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Cutting CAC to one-third by aligning landing pages with acquisition intent

An SEA long-tail campaign purchased very precise intentions but directed them towards too generic experiences. Creating pages adapted to the intent and product simultaneously improved conversion and acquisition economy.

  1. ÷ 3CAC observed on the campaign
  2. +50%average conversion observed
  3. Long-tailnew intentions made addressable

This feedback comes from a project prior to CAIAC. It illustrates the operational mechanisms that contributed to the platform's design. The results are specific to the project context and do not constitute a performance guarantee.

The challenge

Why the business result was out of reach

Acquisition teams often work with great precision on keywords, audiences and auctions. But after the click, several different intentions arrive on the same page.

This break reduces conversion: the desired product is not visible, the vocabulary no longer corresponds to the request, and the user has to rebuild his path alone.

In the technical categories, friction is particularly costly — traffic is limited, products have a high value and each search expresses a precise expectation. The CAC therefore depends not only on the price of the click, but on the whole chain between intent, page, product and conversion.

The starting point

Data, processes and constraints

  • An e-commerce specialized in technical equipment with high basket.
  • A catalogue sought through very specific long-term terms: little volume per request, but a strong commercial intent.
  • A partner agency wishing to quickly test new campaigns without engaging a long cycle of manual production for each combination.
The approach

What was implemented

  1. 01
    Mapping intentionsConsolidate keywords by need, category and level of accuracy — not one page by expression without discernment.
  2. 02
    Select the corresponding productsMake a selection really consistent with the query, not a generic set of popular products.
  3. 03
    Adapt promise and contentTitle, introduction and structure reflecting the expressed problem: clarify the choice, not repeat the keyword.
  4. 04
    Compare performanceTest the new pages against the existing device, over several weeks.
Results

What has been measured, and what it means

  1. Observed result

    The campaign reported a three-fold CAC, an average increase of 50% and better coverage of long-tail intentions.

    The first weeks showed results close to the existing system; the gap widened in the following weeks.

  2. Interpretation

    These results document a device, not a general law. What they suggest: the alignment between intent and experience after click can improve the already purchased traffic economy. They do not make it possible to say that the landing page has caused only the decrease of the CAC — traffic, auction, expenses, product mix and attribution remain to be documented.

Why does it matter

How this translates into business impact

  • Reduce waste after click

    A campaign can buy the right audience and lose its value on a bad destination.

  • Address niche search intents

    Manual processing of a low volume request often costs more than its potential.

  • Protecting the margin

    A lower CAC provides flexibility on auctions and allows for niches that competitors abandon.

  • Accelerate testing and learning

    The results indicate what intentions and messages should be extended.

The principle

What this project inspired in CAIAC

This project has taught us that media optimization does not stop at governance: when intent is precise, the page becomes a direct component of targeting and acquisition economy.

CAIAC today industrializes this principle: requests, intentions, product selection, dedicated page, checks, validation, testing and measurement in the same chain. Neither pages, campaign parameters, nor algorithms of the historical project are included.

About this feedbackShow less

This feedback comes from a project prior to CAIAC. It illustrates the operational mechanisms that contributed to the platform's design. The results are specific to the project context and do not constitute a performance guarantee.

Items to be documented:

  • Comparative expenditure between the two schemes.
  • Distribution of traffic and allocation windows.
  • Conversion volumes and test duration.
  • Possible average basket deviations.
  • Statistical significance of the result.

Anonymized project · pre-CAIAC · unsecured results. Deployment is adapted to the data, systems and levels of autonomy available in the company environment.

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