Upper Funnel vs. Lower Funnel Marketing: What’s the Difference and How Do You Measure Each?

• Upper funnel marketing builds demand among people who are not yet shopping (awareness and consideration channels like TV, CTV, online video, and social prospecting). Lower funnel marketing captures demand that already exists (branded search, retargeting, shopping ads, affiliates).
• Last-touch attribution systematically over-credits the lower funnel and under-credits the upper funnel, because lower funnel channels sit closest to the conversion and intercept demand that other channels created.
• The fix is measuring incrementality instead of attribution: what each tactic causes, not what it touches. In one pair of Measured geo holdout tests, an upper funnel prospecting tactic contributed 8.92% of the brand’s total orders while lower funnel remarketing contributed 0.54%, even though platform reporting made the two look nearly identical.
• Incrementality data also revealed the platform over-credited remarketing by roughly 2.6x (only about 39% of its claimed conversions were incremental), while prospecting’s platform numbers were close to accurate (about 93% incremental).
• Healthy media plans need both funnel stages. The mistake is not investing in the lower funnel, it is sizing the funnel stages based on attribution reports that cannot tell demand creation from demand capture.

Ask an attribution report which half of your funnel is working and it will give you a confident, precise, and wrong answer. Lower funnel tactics like branded search and retargeting will dominate the conversion columns. Upper funnel investments like video and prospecting will look expensive and unaccountable. Marketers have been reallocating budget on that picture for a decade, and it is one of the most expensive systematic errors in the industry.

This guide defines upper funnel and lower funnel marketing, compares the two side by side, and then covers the part most explainers skip: how to actually measure each one, including real incrementality test results that show how differently the two behave once you strip out attribution bias.

Upper funnel marketing (also called top of funnel or demand creation) targets people who are not currently in the market for your product. The goal is to create future demand: build awareness, shape perception, and put your brand into the consideration set before a purchase intent exists.

Typical upper funnel channels: linear TV and CTV, online video (YouTube), podcast and streaming audio, display prospecting, paid social prospecting, influencer and sponsorship activity, and out-of-home.

Typical goals and signals: brand awareness, brand search volume, new-to-brand customers, reach among unexposed audiences, and, ultimately, incremental revenue that appears on a delayed timeline.

The defining economics: upper funnel effects are delayed and diffuse. Someone sees a CTV ad this week and buys in three weeks through a branded search click. The value is real, but it does not arrive attached to the ad that caused it, which is precisely why click-based systems miss it. For a full treatment of the demand creation side, see Demand Creation vs. Demand Harvesting: Which Channels Actually Build Your Business.

Lower funnel marketing (also called bottom of funnel or demand capture) targets people who already have purchase intent. The goal is to convert existing demand efficiently: be present at the moment someone is ready to buy and remove friction from the path.

Typical lower funnel channels: branded paid search, non-brand search on high-intent queries, shopping ads, retargeting and remarketing, affiliate, cart abandonment email and SMS.

Typical goals and signals: conversions, revenue, ROAS, cost per order, and conversion rate.

The defining economics: lower funnel effects are immediate and easy to observe, but they are bounded by the demand that exists. You cannot capture more intent than the market contains, which is why lower funnel channels saturate quickly and why scaling them rarely scales the business. They also sit closest to the conversion event, which brings us to the measurement problem.

Last-touch and multi-touch attribution assign credit based on observed touchpoints in a conversion path. That design has a built-in tilt: whichever channel sits closest to the purchase collects the credit, regardless of what caused the purchase.

A customer sees a CTV ad, considers for two weeks, searches your brand name, clicks the paid search ad, gets retargeted once, and buys. Attribution credits branded search and retargeting. The CTV ad that created the demand gets nothing. Multiply that across every customer journey and the report reads as a mandate: cut the upper funnel, pour money into the bottom.

Brands that follow that mandate discover the trap on a delay. Lower funnel performance holds for a quarter or two while the brand spends down the reservoir of demand its upper funnel built, then branded search volume shrinks, CPCs rise, and the “efficient” channels quietly run out of demand to capture. The full mechanics of this failure mode, and what replaced attribution, are covered in Multi-Touch Attribution Is Dead. Here’s What Replaced It and Attribution vs. Incrementality.

