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Harnessing Incremental Models for Predictive Insights & Retail Media Optimization

Asha Tantuwaya of VML
Asha Tantuwaya

Allocating retail media budgets can be like placing bets at a roulette table. There are lots of options and choosing the right one can be an uncertain gamble. How can you predict an effective outcome? And even if you do win and drive sales growth, is the strategy replicable, and can you improve your chances for next time?

It’s a daunting challenge in a hyper-competitive and increasingly complex retail landscape. Changing consumer behavior and the continued rise of e-commerce add to the problem.

However, the advent of incremental models offers a promising solution to this issue. By leveraging incremental models to inform predictive insights, CPG brands can optimize their retail media budgets to maximize sales impact and achieve sustainable growth.

Understanding Incremental Models 

Incremental models provide a powerful framework for measuring the causal impact of marketing activities on sales. Unlike traditional attribution models, which often struggle to account for external factors and accurately quantify marketing effectiveness, incremental models focus on isolating the true incremental lift generated by specific marketing tactics. By leveraging advanced statistical techniques, manufacturers can gain deeper insights into what works: the real relationship between marketing efforts and sales outcomes.

Predictive Insights for Effective Budget Allocation

By incorporating incremental models into predictive analytics frameworks, CPG manufacturers can enhance their ability to forecast sales impact and allocate retail media budgets more effectively. Predictive models powered by incremental insights enable our clients to anticipate the potential ROI of different marketing strategies and prioritize investments accordingly. Whether it's optimizing ad placements, refining targeting parameters, or adjusting messaging tactics, predictive insights derived from incremental models empower manufacturers to make data-driven decisions that drive tangible sales growth.

Optimizing Retail Media Budgets

One of the key advantages of incremental models is their ability to provide granular insights into the performance of individual marketing tactics. By evaluating the incremental impact of various media channels, formats, and creative assets, businesses can identify the most effective drivers of sales lift and allocate budgets accordingly. When it comes to deciding on investing more heavily in high-performing channels or reallocating resources from underperforming tactics, incremental models enable businesses to optimize their media budgets with precision and confidence.

This is all far from theoretical. My team has developed an incremental model to measure the relationship between sales and two independent variables – paid and earned media – to calculate return on investment (ROI) of creative commerce activations. We first looked at the total scope, expenditure, and impact of a particular campaign, then stripped out the earned media component, to assess what would have happened if we had relied solely on purchasing retail media. By doing this, we have been able to establish a "Creative Multiplier Effect" of the earned media (the amplification of the message), of up to 400%.bThis model was intended to isolate and identify the added impact of creativity in commerce channels, but the same regression analysis can be applied to retail media placement.

The value of incremental models in informing predictive insights to effectively spend retail media budgets cannot be overstated. Businesses can gain deeper insights into the causal impact of marketing activities on sales, enabling them to optimize their media budgets with precision and confidence. As the retail landscape continues to evolve, businesses that embrace incremental modeling will be better positioned to drive sales growth, enhance customer engagement, and achieve sustainable competitive advantage in the marketplace.

Like the roulette wheel, there is still an element of uncertainty. But incremental modeling can help stack the odds more in your favor.

About the Author: Asha Tantuwaya is the Group Director, Strategy, Commerce, VML. As an experienced data science leader, she specializes in advanced analytics, helping her clients make decisions that drive measurable business growth. With over 5 years of experience at VML Commerce, Asha is responsible for fostering the growth of cross-functional teams and spearheading strategic initiatives.

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