The consumer goods sector is facing unprecedented challenges. Rapid e-commerce growth, shifting consumer behaviors, and inflationary pressures have created a complex environment. As companies struggle to maintain volume amid rising prices, promotions can serve as a crucial tool to bridge the value gap, especially since value for shoppers is more important than ever.

Traditional promotional strategies are now proving less effective. Approximately two-thirds of promotions fail to generate incremental value, often due to insufficient planning and optimization. To enhance the promotion management process, businesses need to undertake a comprehensive evaluation of their current practices, advance their capabilities over time, and find ways to leverage the potential of artificial intelligence (AI) technology, including generative AI.

The case for a holistic approach to promotion management

While negative return on investment (ROI) on promotions may not always be immediately apparent, there are a variety of symptoms of poor promotional strategies. The company may, for example, experience extreme sales peaks at quarter ends, promote top-selling products year-round, have several promotions active for the same stock keeping unit (SKU) within a single week, or launch poorly timed sell-through promotions.

It’s crucial to address these symptoms, but doing so successfully requires a holistic view of promotional performance that evaluates efficiency from four perspectives:

1. Brand/product providers’ perspective: To gain a granular understanding of the true incremental value of promotions.

2. Retailers’ perspective: To define operations that benefit both the brand and the retailer, particularly in light of retailers’ consumer-centric pricing strategies.

3. Competitors’ perspective: To analyze competitors' promotional strategies and performance and limit brand switching.

4. Consumers’ perspective: To understand consumer behavior and the impact of promotions on loyalty.

How promotion management capabilities progress

Consumer goods companies exhibit a range of levels of sophistication in their promotional strategies, from basic sell-in views aimed solely at boosting volume to advanced, data-driven approaches that leverage comprehensive insights — including consumer behavior, competitive dynamics, and machine learning-based forecasting — into promotional performance. This evolution progresses through four key stages:

1. Basic: At this initial stage, companies have a limited understanding of the incremental impact of promotions, focusing primarily on direct effects without considering indirect ones and other factors. Promotions are often repeated year-on-year as short-term volume drivers, and the approach is tactical rather than strategic.

2. Market standard: Companies at this level develop a stronger understanding of past promotions' incremental impacts, incorporating a vision of ROI from both their perspective and that of retailers. The promo planning process becomes more sophisticated, allowing for informed negotiations that aim to create win/win scenarios.

3. Advanced: Organizations achieve a full 360-degree view of promotional performance, considering consumer perspectives on recruitment and share of stomach, as well as competitor dynamics. They utilize machine learning for forecasting future promotions' expected impacts and empower teams to plan with clarity regarding financial implications.

4. Next-gen AI-driven: The most mature stage features fully automated promotional plan building, driven by AI. This approach allows for the creation of optimal promo plans based on historical performance and predefined inputs, enabling teams to simulate various scenarios with different constraints. This automation shifts the focus of teams from process management to strategic planning, enhancing overall promotional effectiveness.

Challenges companies face climbing the capability maturity scale

As consumer goods companies climb the capability maturity scale, they face four main challenges:

1. Access to data: To enhance promotional strategies, companies require access to multiple data sources, including loyalty program data, POS information, and online behavior analysis. However, product suppliers often face challenges in obtaining this data, especially if they are not engaged in direct-to-consumer (D2C) distribution. They can employ a host of methods to overcome this barrier, including establishing partnerships with retailers, negotiating data-sharing agreements, implementing direct consumer engagement initiatives, utilizing third-party data providers, and conducting in-store promotions.

2. Merging disparate data sources: Integrating financial data with sales data as well as external data such as competitor promotions and shopper panel insights can be complex. Manual data wrangling is frequently necessary to ensure that the data is suitable for analysis, and issues related to data cleanliness and siloed knowledge within organizations often hinder the process.

3. Change management: Successfully engaging business teams and developing tools that meet their specific needs while ensuring scalability across diverse markets and customer segments presents a significant challenge. It is crucial to foster a culture of collaboration and adaptability to facilitate this transition.

4. Finding the right people: Identifying individuals with strong quantitative skills, a solid understanding of the trade environment, and effective communication abilities is essential for driving these initiatives. However, such profiles are often rare and difficult to find, making talent acquisition a critical component of successful promotional strategy implementation.

Five ways AI and generative AI can enhance promotional strategies

With the increasing adoption of AI (primarily machine learning) and generative AI (large language models), there are a host of powerful tools available for organizations to enhance their promotional capabilities and address some of the challenges they face climbing the maturity scale.

Get a deeper understanding of consumer behavior and segmentation

Advanced machine learning (ML) models can analyze extensive datasets, revealing customer patterns that were previously difficult to discern. By integrating data from sources such as loyalty programs, point of sale (POS) systems, and online interactions, and reviewing a large volume of data and price lists, these models can create a more cohesive and exploitable dataset offer a clear, granular understanding of pricing. Generative AI can also help integrate qualitative data such as competitor promotions from flyers or consumer sentiment analyses to better identify offers that would resonate most with customers.

Optimize promotions' direct impact on sales and margin

AI models provide the opportunity to review past promotional performances in more detail and simulate multiple scenarios — both sell-in and sell-through — so companies can predict the promotion mechanism, consumer segment, discount level, and frequency that will generate maximum value given the market condition, consumer behavior, historical performance, seasonality, channel dynamics, and competitive landscape. By leveraging proprietary tools and models in our promo optimization work, we’ve found that promotions will increase both sales and margins significantly. If sales is the priority, we typically observe a 10% to 20% increase in sales and a 5% to 10% improvement in margins. If margin is the focus, we typically see a 5% increase in sales and a 10% to 20% improvement in margins.

Identify personalized levers for brands going direct to consumers

By collaborating with retailers and distributors to analyze consumer behaviors, consumer goods companies can transition from mass marketing to promotion strategies that target customers at an individual level and improve segmentation. Further, for brands going direct to consumers, generative AI can help craft tailored content that resonates with individual consumers, significantly increasing campaign success rates.

Better measure and optimize promotions’ indirect impact

Measuring real return on investments often proves challenging for product suppliers. Beyond direct ROI, it requires measuring indirect effects such as cannibalization (margin taken from non-promoted items), future margin lost from clients and consumers stocking up on promotion, and the positive effect of completing the basket with other items. By analyzing extensive datasets that include sales history, promotional activities, and consumer behavior, ML models can identify patterns that traditional analysis might overlook and reflect the true value of a promotion.

Better manage channels and arbitrate on promotions

AI and generative AI can analyze data to determine the most effective promotions for each channel. This helps in balancing direct and indirect sales efforts, ensure consistent messaging, and resolve potential conflicts between channels to maximize overall performance. The data also can typically be integrated in a wider AI-driven insights platform for commercial teams.

AI opportunities in promotional management

As consumer goods companies continue to enhance the sophistication of their promotional strategies, AI and generative AI emerge as powerful allies in overcoming the challenges they face. While these technologies may not resolve every issue in promotional management, they offer a transformative opportunity for organizations ready to innovate across the entire promotional cycle — from in-depth analysis to targeted outreach and precise ROI measurement.

By seamlessly integrating human expertise with advanced analytics and AI capabilities, consumer goods companies can not only optimize their promotional strategies for immediate sales and profit gains, but also foster lasting brand loyalty and bolster their competitive advantage in an ever-evolving marketplace. Embracing this synergy will be crucial for companies aiming to thrive in the future of promotional management.