AI-based Solution for Promotional Demand
Turn Your Promotional Strategy into a Profit-driving Opportunity with AI-powered Demand Forecasting
AI-based Promotional Demand Solution
Maximize the Impact of Every Promotion
Promotions shape consumer behavior in powerful ways — they can increase or decrease demand, shift buying timelines, redirect attention to specific products, and even influence long-term shopping habits. When executed effectively, promotional events drive profitability and strengthen brand loyalty. But without accurate forecasting, they can lead to costly stockouts, excess inventory, and missed revenue opportunities.
Churchill’s Promotional Demand solution eliminates the guesswork. Using AI and Machine Learning, we deliver precise demand forecasts at the item, store, and regional level — with insights available daily, weekly, or at a frequency that fits your business needs. This empowers retailers to fine-tune promotions, align inventory with demand, and maximize every opportunity for growth.
Insightful Promotional Analysis
Granular Forecasting at Any Level
Strategic What-If Scenario Planning
Seamless Integration & Scalability
Successful promotions require more than guesswork. Churchill’s Promotional Demand Forecaster™ (PDF) leverages AI to transform historical sales, past promotions, and external market trends into a comprehensive demand forecast. Our advanced software identifies hidden patterns, nonlinear relationships, and key demand drivers, allowing retailers to anticipate how consumers will respond to pricing and promotional strategies. Before generating forecasts, our engineers validate and audit the data, eliminating inconsistencies to ensure unmatched accuracy and reliability — helping retailers maximize every promotion with confidence.
Key Benefits
Tailored Promotions Through Regional Demand Forecasting
By forecasting promotional demand at the regional and store levels, retailers can implement targeted promotions that align with the specific preferences and purchasing behaviors of local customers. This approach moves beyond a uniform, one-size-fits-all strategy, enabling more effective and efficient marketing efforts.
Pricing Decisions That Protect Your Margins
Lowering prices can drive sales, but without a data-driven approach, it can also erode profitability. Our AI-powered demand forecasting determines the potential increase in gross margin when adjusting prices, ensuring that discounts are not only effective in boosting sales but also strategically designed to protect revenue.
Reduced Workload for Promotion Planners
Promo planning requires analyzing large volumes of data to determine the best promotional strategies. AI-driven forecasting relieves promo planners from time-consuming manual work by automating demand analysis and promotional impact predictions.
Retail-Specific Features
Churchill’s AI-based Promotional Demand solution offers tailored insights for key stakeholders, helping them make informed decisions and optimize promotional strategies. Whether you’re a Pricing Manager, Revenue Director, or part of the Marketing Team, our solution provides actionable forecasts and data-driven recommendations to maximize promotional effectiveness and drive business growth.
Pricing Manager
- ✔Predict how consumers will react to different price points
- ✔Set discounts that maximize sales without eroding profit margins
- ✔Fine-tune promotional pricing across locations
- ✔Improve profitability without over-discounting
- ✔Test pricing strategies before execution
Director of Revenue
- ✔Understand how much revenue a promotion will generate before launch
- ✔Improve revenue forecasting accuracy
- ✔Ensure promotions drive profitable growth
- ✔Balance promotional investment with overall business goals
- ✔Reduce revenue loss from poorly planned promotions
Marketing & Advertising Team
- ✔Identify the most impactful promotions and channels
- ✔Adjust messaging, product focus, and timing to match local shopping behaviors
- ✔Target the right audience with the right discounts
- ✔Test different promotional strategies through What-If Analysis