Maximize Advertising ROI Using Measured AI-powered Incrementality Models

Maximize Advertising ROI Using Measured AI

Mahesh Jeswani, SVP, Product

Published 06/01/2023

​​In today's hypercompetitive market, consumer brands are constantly seeking innovative ways to optimize advertising spend and maximize return on investment. With the rapid growth of digital platforms, online and offline interactions, marketers are faced with an overwhelming amount of data to process and isolate the true incremental impact of advertising. This is where AI-powered incrementality models come in.

AI-powered Incrementality models use advanced data science and machine learning techniques that can efficiently process large datasets, identify patterns, and infer the causal impact of advertising efforts. Let's explore how Measured is leveraging AI to deploy best-in-class incrementality models that empower hundreds of consumer brands to conduct sophisticated incrementality analysis and drive valuable business outcomes at scale.

Experimental Design and Execution

Effective experimental design and execution are vital in understanding the true incremental impact of marketing initiatives. By conducting controlled experiments, marketers can isolate the effects of specific marketing activities, enabling them to determine causality and make informed decisions.

Proper experimental design allows marketers to accurately quantify the incremental lift generated by their marketing efforts on an ongoing basis. But it's extremely hard to keep running experiments due to aggressive short-term goals, tight financial and resource constraints, deciding treatment audiences amidst changing consumer behavior, and market dynamics like seasonality.

Measured leverages advanced machine learning algorithms to transform the way marketers approach experiments. It effectively manages the whole process, from the initial design to execution and beyond. The key is in how Measured models make predictions and recommendations. It identifies the ideal moments in the consumer journey to decide which experiments to run at what point in time to drive statistically significant outcomes. This not only maximizes efficiency but also optimizes results.

Measured models also optimize the distribution of test subjects, cell-sizes, as well as the duration of each experiment to arrive at a minimum detectable lift. Before initiating a test, algorithms also foresee and mitigate potential problems. They can detect anomalies and identify points at which further investment yields diminishing returns.

In essence, Measured offers a proactive, data-driven approach to marketing experiments. It's designed to maximize business impact while minimizing unnecessary risks and waste.

Causal Inference and Analysis

Causal inference and analysis play a critical role in accurately measuring incrementality and understanding the causal relationship between marketing efforts and consumer behavior. However, Marketers struggle to establish causal relationships and often fall back on correlations due to the infeasibility of employing rigorous statistical methods. As a result, the true incremental impact of individual channels or tactics may be masked, leading to suboptimal decision-making.

Measured uses AI-powered incrementality models, trained on the outcomes of over 25,000 experiments from hundreds of consumer brands. These models are iteratively refined, optimizing hyperparameters and automatically addressing issues detected by statistical diagnostics. Measured guarantees the robustness and accuracy of incrementality estimations to enable brands accurately perform budget allocation decisions across marketing channels and tactics.

Granular Reporting and Attribution

Granular reporting and attribution play a crucial role in accurately measuring incrementality. By analyzing data at a granular level, marketers can comprehend the incremental impact of individual channels, tactics, campaigns, or sub-campaigns, allowing for more informed decision-making. However, obtaining granular data for deeper analysis can be a daunting task, as data scarcity, fragmentation, privacy, and inconsistencies are common.

Measured leverages advanced data management and machine learning techniques to exclude outliers in the input data before evaluating the unique contribution of each channel, tactic, campaign, or sub-campaign. Measured further provides actionable insights to optimize resource allocation and budgeting decisions. By automating the cleansing and normalization of datasets, resolving data inconsistencies, and aggregating data to a meaningful level, Measured algorithms ensure data accuracy, privacy, and integrity both when data-is-at-rest and data-is-in-transit.

Optimization and Real-Time Decision-Making

Marketers often need to strike a balance between short-term goals and long-term brand building. Allocating the media budget optimally in real-time, based on the true incremental impact of each channel, tactic, and campaign requires robust measurement methodologies, data integrations, and sophisticated attribution models in place.

Measured AI-powered incrementality models use simulation planning and optimization to identify which media channels or tactics drive incremental conversions or sales, allowing marketers to optimize their budget allocations in real-time, based on the incremental impact of each channel. Measured enable Marketers to simulate and optimize various scenarios based on objectives such as conversions, revenue, profit, RoAS, or CPA. These models empower Marketers to proactively adapt to the changing market conditions, explore new opportunities, and drive growth.

Market Intelligence and Competitive Advantage

In a highly competitive ecosystem, marketers need to understand how their advertising efforts stack up against competitors and identify areas for improvement. By leveraging incrementality for benchmarking, marketers can gain a competitive advantage by making data-driven decisions that set them apart from their competitors. However, obtaining accurate and reliable benchmark data can be an uphill task, as access to comprehensive and representative industry data is not readily available.

Measured leverages the largest database of incrementality experiments in the industry to benchmark consumer brands against their competitors based on incrementality insights. By analyzing large volumes of data and results of thousands of tests executed across hundreds of brands, Measured can derive accurate insights by demonstrating the incremental impact of different brands within the same industry, enabling Marketers to refine their strategies, optimize their campaigns, and gain a competitive edge.

Conclusion

The advent of AI is revolutionizing the complex world of media attribution and optimization, empowering consumer brands to accurately track and analyze the impact of their marketing efforts across multiple channels, geographies, and platforms. Measured offers best-in-class AI-powered incrementality models that use advanced data science and machine learning techniques to help consumer brands accurately optimize their marketing spend and drive incremental revenue. By leveraging vast amounts of client data, Measured models can provide brands with tailored insights to improve their marketing campaigns and, ultimately, bottom-line results. As competition continues to intensify, consumer brands that embrace incrementality and data-driven decisions will be better positioned to succeed in the years ahead.

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