Introduction 

Trade Promotion Optimisation (TPO) is a transformative capability that leverages advanced analytics and machine learning to enable firms to improve their trade promotion effectiveness. By converting trade promotions from a routine volume driving expenditure into a strategic asset, TPO helps companies identify and proactively manage trade investment to drive growth and profitability.  

Brands Move Towards TPO and Data Considerations 

Consumer goods companies around the world is increasingly recognising the benefits of TPO, with many companies migrating towards its adoption. However, the success of TPO in any one geography largely depends on the robustness of data infrastructure. While larger companies often have the necessary data systems in place, smaller and mid-sized companies may face challenges in data quality and integration. Ensuring a solid foundation of accurate and comprehensive data is crucial for leveraging TPO effectively.

Understanding Trade Promotion Optimisation (TPO) 

TPO utilizes artificial intelligence (AI) and data science to revolutionise how companies plan, execute, and analyse their promotions. By analysing historical data, market trends, and external factors, TPO enables businesses to transform trade promotions into strategic initiatives that drive significant growth and profitability.  

TPO leverages: 

•  Data Science:  Model future values from past observations (baseline and uplift), model explanations (post-event analysis and volume decomposition) and detect anomalies in data. 

• Artificial Intelligence (AI): Increase productivity by automating work. 

Both data science and AI leverage machine learning and neural networks. 

TPO Capabilities and Components 

TPO capabilities include predictive analytics, post-event analysis, scenario planning, consumption-based promotion forecasting and real-time adjustments. These capabilities enable businesses to proactively optimise promotional spending, refine strategies, and respond swiftly to market changes, ensuring that promotions are as effective and efficient as possible. 

Automated Promotional Forecasting 

TPO establishes a consumption baseline of expected sales and predicts the uplift from promotions. By analysing historical sales data and external factors, TPO systems forecast the likely impact of various promotional strategies, aiding in resource allocation. 

• Baseline Establishment: Identifies expected sales levels without promotions. 

• Uplift Predictions: Estimates the incremental sales from promotions. 

• Cannibalization: Identifies product interaction and estimates the impact of consumers switching to a different product in the portfolio to take advantage of a promotional offer. 

Post Event Analysis 

This component involves evaluating past promotions to understand their effectiveness and derive insights for future strategies. This helps businesses refine their strategies and maximise ROI. 

• Volume Decomposition: Decompose consumption volumes for a given promotional mechanic, retailer, sku and period into the different sources of incrementality. 

• Promotional ROI: Leverages the volume decomposition to build a promotional P&L generating insights into revenue, spend and margin impact driven by the trade activity. 

• Retailer Margin: Provides insights into the cash profit and overall margin generated for the retailer supporting meaningful discussions with channel partners. 

Scenario Planning 

TPO allows businesses to simulate various promotional scenarios to determine optimal strategies. By evaluating multiple potential outcomes, companies can select the best course of action under different market conditions and objectives. 

• Simulation of Scenarios: Tests different promotional strategies. 

• Outcome Evaluation: Assesses potential results of each scenario. 

• Strategic Selection: Chooses the best approach based on simulations. 

Comprehensive Reporting and Insights 

TPO systems provide detailed reporting and insights, offering a clear view of promotional performance across different channels and periods. This comprehensive analysis helps businesses understand the full impact of their promotions and identify areas for improvement. 

• Detailed Reports: Offers in-depth analysis of promotional activities. 

• Cross-Channel Insights: Provides performance data across various channels. 

• Improvement Identification: Highlights areas needing strategic adjustments. 

Data Science and AI in TPO 

Data Science and AI are the analytical backbone of TPO, driving accurate predictions and strategic insights. Data Science involves:  

• Data Collection: Gathers data from sales records, market research, etc. 

• Data Cleaning: Ensures data accuracy and consistency. 

• Data Analysis: Identifies patterns and trends in the data. 

AI enhances TPO by using machine learning algorithms to predict future outcomes and continuously improve these predictions as more data becomes available. AI adapts to new data, refining its recommendations and enabling more nuanced and effective decision-making. 

• Machine Learning Algorithms: Learn from historical data to predict outcomes. 

• Predictive Modelling: Forecasts the likely impacts of promotional strategies. 

Adaptive Systems: Continuously refine predictions with new data. 

Essential Data for TPO 

To effectively transition from Trade Promotion Management (TPM) to TPO, organisations need to incorporate specific types of data: 

• Sell-Out Data (Consumption, EPOS or Syndicated): Detailed records of past sales transactions to identify trends and patterns. This data is crucial for understanding consumer behaviour and predicting future sales. 

• Promotional Data: Information on past promotions, including retailer, promoted products, dates, mechanics, in-store support, funding, pricing types, durations, and outcomes. This data helps in analysing the effectiveness of different promotional strategies. 

• External Data: Economic indicators, weather data, and other external factors that can influence sales. Incorporating external data ensures that promotions are aligned with broader market conditions. 

• Master Data Mapping: Mapping of internal product codes vs. external (syndicated/EPOS) product codes allowing the consolidation of different sources into one single solution. 

• Inventory Data: Information on stock levels to manage supply and demand effectively. Inventory data helps in avoiding stockouts or overstock situations during promotions. 

• Pricing Data: Historical and current customer and consumer pricing information to analyse the impact of price changes on sales. Pricing data is essential for understanding the relationship between price changes and sales performance. 

Mapping these data sets to internal data (products/customers) is crucial and often a key challenge. Ensuring accurate and comprehensive data integration is essential for leveraging TPO effectively. 

Conclusion 

Trade Promotion Optimisation (TPO) is essential for maximising trade promotion effectiveness. Leveraging advanced analytics and machine learning, TPO provides predictive insights, optimises promotional spending, and enhances strategic decision-making.  

Integrating TPO with TPM ensures a comprehensive approach to revenue growth management (RGM).  UpClear’s BluePlanner delivers streamlined processes, efficient automation and a single source of truth for the business. This creates actionable insights for proactive gross to net revenue management. For more detailed insights and understanding, contact UpClear 

About UpClear

At UpClear, our mission is to empower Consumer Goods brands to maximize revenue performance and trade investment returns through intelligent, collaborative software—providing a single source of truth, streamlined automation, and actionable insights. 

BluePlanner Revenue Management software supports end-to-end processes, from Annual Operating Planning to Account Planning and Execution.