The measurement principle that fixes this is incrementality: instead of asking which ads touched a conversion, ask which conversions would not have happened without the ad. That question treats both funnel stages fairly, because it is answered with experiments rather than click paths.

Upper funnel measurement has to solve two problems: the effect is delayed, and it rarely produces a trackable click. Three approaches, in order of rigor:

1. Incrementality testing (geo experiments). A geo holdout test pauses the upper funnel tactic in scientifically selected test markets and compares observed transactions against a modeled prediction of what would have happened with the tactic still running. Because it reads results from transaction data rather than click paths, it captures delayed and view-based impact that attribution structurally misses. This is the ground-truth method for TV, CTV, video, audio, and prospecting. See How to Run Geo Testing for Marketers for the mechanics.
2. Media mix modeling with adstock. MMM handles the delayed-effect problem statistically through adstocking, which distributes an upper funnel tactic’s impact across the weeks following the spend. Calibrated with geo test results, MMM extends experimental ground truth into an always-on read for every week you do not have a live test.
3. Brand and leading indicators. Brand search volume, direct traffic, branded query growth, and new-to-brand customer share are useful corroborating signals. They should support the causal read, not replace it: leading indicators can tell you direction, but only experiments can tell you magnitude in dollars.

One thing that does not work: judging upper funnel channels by platform-reported or last-click ROAS. By design, those systems route upper funnel credit to whichever lower funnel channel closes the sale.

The lower funnel looks easy to measure, and that is exactly the trap. Every platform reports conversions and ROAS in real time, so most teams take the dashboard at face value. But platform-reported conversions for lower funnel tactics include a large share of baseline conversions: purchases from people who were going to buy anyway, intercepted on their way to checkout.

Retargeting is the canonical case. It targets people who already visited your site, meaning the audience is pre-selected for purchase intent, meaning the platform will claim credit for conversions it merely witnessed. Branded search has the same structure: many of those clickers would have found you through the organic listing one inch below the ad.

So the lower funnel needs incrementality testing too, not to detect an invisible effect the way the upper funnel does, but to correct an inflated one. The test adjustment factor from a holdout tells you what share of platform-claimed conversions were actually caused by the tactic, and applying it converts platform ROAS into incremental ROAS you can plan against. For more on running these tests, see What Is Incrementality Testing and the video on retargeting incrementality measurement.

Here is what the asymmetry looks like when the same brand tests both funnel stages on the same platform with geo holdouts.

An ecommerce retailer ran geo holdouts on two Meta tactics: prospecting (upper and mid funnel demand creation, reaching people who had never engaged with the brand) and remarketing (lower funnel demand capture, retargeting prior site visitors). In the platform dashboard, the two tactics looked interchangeable, reporting essentially the same ROAS and cost per order.

The holdouts told a different story:

• Prospecting contributed 8.92% of the brand’s total orders (80% confidence interval of 7.67% to 10.18%) at 93% statistical significance. Turning it off in the test markets produced a shortfall of more than 10,000 orders against the predicted baseline. Its test adjustment factor came in around 93%, meaning nearly every conversion the platform credited to prospecting was genuinely incremental.
• Remarketing contributed 0.54% of total orders (80% confidence interval of 0.47% to 0.62%) at 96% statistical significance. Its adjustment factor came in around 39%: roughly 6 in 10 conversions the platform claimed for remarketing would have happened anyway. The platform was over-crediting the tactic by about 2.6x.
• On an efficiency basis the two were closer than the contribution gap suggests: prospecting delivered a $3.88 incremental ROAS (80% confidence interval of $3.33 to $4.43) and remarketing $3.09 ($2.65 to $3.52). Remarketing was not worthless. It was simply small, near its ceiling, and dramatically over-represented in platform reporting.

The lesson is not “kill the lower funnel.” Remarketing earned a positive incremental return. The lesson is that platform reporting was incapable of showing which tactic was building the business. One tactic drove nearly 9% of all orders and could absorb more budget; the other drove half a percent and had little room to grow. Attribution rendered them identical. Only the experiments separated them. For more results in this pattern across more than 1,000 Google and Meta geo tests, see Fixing the Funnel with Incrementality Measurement.

