How do you test your demand forecasting models?

Testing and validating demand forecasting and pricing models is crucial for accurate decision-making and improved pricing strategies. It helps identify errors, gaps, and biases in data, methods, or logic, leading to better performance and sensitivity. Comparing with alternative scenarios or benchmarks can also identify new opportunities for growth and optimization.

Testing and validating forecasting models is not just a checkmark on a to-do list; it’s about ensuring that your decisions are grounded in reality. Models can look perfect on paper, but only real-world testing reveals their true efficacy and reliability. By rigorously validating your demand and pricing models, you align your strategies more closely with actual market behavior. For instance, Amazon continually tests and adjusts its pricing algorithms to adapt to market trends, ensuring they reflect real customer preferences and maximize sales.

Testing and validating your demand forecasting and pricing models is crucial to ensure they accurately reflect market reality and customer behaviour. This process helps detect errors or data gaps and evaluates model performance against alternative scenarios or benchmarks. By doing so, you can refine your models to make more accurate predictions and informed pricing decisions. Regular testing and validation ensure your models remain effective and reliable.

Hindcasting or Backtesting is a great method to validate your forecasts. If you are able to successfully quantify and extrapolate each individual demand driver coefficient, you can use Hindcasting to run a forecast, where you already know the outcome, then you validate how well your model matched the actual outcome. After completing your MAPE analysis you'll have insight on how accurate your forecasts models are to future outcomes and if anything needs to be tweaked.

To test and validate demand forecasting models, various methods can be employed, depending on the model's type, complexity, and purpose. Backtesting compares forecasted values with historical actuals, using metrics like MAE, MSE, or MAPE to measure accuracy. Cross-validation divides data into subsets for training, validation, and testing, preventing overfitting or underfitting and enabling model selection. Scenario analysis creates and tests different scenarios based on varying assumptions, assessing forecast impact and uncertainty, and preparing for diverse outcomes and contingencies.

As someone deeply involved in AI and data science initiatives, including demand forecasting, I extensively use and suggest out-of-sample testing, the golden standard of forecasting testing! The idea is simple. Imagine you have 100 past data points representing weekly sales for the last two years. Instead of using all data to train your model, you leave out, let's say, the last 20 weeks and use the previous 80 to train the model. Then with the resulting/trained model, you forecast the remaining 20 records and check how accurate your forecast is (i.e., how small the prediction error is). Pls note that the k-fold cross-validation is a special form of out-of-sample testing, which is not a good fit for demand forecasting and should be avoided.

Testing demand forecasts is crucial for transforming theoretical models into practical tools. Each method—whether it's backtesting, cross-validation, or scenario analysis—provides unique insights into how well your forecasts align with real-world conditions and variability. For example, Netflix uses extensive backtesting and scenario analysis to refine its viewer engagement forecasts, which helps in optimizing content investments and scheduling. Such rigorous validation ensures that their models not only predict accurately but also adapt to changing viewer preferences and market trends.

Validating pricing models is akin to refining a compass before embarking on a journey. Analyzing price elasticity, conducting price tests, and benchmarking against competitors provide critical insights that ensure your pricing strategy navigates effectively through market dynamics and customer expectations. For instance, Amazon frequently tests various price points and uses elasticity analysis to optimize its pricing strategy across different regions and product categories. This iterative approach not only helps in maximizing revenue but also in understanding consumer behavior at a granular level, allowing Amazon to stay ahead in a competitive market.

Testing and validating pricing models and assumptions is crucial for any pricing strategy. Price elasticity analysis helps identify the optimal price point, revenue and profit implications, and trade-offs between price and volume. Price testing involves trialing different prices in the market to gather customer feedback and validate hypotheses. Price benchmarking compares your prices with those of competitors, customers, or industry standards to evaluate price positioning, differentiation, and value proposition.

If you’re a CPG product; find a good partner. Go to market with a variety of price depth and frequency strategies and see what drives unit and both top and bottom line growth. A good strategy should be able to optimize all three. Make sure to find a good partner that will execute your tactics! The more the merrier, remember your retailer is going to drive part of the value proposition.

KISS - keep it simple stupid. Excel or any other spreadsheet-based modeling software will do just fine. Remember your return on investment; excel is likely already paid for. ROI is king!

Various tools are available for testing and validating demand forecasting and pricing models, ranging from simple spreadsheets to advanced software. Excel is a popular choice for data analysis, modelling, and visualization. R and Python are powerful programming languages for statistical computing, data manipulation, and machine learning, offering libraries like ggplot2, Flask, and Django for advanced data analysis and web application development. These tools enable you to rigorously test and validate your models, ensuring accurate predictions and informed pricing decisions.

Utilizing tools like Excel, R, and Python for forecasting and pricing validation is similar to equipping oneself with a diverse set of navigational instruments before exploring uncharted territory. Each tool brings unique strengths to the table: Excel for its accessibility, R for its advanced statistical capabilities, and Python for its flexibility and integration. For example, companies like Netflix use Python's powerful libraries, such as Pandas and Scikit-learn, to build predictive models that adjust pricing strategies based on customer behavior patterns. This combination of tools ensures that their models are both accurate and adaptable, enabling them to refine their pricing strategies dynamically.

Improving your forecasting skills is akin to a continuous journey of exploration and adaptation. It's not just about mastering the current methodologies but also staying curious and open to new insights and innovations. For instance, major retailers like Walmart use a blend of traditional forecasting methods and cutting-edge machine learning algorithms. They achieve this by continuously training their teams, fostering a culture of learning, and embracing cross-functional collaboration. This dynamic approach allows them to stay ahead in understanding consumer demand patterns and optimizing their supply chain strategies.

Testing and validating your demand forecasting models requires continuous learning and improvement. To stay current, engage with resources like books, blogs, journals, or podcasts, and participate in online communities, forums, or courses. Experiment by exploring new techniques, algorithms, or datasets, and seek feedback and collaboration from experts, peers, and stakeholders in demand forecasting for valuable insights and suggestions. This approach ensures that your models remain accurate and effective in predicting demand.

Great forecasting requires understanding which internal and external variables drive changes in sales volume. The best forecasting techniques, with bad variables and input data, will lead to poor results. Taking the time to understanding what drives sales, and building those factors into your forecast, will go a long way to improving your forecasting results.

Testing demand forecasting models involves several key steps to ensure accuracy and reliability. Start by dividing historical data into training and testing sets. Train the model on the training data, then evaluate its performance on the testing set to measure predictive accuracy. Utilize metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) to quantify the model's performance. Cross-validation can provide a more robust assessment by testing the model across multiple subsets of data. Additionally, sensitivity analysis can help understand how different variables impact forecasts, ensuring the model's adaptability to changing market conditions. #DemandForecasting #DataAnalytics

Testing and validating pricing models is crucial for optimal pricing. Utilize price elasticity analysis, price testing, and benchmarking to refine pricing strategies. Employ tools like Excel, R, and Python for data analysis and modeling. Continuously learn and adapt your models through feedback, experimentation, and collaboration.