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How to Maximize Impression Share Without Sacrificing Efficiency: The Mabe Case Study
Executive Summary
Mabe Mexico, within its Post-Sales Service division, faced a complex challenge in its brand search campaigns: protecting brand visibility against competitors and unauthorized service providers without sacrificing investment efficiency. To achieve this, it relied on a Target Impression Share (tIS) bidding strategy in Google Ads, prioritizing visibility over conversions.
With Mixilo, Mabe increased conversions by 13%, reduced CPA by 23%, and lowered spend by 12%, even as the market contracted by 31% in eligible impressions. The results validated that marginal efficiency and brand protection are not mutually exclusive; they are complementary when budget allocation is properly optimized.
Challenge
Mabe is a global leader in the home appliance industry. With 75 years of history, presence in more than 70 countries, 17 leading brands, 11 state-of-the-art manufacturing plants, 24,000 employees, and 13 million appliances sold annually, it stands as one of the most important companies in the sector worldwide.
Mabe operates in a highly competitive environment where third parties actively bid on its branded terms, replicating messaging and experiences to capture demand intended for official service. To mitigate this risk, Brand campaigns use Target Impression Share to secure dominant visibility in search results.
However, this strategy often leads to overspending if it is not optimized based on final conversion outcomes.
Solution
After confirming Mixilo’s positive impact on performance campaigns and leveraging the platform’s flexibility to optimize different funnel objectives, Mabe decided to extend the strategy to its Brand campaigns.
Implementation
To evaluate efficiency, the rollout was designed as a controlled experiment.
Duration
34-day testing period.
Conditions
- Mixilo recommendations were applied at least three times per week across all campaigns.
- Mixilo operated exclusively on the budget variable, without modifying Google Ads’ bidding logic.
- The Planner module was used to intelligently distribute available budget, allowing Mixilo to determine optimal daily spend based on seasonality.
- Campaigns remained active throughout the experiment, regardless of short-term performance fluctuations.
Hypothesis
Three possible scenarios were defined:
Scenario A – Positive Impact
Impression Share is maintained or improved while efficiency increases (more conversions and/or lower CPA).
Scenario B – Negative Impact
Efficiency improves, but Impression Share drops significantly.
Scenario C – Neutral Result
No meaningful changes in visibility or efficiency.
Given Mixilo’s ability to optimize across funnel stages, the objective was to achieve Scenario A: sustain or improve Impression Share while simultaneously increasing efficiency. The premise was clear: improve visibility and performance at the same time.
Key Results: Maximizing Coverage and Efficiency in a Contracting Market
During the 34-day experiment, the market experienced a 31% decline in eligible impressions. Despite this contraction, Mixilo’s implementation in Mabe’s Brand Search campaigns maximized coverage and nearly eliminated Impression Share loss due to budget constraints.
Business Impact:
- -12% spend
- +13% conversions
- -23% CPA
Importantly, these results were achieved without changing bidding strategies or platform optimization goals — solely through improved budget allocation across campaigns.
Performance Analysis
To understand the impact, results were analyzed across four dimensions:
- Reach & Budget Efficiency
- Traffic Quality & Costs
- Business Performance Impact
- Campaign-Level Impact
1. Reach & Budget Efficiency
For Target Impression Share campaigns, cost-weighted metrics were used to evaluate visibility based on where investment was concentrated.
2. Traffic Quality & Cost Impact
Improved budget distribution had a direct impact on auction dynamics and inventory quality.
3. Business Performance Impact
Although Mixilo’s AI engine was configured with the specific objective of maximizing conversions, the removal of structural inefficiencies generated a highly positive impact on business metrics.
As anticipated in Scenario A of the hypothesis, reducing investment with no incremental value allowed total spend to decrease by 12% without compromising brand visibility. This more efficient capital reallocation translated into a 13% increase in conversions and a 23% improvement in CPA.
4. Campaign-Level Impact
Shortly after optimization, the portion of Impression Share lost due to budget (“green zone”) dropped to nearly zero.
This created a double benefit:
- Campaigns reached their maximum effective coverage threshold.
- Remaining budget was redirected to other campaigns with incremental opportunity.
Manually determining optimal campaign-level budgets is difficult. Over-allocating budget risks underutilization and missed opportunities elsewhere.
To reach 100% Impression Share, the remaining lost opportunity due to Rank Lost Impression Share (“yellow zone”) must be addressed through in-platform optimizations (bids, ad quality, relevance). Mixilo facilitates this by clearly visualizing these constraints, enabling faster and more precise action in Google Ads.
How Mixilo Drove the Results
The experiment’s success was driven primarily by improved budget allocation — not by changes in Google Ads’ bidding logic, which continued prioritizing Target Impression Share.
Mixilo eliminated non-incremental spend and redistributed budget efficiently across campaigns, reducing Impression Share losses caused by budget constraints.
In this sense, Mixilo acts as a budget allocation layer operating above the platform’s bidding logic, identifying and removing marginal inefficiencies between campaigns competing for the same budget.
A key finding: after reallocation, several campaigns shifted from losing impressions due to budget constraints to losing impressions due to rank. This indicates budget was no longer the limiting factor and that further growth would depend on in-platform optimizations.
By correcting misallocated constraints, Mixilo unlocked incremental demand, increased conversions, improved CPA, and slightly increased Search Top Impression Share.
In other words, Mixilo removed inefficiencies in capital distribution — proving that brand protection and marginal efficiency can not only coexist, but reinforce each other.
Conclusion
This experiment allowed Mabe to empirically validate that efficiency and brand protection can be aligned by using Mixilo as a budget optimization layer — even in campaigns configured with Target Impression Share.
The results confirm that maintaining competitive Impression Share is fully compatible with improving conversions and reducing CPA.
This case demonstrates that intelligent budget allocation is not merely a tactical adjustment it is a structural growth lever. By delegating budget distribution to Mixilo, Mabe eliminated overspending, protected brand visibility, and improved business metrics simultaneously proving that branding and performance can amplify each other when marginal efficiency is managed systematically.
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