There is a well-known industry heuristic from Les Binet and Peter Field’s IPA research suggesting roughly 60% of budget toward brand building and 40% toward activation for long-term growth. It is a useful corrective for attribution-driven plans, which typically land far below that on the brand side. But it is a population average, not your answer.

The better approach is to let your own incrementality data set the split. Each tested tactic gets an incremental ROAS and a response curve showing where additional spend stops producing additional return. Lower funnel tactics typically saturate early, which means the marginal dollar often earns more in demand creation even when average returns look similar. From there, budget allocation becomes an optimization problem rather than a philosophy debate: fund each tactic to the point where its marginal incremental return matches the alternatives. A practical starting sequence is to test your largest lower funnel line items first (branded search and retargeting, where over-crediting is most likely), reallocate the exposed non-incremental spend into upper funnel tests, and rebalance as each new result lands. Guidance for structuring the creation-side investment is in the brand campaign guide, and the prospecting-versus-retargeting balance specifically is covered in this video.

This is the approach Measured operationalizes for 160+ enterprise brands: geo experiments establish each tactic’s true contribution, those results calibrate an always-on incrementality model, and budget moves to wherever the next dollar earns the most incremental return, upper or lower funnel, without attribution bias putting a thumb on the scale.

What is the difference between upper funnel and lower funnel marketing?

Upper funnel marketing creates demand among people who are not yet shopping, using awareness and consideration channels like TV, CTV, online video, and social prospecting. Lower funnel marketing captures demand that already exists, using channels like branded search, shopping ads, and retargeting that reach people with active purchase intent. Upper funnel effects are delayed and build the future customer base; lower funnel effects are immediate but bounded by existing demand.

Why does lower funnel marketing look better in attribution reports?

Because attribution assigns credit to the touchpoints closest to a conversion, and lower funnel channels sit at the end of nearly every purchase path. Branded search and retargeting intercept buyers whose demand was created elsewhere, so they collect credit for conversions they did not cause. Incrementality testing corrects this: in Measured test data, a lower funnel remarketing tactic had roughly 61% of its platform-claimed conversions exposed as non-incremental, while an upper funnel prospecting tactic’s platform numbers were about 93% accurate.

How do you measure upper funnel marketing?

The most rigorous method is geo incrementality testing: pause the upper funnel tactic in selected test markets and measure the shortfall in actual transactions against a modeled baseline. Because it reads business outcomes rather than clicks, it captures the delayed, view-based impact that click attribution misses. Media mix modeling with adstock effects, calibrated by those experiments, extends the measurement to an always-on view. Brand search volume and new-to-brand customer share serve as supporting indicators.

Is upper funnel or lower funnel marketing better?

Neither works alone. Lower funnel channels convert efficiently but only capture demand that exists, and they saturate quickly. Upper funnel channels create the demand that lower funnel channels later harvest. The practical question is the split, and the answer should come from incrementality data: fund each tactic to the point where its marginal incremental return matches the alternatives, rather than sizing the funnel from attribution reports that structurally favor the bottom.

What are examples of upper funnel and lower funnel channels?

Upper funnel: linear TV, CTV and streaming video, YouTube, podcast and streaming audio, display prospecting, paid social prospecting, influencer, and out-of-home. Lower funnel: branded paid search, high-intent non-brand search, Google Shopping, retargeting and remarketing, affiliate, and cart abandonment programs. Mid funnel tactics like non-brand search on category terms and engaged-audience social sit between the two.

How much should I spend on upper funnel vs. lower funnel?

Industry research from Binet and Field suggests roughly 60% brand building and 40% activation as a long-term growth benchmark, but the right split is brand-specific. Use incrementality testing to establish each tactic’s true incremental return and response curve, then allocate to equalize marginal returns. Most brands that do this discover their lower funnel is over-funded relative to its incremental contribution, because years of attribution-guided decisions steered budget toward the tactics that looked best in the report.

• Binet, L. and Field, P., IPA effectiveness research on brand vs. activation budget split (the 60/40 rule, from “The Long and the Short of It” and “Media in Focus”). Please confirm preferred canonical source and link before publish.
• Measured geo holdout test data (demo environment, tests 5989 and 6000)