Last Updated: September 27, 2024
Businesses are updating and adapting their analytics, planning and forecasting strategies to integrate external factors. Macroeconomic indicators, such as GDP growth, inflation, interest rates, combined with industry-specific trends, customer sentiment, offer valuable insights to enhance decision-making, the accuracy of business forecasts and act as an early warning system – constantly watching and alerting the business as the external environment changes.
However, understanding how to effectively integrate external data – and identify the most relevant, impactful and leading indicators – for your business analytics and planning processes can be challenging.
At Prevedere, we specialize in enabling businesses to – easily and cost effectively – access and leverage external economic data, deploy AI-powered analytics and econometric models, all to improve forecasting precision and inform decision-making. Explore our Platform introduction here.
This blog, authored by Prevedere’s Chief Economist, Michelle Green, outlines best practices for incorporating external economic indicators into analytics and planning as a foundation for businesses to build resilience and agility in response to changing economic conditions.
The Importance of External Data as Business Drivers
External data includes macroeconomic indicators (GDP, inflation rates, and unemployment levels) that influence market conditions, as well as industry, consumer activity and sentiment, demographic, weather and other indicators – constantly measuring the external environment and forces outside of your business’s walls.
By incorporating these indicators into business analytics and planning processes, businesses will gain a broader understanding of the external factors that shape and impact demand, costs, and overall performance. Armed with this critical information, businesses make better-informed decisions that improve business outcomes in both stable and uncertain times.
The external indicators can explain past and current performance, as well as potentially be leading indicators and business drivers of future performance. Combining internal and external data is a critical analytics and planning capability.
Identifying Relevant External Indicators
When planning future performance, it is essential to identify, incorporate and account for external indicators that have a proven relationship with your business. Companies should:
- Focus on relevant drivers: Identify the macroeconomic and external factors most strongly correlated with your business performance. Historical analysis can reveal these connections.
- Leverage credible sources: Utilize government data (e.g., the Bureau of Economic Analysis, Federal Reserve), financial institutions, and industry research reports for accurate and timely information. Many data sources are public and available at zero cost (although it may be challenging to automate integration). Where needed, you should invest in proven data sources to augment and complement public sources for a complete, consistent and always up to date picture of your external environment.
- Benchmark data from industry reports: Benchmarking against industry and market-specific trends helps assess how broader economic conditions will influence performance.
- Embrace and harness multiple approaches: As well as leveraging external indicators for your business-level analytics and planning, apply them to market and industry-level forecasts (or even major competitors) to validate growth and market-share objectives.
External Data Collection and Management
Accurate, up-to-date data is the foundation of effective forecasting. Businesses must:
- Ensure data accuracy and timeliness: Use reliable, regularly updated external data sources (like Prevedere’s Global Intelligence Cloud), which should issue alerts based on thresholds and directional change.
- Integrate internal and external data: Consolidating external data from diverse sources ensures a more complete picture of potential drivers. An easy starting point is identifying, dashboarding and monitoring the most relevant external indicators, then developing and issuing reports and analysis focused on external indicators, alongside internal key performance indicators.
- Implement data management tools: Utilize tools that collect, centralize and facilitate the consistent and efficient management of large external datasets – expanding easily with new external data related to your next use cases.
Advanced Analytical Models and Forecasting
Modern econometric techniques and AI offer powerful ways to quantify relationships between economic factors and business performance. Tools will provide skills (e.g. Generative AI) and capacity (e.g. Predictive AI) to augment your data and analytics team’s capabilities.
By applying these methods, businesses can build more accurate forecasts – standalone external data-driven models, and as inputs to internal forecasts. Best practices include:
- Econometric modeling: Use regression analysis, time-series models, and scenario-based forecasting to understand the impact of economic drivers under various conditions.
- Scenario planning: Develop optimistic, baseline, and pessimistic scenarios to account for a range of economic possibilities.
- Machine learning: Employ machine learning algorithms to detect emerging patterns and improve forecast accuracy over time, continuously refining models with new data inputs.
- Implement analytical tools: Specialist platforms bring AI and machine learning to deliver econometric modeling and scenario planning. Such platforms can enable knowledgeable analysts to harness external data, add an economist perspective and enhance overall analytical and planning power.
When developing your external data-driven models and forecasts inevitably the initial phase includes learnings, refinement and optimization. Therefore, allow for an “observation period” to confirm appropriate model health and forecast accuracy, educate your audience, and build a consensus-forecast process that incorporates the external indicators and insights as an additional perspective.
Incorporating Economic Insights into Forecasting
When ready, external indicators and insights will be incorporated into analytics, reporting and forecasting – for guidance, acting as guardrails and influencing analysis and decisions related to performance.
Integrating and combining insights from external data into your forecasting process ensures that your business remains aligned with the broader economic landscape. Key steps include:
- Cross-functional collaboration: Engage departments across finance, marketing, and operations to ensure all perspectives are considered when interpreting economic data.
- Regular updates: Continuously adjust forecasts based on the latest external environment developments, indicator updates, and model outputs, making the process dynamic and responsive.
Monitoring, Evaluation and Continuous Improvement
Evaluating the accuracy of your standalone external data-driven models and consensus forecasts against actual outcomes is critical. Businesses should:
- Monitor your external indicators and models: Monitor actual performance against forecast expectations, and adjust external indicator monitoring and alerting processes, and your models accordingly.
- Establish feedback loops: Regular reviews of external business drivers and their effects will ensure analysis and forecasts are relevant.
- Optimize: Explore, test and find the optimum balance – such as breaking summary models into lower levels by brand, category, and regions.
- Adapt to changing conditions: As economic drivers and business conditions evolve – analysis and models must be updated or added to best reflect new realities.
Risk Management
Economic volatility presents risks that must be accounted for in business forecasts. Businesses should:
- Incorporate External Indicators into analysis and planning: Understand, monitor and plan for the impact of the external environment. Develop an early warning system.
- Develop contingency plans: Prepare for different economic scenarios and integrate risk mitigation strategies into the forecasting process.
- Maintain organizational agility: Ensure the organization is prepared to adapt to rapid changes in the economic environment.
Tools, Technology and Training
Implementing the right tools and providing adequate training to your team is crucial for success. Key recommendations include:
- Advanced external data management and forecasting platforms: Utilize platforms like Prevedere’s, which support the integration of external data with AI-driven econometric models.
- Integration, automation and ongoing maintenance: Ensure data sources, relevant external data, insights, models and forecasts are integratable into existing processes. As processes are updated, integration allows for automation and easy maintenance.
- Data visualization: Clear communication of economic insights to stakeholders is essential. Use dashboards and visual reports to convey complex data simply.
- Ongoing training: Provide continuous education in external data and impact, econometric modeling, data analysis, and forecasting to empower teams to use these tools effectively and for business users to understand the impact and implications.
Conclusion
Incorporating external economic data to deliver insights and planning drivers is an essential best practice. Building an analytics and planning process that combines a business’s internal plans, strategy and tactics alongside and integrated with the external environment reality offers businesses a significant competitive advantage.
By adopting best practices in identification and data collection of the external data that are business drivers, used for modeling, forecasting and scenario planning, businesses can enhance forecast accuracy, improve decision-making, and increase resilience to economic fluctuations.
At Prevedere, we help businesses harness the power of economic data and AI-driven analytics to deliver external insights and build robust, future-proof forecasts. Contact us and we’d be delighted to discuss how our Platform can support your business’s needs.
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