How to build an effective trade promotion optimization model for CPG

In the rapidly evolving world of consumer-packaged goods (CPG), staying ahead of the competition is crucial. One powerful way to do this is by building an effective trade promotion optimization model. But what exactly is a trade promotion optimization model, and why does it matter? 

A trade promotion optimization model involves using advanced analytics and predictive modeling to maximize the performance of your promotional activities. It’s like having a crystal ball that helps you see which promotions will drive the most revenue and improve your forecasting accuracy. By leveraging data-driven insights, you can make smarter decisions and transform your daily commercial operations. 

A well-built optimization model is a CPG company’s secret weapon. It helps you spend your budget more wisely, boost your return on investment (ROI), and gain a competitive edge. In this blog post, we’ll explore why building a promotion optimization model is essential, discuss the key components involved, and provide a step-by-step guide to help you get started. 

Why CPG companies need a trade promotion optimization model 

Have you ever wondered why some trade promotions seem to hit the mark while others fall flat? It’s a common challenge in the CPG industry. Inefficient spending and limited visibility into performance can make it tough to know what’s working and what’s not. This is where a trade promotion optimization model comes in. 

Think about it like this: Imagine you’re planning a big family dinner. You want to make sure you buy the right amount of food, so you don’t end up with too much or too little. You look at past dinners, consider how many people are coming, and maybe even check what dishes were popular before. This helps you make better decisions and ensures everyone leaves happy. 

Pre- and post-promotion evaluation

Similarly, an optimization model helps CPG companies make better decisions about their trade promotions. It uses data and advanced analytics to forecast outcomes before the promotion even starts. This means you can pre-evaluate the potential success of a promotion, adjust your strategy if needed, and ensure you’re setting yourself up for the best results. 

For example, let’s say a company is planning a promotion. Without an optimization model, they might not know if the promotion will drive sales or just eat into their profits. But with a model, they can forecast the impact of the promotion, make necessary adjustments before it launches, and ensure they’re getting the best results. 

After the promotion has run, the model also helps in post-evaluating the actual outcomes. This allows the company to compare the predicted results with the actual performance, learn from the data, and refine future promotions. 

Even when launching a new product, a similar approach can be taken. By linking the new product to a similar, existing product, the model can predict the impact based on the behavior of the like-for-like product. This way, the company can still forecast and optimize the promotion effectively. 

In short, a trade promotion optimization model is essential for making data-driven decisions, optimizing promotional strategies, and ultimately achieving better outcomes.

Key components of a trade promotion optimization model 

Building a trade promotion optimization model involves several crucial components. Let’s break them down: 

Data collection 

First up is data collection. Think of data as the foundation of your model. You need to gather essential data sources like historical sales, promotion outcomes, and shopper behavior. This data provides the raw material that your model will analyze to generate insights. For example, knowing how a past promotion performed can help predict the success of future ones. 

Advanced analytics 

Next, we have advanced analytics. This is where the magic happens. Using machine learning and AI, you can analyze complex datasets to uncover deep insights. These technologies can identify patterns and trends that might not be obvious at first glance. For instance, AI can reveal which types of promotions work best for different customer segments. 

Predictive modeling 

Then there’s predictive modeling. This component supports accurate forecasting of promotion outcomes. By using predictive models, you can estimate how a promotion will perform before you even launch it. This helps in making informed decisions and minimizing risks. Imagine being able to predict the sales lift from a discount campaign with high accuracy. 

Scenario planning 

Another key component is scenario planning. This allows you to test different promotional strategies and identify the optimal solutions. By running various scenarios, you can see how changes in one area might impact overall performance. For example, you can test the effect of increasing the discount percentage, changing the promotion duration, or using a different promotional mechanism. 

Together, these components form the backbone of a robust trade promotion optimization model. They enable CPG companies to make data-driven decisions, optimize their promotional strategies, and achieve better results. 

Steps to build a trade promotion optimization model 

Building a trade promotion optimization model might seem daunting but breaking it down into manageable steps can make the process much smoother. Here’s how you can get started: 

Step 1: Define objectives and KPIs 

First, you need to be clear about what success looks like. Start by defining your objectives and key performance indicators (KPIs). Are you aiming to increase sales volume, boost profitability, or rather gain market share? For example, if your goal is to increase sales volume, you might measure the promotion’s impact in terms of units sold. Having clear objectives helps you stay focused and measure the effectiveness of your promotions accurately. 

