Employing Artificial Data in Online Promotion Strategies
In the dynamic world of digital marketing, challenges such as limited sample sizes, privacy issues, and rapidly-changing omnichannel strategies often pose significant hurdles. However, a novel solution is emerging: the use of generative adversarial networks (GANs) with synthetic tabular data.
This innovative approach helps digital marketers and brand strategists tackle issues like limited sample sizes and the S-curve in advertising spend versus sales. By creating realistic, statistically representative synthetic data, GANs provide a powerful tool for augmenting small or imbalanced datasets.
Addressing Limited Sample Sizes
GANs, such as the conditional tabular GAN model CTGAN, generate synthetic tabular data that closely resembles the statistical properties and complex relationships of real datasets. This synthetic augmentation expands effective sample sizes without breaching privacy or relying on scarce real samples, enabling marketers to build better predictive models even with limited original data.
Improving Model Robustness and Validation
With richer synthetic datasets, digital marketers can better test and validate predictive models, minimizing overfitting issues common when training on small samples. This leads to more accurate forecasts of campaign outcomes such as sales lift relative to advertising spend.
Mitigating the S-Curve Effect
The S-curve in advertising represents diminishing returns – after a point, extra ad spend yields smaller sales increases. By augmenting data through GAN-generated synthetic tabular data, marketers gain deeper insights into the nuanced impact of incremental ad spend. Synthesized data reflecting varied market conditions and audience responses allow better modeling of the non-linear relationship between spend and sales, enabling smarter budget allocation to avoid inefficient overspending.
Supporting Data-Driven Strategy Under Privacy and Data Scarcity Constraints
GANs enable the creation of synthetic customer and campaign data without exposing sensitive real data, facilitating experimentation and strategy refinement in privacy-compliant ways. This is crucial as first-party data use rises and walled gardens lose dominance, pushing brands to own more of their data and optimize spend internally.
The VCR, or Variance Concentration Ratio, is a metric used to quantify data. Defined as the ratio between the largest singular value of the dataset and the total sum of all singular values, the VCR answers the question: "What's the data variance percentage concentrated on the direction of the first singular value?" The VCR of the synthetic dataset has to be equal or higher than the VCR of the original data to ensure similarity.
Tabular GANs empower digital marketers and brand strategists to overcome data limitations and complex ad spend dynamics by synthesizing high-quality, diverse tabular data. This synthetic data boosts model performance and strategic insights around advertising ROI, including better understanding and managing the S-curve of diminishing returns in ad spend versus sales.
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The conditional tabular GAN model CTGAN is a type of GAN that generates synthetic tabular data to resemble real datasets, helping digital marketers expand effective sample sizes without breaching privacy or relying on scarce real samples.
By creating statistically representative synthetic data, GANs facilitate the testing and validation of predictive models, reducing overfitting issues and leading to more accurate forecasts of campaign outcomes.
