IAB: 63% of Marketers Struggle with Ad Strategy in 2026

A recent IAB report revealed that 63% of marketers struggle with accurately attributing campaign performance across diverse digital channels, a statistic that shows a widespread disconnect between advertising spend and measurable impact. This challenge, often rooted in inadequate marketing intelligence, directly impedes the development of effective ad strategy. How can teams move beyond guesswork and toward data-driven precision in their advertising efforts?

The Attribution Conundrum: 63% Struggle with Cross-Channel Performance

The IAB’s finding that 63% of marketers struggle with accurate cross-channel attribution is more than just a data point. It’s a flashing red light for the entire industry. This isn’t just about knowing which ad led to a sale. It’s about understanding the entire customer journey, from initial impression to conversion, across every touchpoint. Think about it: a customer might see an ad on a connected TV (CTV) app, then search for the product on their phone, click a paid search ad, and finally convert after seeing a retargeting ad on a social media platform. Without sophisticated attribution models, most of that journey remains opaque. We end up crediting the last click, which often paints an incomplete, if not misleading, picture.

The problem isn’t a lack of data. It’s often a lack of integration and the right tools to make sense of it all. Many organizations operate with siloed data sets: one for social, another for search, a third for programmatic display. This fragmentation makes a well-rounded view impossible. My professional experience suggests that teams often prioritize individual channel performance over collective campaign effectiveness. This leads to internal competition and suboptimal budget allocation. To truly address this, organizations need to invest in unified measurement solutions that can ingest data from all channels and apply advanced statistical models to allocate credit appropriately. Without this, improving ad strategy becomes a series of educated guesses rather than a precise science.

AI’s Impact: 22% Higher ROAS with Predictive Analytics

A recent report from eMarketer highlighted a significant trend: campaigns using AI-powered predictive analytics achieved an average 22% higher return on ad spend (ROAS) in 2025. This isn’t theoretical. It’s a tangible, measurable improvement. Traditional analytics often focus on historical data to understand past performance. Predictive analytics, however, uses machine learning algorithms to forecast future outcomes based on current trends and a vast array of variables, including seasonality, economic indicators, competitive activity, and even real-time news events. Imagine being able to predict which audience segments are most likely to convert in the next 48 hours, or which creative elements will resonate best in a specific geographic market. That capability fundamentally changes how media buyers approach their work.

The real power here lies in the shift from reactive to proactive decision-making. Instead of adjusting bids and targeting after a campaign has underperformed, AI allows for dynamic optimization before potential issues arise. This means less wasted spend and more efficient allocation of resources. For teams looking to gain a competitive edge, integrating AI into their marketing intelligence stack is no longer an option. It’s a strategic imperative. Platforms like Google Ads Performance Max, for example, are increasingly incorporating advanced AI to automate bidding and audience targeting across Google’s inventory, demonstrating this trend in action.

For brands and agencies working through the complex field of digital advertising, especially in emerging channels like Connected TV (CTV) and Over-The-Top (OTT) streaming, precision is paramount. This is where specialized expertise becomes invaluable. A mobile and digital marketing agency like Moburst, for example, offers dedicated OTT Advertising services. Their approach to OTT leverages deep analytics and strategic placement to ensure ad spend reaches the most receptive audiences on streaming platforms. For a team, this means having a partner who understands the nuances of TV viewership, audience segmentation within streaming environments, and how to optimize campaigns for both reach and measurable conversions. It’s about moving beyond spray-and-pray tactics to a data-informed strategy that maximizes impact on a channel still evolving rapidly.

Dedicated Market Research: 15% Higher Customer Retention

A study published by HubSpot found that companies investing in dedicated market research teams for real-time consumer insights report a 15% higher customer retention rate. This often overlooked aspect of marketing intelligence provides the qualitative and quantitative depth that pure analytics sometimes miss. While analytics tells you what is happening, market research helps you understand why. Why are customers churning? What new features do they desire? What are their pain points that your product isn’t addressing? These are questions that direct surveys, focus groups, ethnographic studies, and social listening can answer.

Too many companies treat market research as a one-off project rather than an ongoing function. The reality is that consumer preferences, competitive field, and technological capabilities are in constant flux. An effective ad strategy today requires a continuous feedback loop from the market. This isn’t just about product development. It directly informs messaging, creative direction, and channel selection. If your target audience is increasingly concerned about data privacy, your ads need to reflect that awareness and your product needs to deliver on those expectations. Without consistent, dedicated research, campaigns risk becoming tone-deaf or irrelevant, leading to wasted ad spend and, critically, lost customers. The 15% retention increase isn’t a coincidence. It’s a direct result of understanding and responding to the customer’s evolving needs.

Unified Customer Profiles: Up to 30% Reduction in Ad Waste

Consolidating disparate data sources into a unified customer profile can lead to a reduction in ad waste of up to 30% by eliminating redundant targeting. This is perhaps one of the most actionable insights for any organization seeking to improve its ad strategy. Think about it: how many times does the same customer see the same ad across different platforms from the same brand within a short period? This isn’t just annoying for the consumer. It’s inefficient and costly for the advertiser. A unified customer profile, often built within a Customer Data Platform (CDP), brings together data from CRM systems, website interactions, app usage, email engagement, purchase history, and third-party data sources. This creates a single, complete view of each individual customer.

With a unified profile, marketers can orchestrate more intelligent, personalized campaigns. They can sequence messages across channels, ensuring that a customer who has already converted on one platform isn’t shown a “new customer” offer on another. They can suppress ads for customers who have recently purchased a product or upsell them based on their previous buying behavior. This level of precision is impossible when data remains fragmented. The 30% reduction in ad waste isn’t an exaggeration. It comes from avoiding impressions on uninterested or already converted individuals, and instead focusing spend on high-potential prospects with tailored messaging. It also allows for more accurate measurement of incremental lift, providing clearer insights into true campaign effectiveness.

The Conventional Wisdom I Disagree With: “More Data Always Means Better Strategy”

There’s a pervasive belief in marketing circles that “more data always means better strategy.” I fundamentally disagree with this. While data is essential, simply accumulating vast quantities of it without a clear purpose or the tools to process it can be counterproductive, leading to what I call “data paralysis.” I’ve seen teams drown in dashboards, unable to extract meaningful insights because they lack the proper analytical frameworks or the human expertise to interpret complex data sets. Having petabytes of raw behavioral data doesn’t automatically translate into a winning ad strategy if you don’t have the right questions to ask, the right models to apply, and the right people to make sense of the answers.

The focus shouldn’t be solely on data volume, but on data quality, relevance, and actionability. Is the data clean? Is it current? Does it directly inform a specific marketing objective? Often, a smaller, well-curated dataset analyzed by a skilled professional can yield far more impactful insights than a massive, messy one. The challenge lies not just in collecting everything, but in intelligently filtering, structuring, and visualizing that information to support strategic decision-making. We need to move beyond the fetishization of big data and embrace smart data, data that is purposefully collected and expertly analyzed to drive specific business outcomes.

To truly excel in today’s competitive field, marketers must move beyond collecting data to actively transforming it into actionable marketing intelligence that fuels precise ad strategy. This requires investment in integrated platforms, AI-driven analytics, continuous market research, and a commitment to building unified customer profiles, all while maintaining a critical eye on the quality and relevance of the data itself.