes. Record the date basis used by each report and how pending cases will be handled. Keep those rules consistent across the comparison.
The selected advertising exposure may end before evaluations and procurement decisions are ready to classify. Use the same outcome definition and observation rule across the comparison.
Read the diagram
Ad-exposure track ends. Outcome-observation track continues through evaluation to classification under the agreed rule. The groups use the same rule and observation period.
Google’s conversion-lag guidance explains why recent acquisition-cost or return figures can change as conversions arrive. Use the account’s relevant history and the study method rather than importing a universal waiting period. A mature platform conversion count can still be an immature sales result if the measured action happens much earlier than qualification.
If a lift report includes modelled or projected outcomes, label them as such. Do not quietly combine an estimate of future conversions with realized contracts and call the total completed business. Google’s user-based lift reporting guidance describes projected delayed outcomes for particular study types. Check the selected method’s definitions rather than assuming every lift report handles the delay in the same way.
Ternbridge’s review packet has three status labels: observed and ready under the agreed definition; pending; and unresolved because data are missing. Pending is not the same as lost. Missing is not the same as zero.
Separate outcomes that are ready under the definition from those still pending and those unresolved because data are missing. These statuses affect what can be concluded and should not be collapsed into losses or zeros.
Read the diagram
Observed and ready. Pending is not lost. Missing is not zero. Keep the original cohort and agreed classification rule.
The original cohort also stays intact. A later contract from an earlier evaluation should be handled under the agreed observation rule, rather than moved into a new period merely because that produces a cleaner comparison.
This discipline prevents an apparently decisive result from depending on who was given more time. It also lets leadership receive an operating update while the final business conclusion remains open.
Read the result before calculating a return
When the study ends, start with what was actually implemented. Did the selected advertising differ as intended? Did the outcome records remain complete enough for the analysis? Did a regional promotion, outage or campaign change materially alter the comparison?
An analysis can be mathematically correct for data that no longer answer the original question. Resolve that issue before celebrating the result.
The report should identify the outcome, population, period, estimated effect, uncertainty and important assumptions. Google distinguishes incremental results from ordinary attributed conversions in its Conversion Lift measurement documentation. The campaign’s credited total is not an interchangeable substitute for the study estimate.
Read the result as a complete packet: what was measured, where and when, how large the estimate is, how uncertain it is and which limitations matter. The campaign’s attributed total belongs to a separate reporting field.
Read the diagram
Result packet fields: outcome; studied population; period; effect estimate; uncertainty; assumptions and limitations. Attributed conversions remain separate. No result values are supplied in this planning diagram.
A positive estimate with wide uncertainty can leave both a worthwhile benefit and an uneconomic result plausible. A narrow estimate can still be too small to matter commercially. Evidence consistent with little effect does not automatically establish exact equivalence, especially if the study was incapable of detecting the difference the business cares about.
Do not manufacture a confidence interval from two aggregate totals or borrow a platform’s confidence label for a different outcome. If the primary report measures form events and a separate analysis measures qualified organizations, the latter needs an analysis appropriate to its data and assignment.
For a geographic study, unequal region size, pre-period relationships and the chosen estimator can affect interpretation. Simply subtracting one region’s raw total from another is not a universal lift calculation. Ask the analyst to make the result understandable without hiding the method or replacing it with a homemade shortcut.
A valid comparison can still leave the commercial decision unresolved because precision is insufficient or the effect is too small to matter. An invalid comparison belongs outside that interpretation matrix and needs redesign, regardless of precision.
Read the diagram
Commercial relevance and evidence precision are separate dimensions for interpreting a valid comparison. An invalid comparison leads to redesign; greater precision cannot repair it. No plotted position is a measured Ternbridge result.
“The study could not resolve our commercial threshold” is a different statement from “brand advertising contributes nothing.” Likewise, “the tested ads increased the measured outcome in this setting” does not establish that every future brand campaign should receive unlimited spend.
Work out what the additional customers would need to earn
Before seeing a result, Ternbridge can make its economic question explicit without pretending to know the answer.
Consider a separate fictional planning scenario for a defined acquisition period. Keeping the selected advertising would cost CAD 18,000 in media plus CAD 4,500 in other campaign costs that would genuinely change with that policy. The total incremental cost being considered is CAD 22,500.
Assume an additional acquired customer would contribute CAD 7,500 over its first 12 months after the direct delivery costs included in the example’s definition. That contribution excludes the CAD 22,500 campaign costs, which are deducted separately. All figures are assumptions for teaching, not observed Ternbridge results, pricing benchmarks or a forecast.
Include the costs that genuinely change between the advertising policies. The example adds media and other changing campaign costs; unchanged allocated overhead and the one-time cost of the study are considered separately.
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CAD 18,000 media plus CAD 4,500 other changing campaign costs equals CAD 22,500. Unchanged allocated overhead and one-time study costs are outside this policy-cost example.
| Additional customers caused by keeping the selected ads: assumed scenario | First-year contribution at CAD 7,500 each | Contribution less CAD 22,500 campaign costs |
|---|---|---|
| 0 | CAD 0 | −CAD 22,500 |
| 1 | CAD 7,500 | −CAD 15,000 |
| 2 | CAD 15,000 | −CAD 7,500 |
| 3 | CAD 22,500 | CAD 0 |
| 4 | CAD 30,000 | CAD 7,500 |
The calculation is straightforward: CAD 22,500 divided by CAD 7,500 is three additional customers to cover the included campaign costs. Three produces break-even under these assumptions, not a desirable return. The business may require more to compensate for risk and other demands on its resources.
