The key role of Trade Promotions and reasons for constant change
One of the primary reasons why Trade Promotions have become more important is the increasing power of consumers. Today’s consumers are more informed and empowered than ever before. Through Internet and mobile technology, they have access to a wealth of information about products, prices, and promotions, and regularly use this information to make informed purchasing decisions. As a result, consumer goods companies need to be more creative and strategic in their Trade Promotions in order to capture the attention and loyalty of consumers by constantly changing their game of approach.
Additionally, inflation pressures have increased supplier costs which have cut into the profit of individual consumer goods products. Pricing and promotion planners need to be provided with smarter tools so that unnecessary promotion expenses can be minimized to preserve profit margins.
When consumers select a certain brand of soap on sale at a retail store, somewhere out there, a certain promotional planner has won the game because the proper mix of promotional tactics influenced buyer behavior. Some of these tactics include:
- The product is found in the right store
- The correct location inside the store
- Including a notable discount when compared to competing products
- For an ideal number of weeks
- Starting on the right date
- Timing of promotion during the best seasonal cycle for maximum profit
But what seems to be a walk in the park for the consumers can be a nightmare for the CPG companies. The right promotional campaign for Consumer Packaged Goods (CPG) companies can get quite overwhelming for a promotional planner since eight out of ten promotions, according to an industry study, underperform. As a result, Predictive Modeling techniques have emerged as a powerful tool to optimize Trade Promotions, resulting in increased revenue and profitability.
Role of Predictive Models in helping CPG companies
Predictive Models are algorithms that use historical data to make predictions about future events. By analyzing historical sales and related data, Predictive Models can identify patterns and relationships between promotions, pricing, and sales performance. CPG companies have been utilizing Trade Promotions as a key strategy to increase their sales and market share. This information enables them to create a Trade Promotion plan that resonates with the customer and drives sales by:
Formulate the right Predictive Model to optimize Trade Promotion
Formulating the right Predictive Model to optimize Trade Promotion requires a structured approach that includes the following steps:
- Define the drivers: Clearly define the business objectives that the Predictive Model is meant to solve. This could be improving Trade Promotion effectiveness, Return on Investment, reducing out of stock conditions, or optimizing pricing, among others. For example, Frito-Lay set a clear objective to increase market share for their Doritos brand. They achieved this by offering a promotion where consumers could receive a free bag of Doritos with the purchase of a pizza.
- Gather quality historical data: Collect and clean relevant data that will be used to train the model. This could include historical sales data, pricing data, promotional data, inventory data, and external factors such as economic indicators and weather data.
- Select model features: Identify the variables (features) that are most relevant to the problem at hand. This could include variables such as the timing, duration, and type of promotions, the price of the product, and external factors such as seasonality and economic indicators. Coca-Cola used targeted promotions to promote their line of Zero Sugar beverages to health-conscious consumers. This led to a 20% increase in sales volume for the Zero Sugar line.
- Choose appropriate ML modeling technique: Select a Machine Learning modeling technique that is appropriate for the problem and the available data. This could include linear regression, logistic regression, decision trees, or neural networks, among others. Procter & Gamble used Predictive Modeling to optimize their Trade Promotions for their Tide brand. They found that offering a larger discount on larger package sizes was most effective in driving sales.
- Train the ML model: Use the selected modeling technique to train the Predictive Model on the available data. This involves selecting a training algorithm, setting model parameters, and iterating until the model achieves the desired level of accuracy.
- Validate the model: Test the Predictive Model on a validation dataset to ensure that it is accurate and reliable. This involves evaluating the model’s performance metrics, such as accuracy, precision, recall, outlier detection and F1-score.
- Deploy the model: Once the model has been validated, deploy it into production and integrate it into the decision-making process. This could involve developing a user interface that allows business stakeholders to interact with the model and make informed decisions based on its predictions.
- Monitor and update the model: Continuously monitor the Model’s performance and update it as new data becomes available or as business requirements change. For example, In the case of Nestle Purina’s Beneful brand of dog food, they monitored the success of their Trade Promotions and adjusted marketing strategies accordingly.
Artificial Intelligence (AI) & Machine Learning (ML) simplifying the process
AI and Machine Learning have emerged as powerful tools to create Predictive Models that are helping CPG companies make more informed decisions. CPG companies produce a huge amount of data, including information on sales, customers, the supply chain, and more. AI and Machine Learning algorithms can quickly and effectively process this data; finding patterns and trends that would be challenging for humans to see.
AI and Machine Learning can create Predictive Models by taking into account the following:
Conclusion
Trade Promotions are a key strategy for CPG companies to increase sales volume, value and profits. Artificial Intelligence via Machine learning techniques can help companies identify the best Trade Promotion Strategy for a given product and target consumer audience. Collaborating more closely with retailers and using powerful data analytics can help organizations develop more effective Trade Promotions that drive more sales and profit while containing promotion related expenditures.
