Retail, CPG and Logistics

How to build an effective trade promotion optimization model for CPG

The changing dynamics of customer behaviour in a volatile market have made trade promotion management a herculean task for the CPG (Consumer Packaged Goods)  industry. Factors like digital ubiquity, increased use of social media, dominance of national retailers, etc., have posed new challenges to CPG brands. Hence, CPG brands need to optimise their trade promotion strategies to stay ahead of the curve.

Trade promotion management entails planning, executing and analysing promotional activities to drive sales. Managing trade promotions effectively helps CPG brands improve relationships with their retail partners and drives sales. Ineffective trade management can increase spend, reduce sales, lead to poor customer experience, etc. Hence, it becomes imperative for CPG brands to optimise trade promotions to achieve the desired result.

What is trade promotion optimisation for CPG?

Trade Promotion Optimisation(TPO) is a strategic approach to enhance the efficacy of trade promotions. It is a data-driven approach to optimise trade promotion budgets and activities. It enables CPG brands to develop effective promotional plans that enhance brand awareness and drive sales. The promotional plans are built based on analyses of past performance, consumer behaviour, and market trends. TPO provides a CPG brand with a competitive edge by facilitating data-based decision-making for investment in trade promotions. Optimised trade promotions lead to better ROI.

TPO involves analyses of vast volumes of data, leveraging advanced analytics and AI-driven models to extract valuable insights and trends. It helps CPG businesses to identify patterns, forecast outcomes and make data-based decisions. TPO enables CPG brands to develop trade promotion strategies that align with market demand, retailer behaviour, and financial goals.

How TPO works

Step 1: Data collection and integration

TPO relies on historical data and real-time market information such as sales data, customer insights, retailer data, promotional activity history, etc. 

Data is gathered from various sources such as point-of-sale (POS) systems, supply chain logs, marketing campaigns, external market research, social media behaviour, etc.

Step 2: Demand forecasting

TPO uses historical trends and predictive models to forecast demand under different promotional scenarios. This ensures that promotions align with market demand and prevent overstocking or understocking.

Step 3: Modelling and scenario analysis

Predictive modelling tools simulate different promotion strategies. The performance of a strategy is evaluated for various scenarios such as varying discount levels or timing of discounts, shelf-space investments, geographical targeting, or any other such condition. This helps estimate their impact on sales, profits, and other KPIs.

Step 4: Optimisation algorithms

Advanced ML (Machine Learning) or statistical optimisation techniques evaluate multiple variables like promotional costs, product demand, competitor actions, and more to determine the most efficient combination of promotional strategies.

The goal is, trade spend optimisation and expected sales enhancements.

Step 5: Execution 

Once optimal strategies are identified, CPG companies implement them with their retail partners. This involves planning schedules, budgeting, and executing suitable promotional activities at the right time and place.

Step 6: Tracking and adjustment

After execution, TPO continuously monitors sales performance, competitor activity, and market shifts to adjust promotions in real time. This helps CPG companies to remain agile and make data-driven adjustments to their promotional strategies in real time.

Several TPO tools are available in the market that help CPG brands manage trade promotions.

Key Components of TPO Tools

Machine Learning (ML) Models: ML algorithms analyse large datasets to predict patterns and optimise strategies.

Data Analytics: Market and promotional performance visualisations allow decision-makers to identify trends.

AI-powered forecasting: AI algorithms predict future consumer behaviour under various market and promotional conditions.

Integration with sales and supply chain: Linking TPO tools with inventory and logistics systems ensures that promotions can match supply capabilities.

Why do CPGs need TPO?

TPO offers several benefits to CPG such as:

• Boosts ROI: TPO ensures that the amount spent on promotions actively increases sales and profit.
• Reduces waste: Through better forecasting, CPG companies avoid overspending on ineffective promotions or misallocating resources.
• Enhances forecast accuracy: Predictive models ensure that demand matches supply more closely, reducing stockouts or overstocks.
• Helps build stronger retailer relationships: Data-driven collaboration and well-executed promotional strategies strengthen relationships with retailers.
• Provides competitive advantage: TPO allows companies to respond faster to market changes or competitor strategies by using real-time data insights.

In conclusion

Trade promotion optimisation facilitates accurate forecasts thereby paving the way for better planning. It provides visibility into the promotional strategies in both the planning and execution stages. TPO is a more agile and efficient approach for managing trade promotions as it enhances revenue and profitability while also helping CPG brands build deeper relationships with retailers and consumers.

How can Infosys BPM help?

Infosys BPM offers Trade Promotion Management services that provide innovative solutions to CPG brands. We help CPG companies transform their processes with agility and speed with the help of advanced analytics and automation. We help our clients develop effective trade promotion strategies that reduce waste, boost sales, and enhance ROI.