The difficult input is additional customers caused by the advertising. Attributed customers cannot simply be inserted into that column. Nor can an incremental qualified-opportunity estimate become a customer count without a further assumption about conversion to contracts.
Each assumed additional acquired customer contributes CAD 7,500 in the first year before the included campaign costs. Three cover CAD 22,500 exactly; four leave CAD 7,500. Attributed customers and qualified organizations cannot be substituted for this causal customer input.
Read the diagram
Assumed additional customers and contribution less CAD 22,500: zero, minus CAD 22,500; one, minus CAD 15,000; two, minus CAD 7,500; three, CAD 0, break-even; four, plus CAD 7,500. These are conditional scenarios, not forecasts or study estimates.
Keep the horizon consistent. First-year contribution cannot be compared casually with lifetime revenue, and an annual contract value says little by itself about delivery cost or cash collection. The ROAS and ROI guide covers those financial definitions more fully.
Have finance distinguish avoidable costs from allocated overhead. If the company would pay the same management fee after reducing one brand campaign, that fee is not automatically a saving from this particular decision. If a study needs a one-time analysis project, that is a cost of obtaining information and should be evaluated separately from the ongoing policy comparison.
The threshold is sensitive to assumptions. If first-year contribution were CAD 6,000 instead, CAD 22,500 would require 3.75 customers in the arithmetic, meaning at least four whole customers to cover the stated costs. Four would leave CAD 1,500. This does not predict four wins; it shows why finance must review the contribution assumption before an impressive-looking lift estimate becomes a budget recommendation.
Lower contribution per customer raises the threshold while campaign costs stay fixed. At CAD 6,000, the arithmetic requirement is 3.75 customers, so four whole customers are needed to cover the stated costs and leave CAD 1,500.
Read the diagram
CAD 22,500 costs. At CAD 7,500 first-year contribution per additional customer: threshold three and whole-customer requirement three, leaving CAD 0 at three. At CAD 6,000: threshold 3.75 and whole-customer requirement four, leaving CAD 1,500 at four.
Make the decision the evidence can support
Ternbridge’s initial meeting began with a cheap brand-demo report and a proposal to pause advertising. It ends with a more specific plan and several limits the team can explain.
The company has defined the brand-Search exposure it wants to evaluate, separated new-business organizations from repeat contacts, and rejected Canada-versus-US as a ready-made controlled comparison. It has a proposed economic threshold, while recognizing that a study of qualified organizations would not directly establish additional customers.
The next decision is whether the actual account, regional data and outcome frequency can support the chosen study. Until that feasibility work is complete, the fictional team has no measured basis for declaring the brand campaign essential or unnecessary. Any interim spending policy remains a business judgement made under uncertainty.
Ternbridge has made the spending question more precise and rejected a premature causal conclusion. Its next decision is whether the actual account, geographic data and outcome frequency can support a useful study.
Read the diagram
Defined: selected brand-Search exposure and qualified new-business organizations. Rejected: Canada versus US as a ready-made control. Open: account and study feasibility, and the eventual result. Next: feasibility before a campaign-value verdict.
Your eventual action should follow the evidence:
- Keep the defined policy when the relevant result and economics support it, preserving the limits of the tested market and period.
- Reduce or remove the selected spend when a defensible analysis supports that choice and the business accepts the customer-journey consequences.
- Redesign the study when exposure leakage, outcome definitions or unsuitable comparisons prevented an answer.
- Retain an explicitly provisional policy when no feasible study can resolve the decision at an acceptable cost.
Avoid combining the result with several untested changes. Cutting brand advertising, doubling category spend and rebuilding the landing page together creates a new operating policy. It may be a sensible proposal, but the original test does not automatically validate the package.
After any authorized change, check the resulting account state and the relevant business outcomes. Temporary experiment settings should not remain active by accident. Record what was restored, what was adopted and which later changes would make the original finding less relevant.
Finish an authorized change by checking the resulting account configuration. Record what was restored, what was deliberately adopted and what still needs resolution so temporary settings do not become a permanent policy by accident.
Read the diagram
Review temporary configuration, including paused placeholders, exclusions, budgets and duplicate campaigns. Restore, adopt or resolve deliberately. Verify the final account state.
A short decision note is enough if it contains the important facts: what changed in the comparison, what was measured, what the result supports, what remains uncertain and what the business has chosen to do. Keep the supporting analysis available. A permanent rule such as “always bid on brand” or “never pay for brand” throws away the conditions that made the evidence useful.
The accompanying brand-spend worksheet helps prepare that discussion without connecting an account. Use it to establish the question and missing evidence, rather than treating completed boxes as proof that a study is valid.
Download the brand-spend decision kit (ZIP) or the printable worksheet (PDF). The kit includes an editable worksheet and the guide’s static teaching examples.
Attach the spending choice to the tested exposure, measured outcome, supported finding and remaining uncertainty. Keep the market and period boundaries visible when deciding whether later circumstances justify a new review.
Read the diagram
Decision note: what changed; what was measured; what the evidence supports; what remains uncertain; what policy was chosen. Preserve the supporting analysis and the limits of the tested setting.
If your team needs help separating brand-campaign reporting from a defensible spending decision, start with Canada Create’s Google Ads service. Bring the campaign scope, outcome definitions and the decision you need to make. Custom-scoped engagements; proposal after discovery.
