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How do you measure forecast accuracy for yield management in banking?

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Yield management is a strategy that aims to maximize the revenue and profitability of a bank by adjusting the prices and availability of its products and services based on the demand and supply conditions. To implement yield management effectively, a bank needs to forecast the demand and supply of its products and services accurately, and measure how well its forecasts match the actual outcomes. In this article, we will discuss how you can measure forecast accuracy for yield management in banking, and why it is important for your business.

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JL Suarez, MBA

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1 Why measure forecast accuracy?

Forecast accuracy is the degree of closeness between the forecasted and the actual values of a variable, such as demand, supply, price, or revenue. Measuring forecast accuracy helps you evaluate the performance and reliability of your forecasting methods, identify and correct any sources of error or bias, and improve your decision making and planning processes. Forecast accuracy also affects your yield management outcomes, as inaccurate forecasts can lead to overpricing or underpricing, overbooking or underbooking, and lost revenue or customer dissatisfaction.

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Dr Kumar Kaushish

Holistic Mental Health, Wellness, Lifestyle, Psychotherapist, Coach helping navigate Personal, Professional, Family Challenges and Enjoy Meaningful - Purposeful - Successful Business, Life, Career, Personality, Relations
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Point-in-Time Accuracy compares the forecasted value e.g., loan demand, deposit levels at a specific point in time with the actual value that occurred. Expressed as a Mean Absolute Error or Mean Squared Error, indicating the average difference between the forecast and the actual value. Horizon Accuracy assesses the accuracy of forecasts over a specific time period, like a week, month, quarter and helps identify if forecasts consistently deviate from reality over time. Use Mean Forecast Error (MFE) or Root Mean Squared Error (RMSE) for this analysis. Uplift Analysis compares the profitability achieved using the forecast with the profitability that would've been achieved using historical averages to quantify the actual benefit derived.

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2 How to measure forecast accuracy?

When determining forecast accuracy, there are a few common methods to consider. Mean absolute error (MAE) is the average of the absolute differences between the forecasted and actual values, and a lower MAE indicates higher accuracy. To measure the relative size of forecast errors, compared to the actual values, mean absolute percentage error (MAPE) is used. Mean squared error (MSE) is the average of the squared differences between the forecasted and actual values, with more weight given to larger errors. Lastly, root mean squared error (RMSE) measures the standard deviation of forecast errors and has the same unit as the actual values - a lower RMSE indicates higher accuracy.

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JL Suarez, MBA

?? 50+ Awarded LinkedIn Top Voice Badges: ??Manager of Enterprise Data Services at Holiday Inn Club Vacations??: Inspiring Innovation & Leadership in Business Intelligence??
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When determining forecast accuracy, it's crucial to understand the strengths of each method. MAE is straightforward and easy to interpret, making it great for quick assessments. MAPE, on the other hand, is useful for comparing forecast accuracy across different scales. MSE and RMSE give more weight to larger errors, which can be beneficial when large deviations are particularly costly. For yield management in banking, choosing the right method depends on the specific context and the cost implications of forecast errors. ??

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3 How to choose the best method?

Choosing the best method to measure forecast accuracy is not an easy task, as each method has its own advantages and disadvantages. When selecting a method, you should consider the scale and distribution of your data, the purpose and frequency of your forecasts, as well as the metrics and benchmarks you use. For example, MAPE may be misleading if your actual values are close to zero, while MAE and MAPE may be more useful for comparing different products or services. MSE and RMSE may be more suitable for evaluating and improving forecasting models, or for optimizing yield management strategies. Additionally, MAPE can be linked to percentage change in revenue or profit, while RMSE can be related to the standard deviation of revenue or profit. Therefore, it is important to align your forecast accuracy method with your yield management metrics and benchmarks, or use multiple methods to capture different aspects of your forecast accuracy.

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Dr. Jaimine V

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When comparing forecasting methods or models, calculate these metrics on a validation set that wasn't used to train the models. This gives you an unbiased estimate of future accuracy. Don't rely only on one metric. MAE and RMSE capture different aspects of accuracy, so consider both. MAPE can be unstable if actual values are near zero. For time series data, scale your data before calculating metrics like MSE and RMSE so the errors are comparable across time periods To interpret the metrics, you'll need context on the acceptable range for your particular use case. Define targets for each metric to aim for based on business needs Plot forecasts vs actuals over time visually to complement numeric metrics. This can reveal patterns in errors

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4 How to improve your forecast accuracy?

Improving your forecast accuracy is not only a matter of selecting the best method, but also of utilizing the best practices and techniques in your forecasting process. To achieve this, you should use reliable and relevant data that reflects current and expected demand and supply conditions. Additionally, you should use appropriate and flexible forecasting models that can capture the patterns, trends, seasonality, and variability of your data. Furthermore, you should not rely on a single forecast, but use multiple forecasts from different sources, methods, or perspectives. Involving various stakeholders in your forecasting process and soliciting their feedback can also help to improve accuracy and validity. Lastly, you should continuously evaluate your forecasts throughout the period and compare them to actual outcomes as they become available. Identifying and analyzing sources of forecast errors can lead to corrective or preventive actions to improve your forecasts.

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5 How to use your forecast accuracy for yield management?

Measuring your forecast accuracy is not an end in itself, but a means to an end. You should use the results of your forecast accuracy to inform and enhance yield management decisions and actions, such as setting and updating prices, managing and allocating inventory, and segmenting and targeting customers. Your forecast accuracy results can help you determine optimal prices based on expected demand and supply conditions, manage inventory in order to avoid overbooking or underbooking, and segment customers based on their preferences and behavior. Additionally, you can use your forecast accuracy results to monitor and evaluate the impact of your strategies on yield management performance and customer satisfaction.

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How do you measure forecast accuracy for yield management in banking?

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1 Why measure forecast accuracy?

2 How to measure forecast accuracy?

3 How to choose the best method?

4 How to improve your forecast accuracy?

5 How to use your forecast accuracy for yield management?

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