Global health issues like pandemic especially for healthcare accounts

This affects future years forecasting as WFM won’t be able to rely on previous year over year growth rate to do forecast because of the changes on call volume attributed to the pandemic and succeeding years volume forecast will be dependent on when these types of issues are resolved. How do we prepare forecasting in this kind of situation?

Preparing forecasting in a situation where there are significant disruptions to historical data, such as the changes in call volume attributed to the pandemic, can be challenging. However, there are several strategies that can be employed to improve forecast accuracy despite the uncertainties:

  1. Data Analysis and Segmentation: Start by thoroughly analyzing the available data to identify patterns and trends during the pandemic period. This might involve segmenting the data into pre-pandemic, pandemic, and post-pandemic periods. Understanding how different factors affected call volume during these phases can help create more accurate forecasts.
  2. Adaptation to New Normal: Recognize that the pandemic may have permanently altered customer behaviors and preferences. Consider whether these changes are likely to persist in the future and adjust your forecasting models accordingly.
  3. Scenario Planning: Develop multiple scenarios for the future. Consider optimistic, moderate, and pessimistic possibilities. Each scenario should take into account different potential resolutions to pandemic-related issues and their impact on call volume. This approach can help your organization plan for various contingencies.
  4. Incorporate External Factors: In addition to historical data, consider integrating external factors that may have influenced call volume during the pandemic. For instance, changes in government regulations, economic conditions, or the adoption of new technologies can all play a role.
  5. Expert Judgment: Seek insights from subject matter experts who have a deep understanding of the industry, your organization, and the potential impact of the pandemic. Their knowledge and intuition can complement data-driven forecasting methods.
  6. Machine Learning and AI: Leverage advanced forecasting techniques, such as machine learning and artificial intelligence, to model complex relationships between call volume and various influencing factors. These models can help uncover patterns that traditional methods may miss.
  7. Real-Time Monitoring and Adjustment: Continuously monitor the call volume and compare it to the forecast. If there are significant deviations, be prepared to update your forecasts and strategies accordingly.
  8. Communication and Collaboration: Ensure that all relevant stakeholders are aware of the uncertainties and the need for flexible forecasting. Collaborate with departments like sales, marketing, and operations to gain a comprehensive understanding of factors that may affect call volume.
  9. Long-Term Planning: While dealing with immediate uncertainties, it’s crucial to also focus on long-term planning. Develop a strategy for gradually incorporating post-pandemic data into forecasting as it becomes available.
  10. Learn from Experience: As time progresses and more data becomes available, continuously learn from the outcomes and adapt your forecasting techniques accordingly. This iterative learning approach can improve forecast accuracy over time.

Remember that in highly uncertain situations like a pandemic, no forecast will be perfect. The goal is to reduce uncertainty and improve accuracy as much as possible by leveraging available information and employing robust forecasting methods.