Performance vs. Brand: How to Find Balance and Use Data for Growth

"How can you properly balance investments between brand building and performance campaigns? And how can you connect the online and offline worlds in a way that ensures the brand not only communicates effectively but also grows in a measurable way? Together with experts from various industries, we set out to find the answers.

How Much Should You Invest in Brand Building?

Based on research and market-wide experience, it is clear that the minimum investment in brand building should be 30% of the total advertising budget. Ideally, brands should aim for around 60%. This level is often referred to as the "sweet spot" — the optimal balance that drives the most significant effect for both brand development and long-term performance growth. However, the right balance always depends on the specific situation of the company — how established it is in the market, which industry it operates in, and what its overall strategy is.

There was also discussion about the so-called "brandformance" mix — campaigns that attempt to combine brand building and performance marketing into a single message. However, results show that this approach is not effective. On the contrary, it works best when brand and performance campaigns run separately but simultaneously — each fulfilling its role while reinforcing the other.

Where Are Companies Today?

The discussion also included a survey in which participants indicated where they currently stand on the investment spectrum. The spectrum, illustrated in the attached chart (below), shows how different levels of investment impact campaign performance.

Investing in Brand Building: Finding the Right Balance

Risk Below 30% When brand investments fall below 30%, companies risk falling into what is known as the "doom loop" — an over-reliance on short-term performance without building brand equity, leading to a long-term decline in marketing effectiveness. Brands focus only on the immediate performance of activation campaigns and optimize for misleading metrics. For example, they may react to the immediate success of a discount campaign. However, if discounts become frequent, marketing managers are forced to offer even steeper discounts in future campaigns to maintain the same effect, accelerating brand erosion.

Investment Benchmarks

• 30–40% indicates room for optimization — still a performance-driven approach, but with initial efforts to build brand equity.

• 40–60% is considered the ideal zone for most brands, providing a healthy balance between brand building and performance.

• 60–75% shows a strong emphasis on brand building while still maintaining performance impact.

• Above 75% there is a risk that the brand loses connection with immediate in-market opportunities. In such cases, marketing managers rely solely on brand presence without actively engaging customers looking for immediate purchase incentives. That said, this approach can be justified in industries where dealers or third parties manage activation, as discussed.

Where Do Companies Stand Today? A survey conducted during the session showed that industries like travel, food, retail, energy, and cosmetics mostly invest within the recommended 40–60% range. Some brands, especially those with a strong brand legacy, invest even more — exceeding 60%. Conversely, industries like fast food, e-commerce, and optical retail often hover near the minimum recommended threshold, reflecting a heavy focus on short-term performance.

Don't Forget About Testing Another important point raised was the role of testing budgets. Allocating room for experimentation and testing new channels or messages is crucial — the recommended proportion for testing is between 5–10% of the total budget. For smaller brands, a lower share of 2–3% may be realistic, but it remains important to keep space for innovation.

From Data to Decisions

The session also focused on the importance of connecting the online and offline worlds so that brands can deliver a consistent customer experience across channels and measure impact effectively. Participants agreed that mapping the entire customer journey is increasingly seen as essential — but real-world practice faces several challenges.

One major hurdle is the saturation of the market with mobile apps. Consumers are becoming selective, only keeping apps they regularly use. When purchase frequency drops, apps quickly lose space on the home screen — even though these users often contribute significantly to sales. Keeping customers engaged requires a clear value proposition for the app and relevant incentives for repeat purchases.

In many companies, online sales are still accounted for simply as "another branch," encouraging a siloed view of results and slowing the adoption of broader omnichannel strategies. The situation is further complicated by centrally managed technology budgets within multinational corporations, which makes it difficult for local teams to push for new measurement tools or data activation solutions.

Activating Customer Data Activating customer data makes sense even when it captures only a part of the audience. Participants agreed that even limited improvements in data usage — focused on specific segments — can validate hypotheses faster and create a business case for scaling. This approach is particularly valued where the return on investment for precise measurement initially seems uncertain.

However, measuring the long B2B customer journey remains a challenge. Digital investments in B2B are difficult to report to management because results take longer to materialize. Some firms (including in B2B sectors) are considering marketing mix modeling (MMM). While marketers believe MMM could help clarify the impact of activities on business results, concerns around data sharing between client and agency remain an obstacle.

The Role of AI and Automation Finally, the conversation turned to AI and automation. There is clearly a growing adoption of AI across different areas of marketing. However, there is heightened concern about the security of both inputs and outputs of language models. Highly regulated industries like finance and healthcare are particularly cautious about compliance risks and regulatory scrutiny, which slows down AI adoption.

Despite these concerns, participants expressed confidence that with gradually relaxed internal processes and improving model transparency, AI will eventually find a solid place even in these industries. Solutions like Google's NotebookLM, which enables working with language models over strictly limited datasets, were presented as promising tools for controlled AI usage.