Step 2: Collect and clean data 

Next, gather all the necessary data. This includes historical sales data, promotion outcomes, and consumer insights. Reliable and complete data is crucial for accurate model outputs. Think of it like cooking a meal—the better the ingredients, the better the dish. Clean and harmonize your data to remove any inaccuracies or inconsistencies. This step ensures that your model has a solid foundation to work from. 

Step 3: Choose the right tools and software 

Selecting the right tools and software is also essential for building an effective model. TPx software like Visualfabriq offers scalable, AI-driven optimization and accurate forecasting. Such a tool can handle large datasets and provide the advanced analytics needed for your model. Make sure the software you choose aligns with your specific needs and market conditions. 

Step 4: Leverage predictive models 

Now, it’s time to put your data to work. Utilize AI-powered prediction models in your chosen software, or test, refine, and validate your own models. These models help forecast promotion outcomes with high accuracy. For instance, you can predict the sales lift from a discount campaign and adjust your strategy accordingly. 

Step 5: Implement and monitor 

Finally, implement your model and continuously monitor its performance. Ongoing monitoring and refinement are key to continuous improvement. This step ensures that your model adapts to changing market conditions and remains effective over time. For example, if a promotion isn’t performing as expected, you can take away learnings and make the necessary adjustments to improve future results. 

Day-to-day impact on commercial teams 

Implementing a trade promotion optimization model can significantly change daily workflows for commercial teams. By automating forecasts and insights, the model allows teams to allocate their time and resources more effectively. This means less time spent on manual data analysis and more time focusing on strategic decision-making. 

How to evaluate the effectiveness of your optimization model 

Once you’ve built and implemented your trade promotion optimization model, it’s crucial to evaluate its effectiveness. This ensures that your model is delivering the desired results and helps identify areas for improvement. 

Start by tracking key performance indicators (KPIs). These are metrics that help you measure the success of your promotions. Common KPIs include forecast accuracy, sales lift, and return on investment (ROI). For example, you might compare the predicted sales lift from your model to the actual sales lift achieved. If the numbers are close, it indicates that your model is accurate. 

Another important aspect is continuous improvement. AI models are self-learning, meaning they get better over time as they process more data. Regularly review your model’s performance and adjust as needed. This helps ensure that your model stays aligned with changing market trends and consumer behaviors. For instance, if you notice a drop in forecast accuracy, it might be time to update your data or tweak your model. 

It’s also helpful to track performance against human estimation. When starting out, compare your model’s predictions with those made by your team. This can provide valuable insights into the model’s accuracy and highlight any areas where it might need improvement. Over time, as the model learns and improves, you should see its performance consistently surpass human estimations. 

Lastly, remember that market shocks can impact on your model’s performance. Events like economic downturns or pandemics can disrupt market conditions and affect your model’s accuracy. Be prepared to make significant adjustments during such times to ensure your model remains effective. 

By regularly evaluating your model using these methods, you can ensure it continues to deliver accurate and valuable insights, helping you optimize your trade promotions and achieve better results. 

Real-world examples with Visualfabriq 

Dutch multinational brewing company Heineken has successfully leveraged Visualfabriq’s AI prediction models in the Netherlands to enhance their trade promotion strategies. One of the standout features of Visualfabriq’s software is its flexibility, allowing customers to build, train, and adjust their own prediction models. This adaptability has enabled Heineken to tailor their models to specific needs and market conditions, significantly improving the accuracy of their trade promotion forecasts. 

By filtering data per retailer and SKU and creating submodels that consider factors like promotion price elasticity and seasonality, Heineken has seen better decision-making and increased profitability. As Victor de Graaff, a freelance Data Scientist involved with the Heineken promotion optimization effort, notes, “(AI) predictions are better than manual predictions.” 

For more details on Heineken’s experience with Visualfabriq, you can read the case study and watch a video. 

Takeaways 

In today’s competitive CPG landscape, building a trade promotion optimization model is not just a nice-to-have—it’s a necessity. By leveraging advanced analytics and predictive modeling, companies can improve forecasting accuracy and drive profitable revenue growth. 

A well-built optimization model supports you to make data-driven decisions. This will help maximize your promotional performance and allow you to gain a competitive edge. From defining clear objectives and collecting reliable data to choosing the right tools and continuously monitoring performance, each step is crucial for success. Real-world examples, like Heineken’s use of Visualfabriq’s flexible AI prediction models, demonstrate the tangible benefits of this approach. 

Implementing an optimization model can lead to long-term gains in trade promotion efficiency and profitability. It empowers commercial teams to allocate their time and resources more effectively, ultimately driving better business outcomes. 

Ready to see the impact of a trade promotion optimization model for yourself? Book a demo with Visualfabriq today and discover how our AI-driven solutions can transform your promotional strategies.