Market Analysis – 81121504 – Economic forecasts

Executive Summary

The global market for economic forecasts, currently estimated at $6.8 billion, is projected to grow at a 3-year historical CAGR of est. 5.2%. This growth is fueled by persistent macroeconomic volatility and the increasing complexity of business planning. The single greatest opportunity for procurement lies in leveraging advanced analytical capabilities, particularly AI-driven predictive modeling, which is rapidly moving from a niche innovation to a core competitive differentiator among top-tier suppliers. Conversely, the primary threat is the commoditization of basic economic data, which pressures suppliers to justify premium pricing through unique insights and proprietary models.

Market Size & Growth

The global Total Addressable Market (TAM) for economic forecasting services is substantial and expanding steadily. Demand is driven by the need for strategic foresight in an increasingly uncertain global economic environment. The market is projected to grow at a compound annual growth rate (CAGR) of est. 6.1% over the next five years. The three largest geographic markets are 1. North America, 2. Europe, and 3. Asia-Pacific, together accounting for over 85% of total market spend.

Key Drivers & Constraints

1. Demand Driver: Heightened Volatility. Geopolitical instability, supply chain disruptions, and dynamic inflationary environments increase corporate demand for sophisticated scenario planning and risk modeling, moving beyond basic baseline forecasts.
2. Demand Driver: Regulatory & Compliance Needs. Financial institutions require complex economic forecasts for regulatory stress testing (e.g., CCAR, DFAST). Similarly, emerging climate disclosure mandates (e.g., SEC, EU SFDR) are creating a new demand segment for climate-related economic impact modeling.
3. Technology Driver: AI & Big Data. The adoption of Artificial Intelligence (AI) and Machine Learning (ML) enables more accurate, high-frequency "nowcasting" and the analysis of vast unstructured and alternative datasets (e.g., satellite imagery, shipping manifests), creating a performance gap between tech-forward and traditional providers.
4. Cost Driver: Talent Scarcity. Competition for top-tier Ph.D. economists and data scientists is fierce, with significant wage pressure from the technology and financial services sectors, directly impacting supplier operating costs.
5. Constraint: In-sourcing & Data Democratization. The availability of high-quality, free public data from sources like the Federal Reserve (FRED), Eurostat, and the World Bank, combined with the growth of internal corporate data science teams, can reduce reliance on external providers for foundational analysis.

Competitive Landscape

Barriers to entry are High, predicated on brand reputation, the significant cost of acquiring and retaining elite analytical talent, and access to proprietary data sets and advanced modeling platforms.

⮕ Tier 1 Leaders * S&P Global (incl. IHS Markit): Differentiated by deep, integrated data sets spanning specific industries (e.g., automotive, energy, maritime) and financial markets. * Moody's Analytics: Differentiated by its core focus on credit risk, structured finance, and economic modeling tailored for financial institutions. * Bloomberg L.P.: Differentiated by the ubiquitous Bloomberg Terminal, which integrates real-time data, news, and economic analysis in a single ecosystem. * The Economist Intelligence Unit (EIU): Differentiated by strong brand recognition and a focus on qualitative country-level political and economic risk analysis.

⮕ Emerging/Niche Players * Oxford Economics: Strong academic linkage and reputation for rigorous, independent econometric modeling and global scenario analysis. * Capital Economics: Independent research firm known for offering concise, often contrarian, macroeconomic viewpoints. * Preqin: Niche focus on providing data and forecasts for the alternative assets industry (private equity, venture capital, hedge funds). * AI-driven Startups (e.g., various fintechs): Focus on using alternative data and ML algorithms for high-frequency, specialized predictions (e.g., inflation, consumer spending).

Pricing Mechanics

Pricing is predominantly structured around annual, multi-seat subscription models that provide access to data platforms, standardized reports, and analyst support. Tiers are based on the depth of data, number of users, and access to specific industry or country modules. For bespoke needs, pricing shifts to project-based consulting fees for custom model development or retainer agreements for ongoing strategic advisory. The price build-up is heavily weighted towards intellectual capital.

The most volatile cost elements for suppliers, which exert upward pressure on pricing, are: 1. Salaries for Top-Tier Talent (Economists, Data Scientists): Recent annual increases of est. +8-12% due to intense competition. 2. Alternative Data Acquisition: Costs for unique, high-value datasets (e.g., satellite, GPS, transaction data) are rising by est. +15-20% annually. 3. Cloud Compute & AI Infrastructure: Expenses for training and running complex ML models have seen steady increases of est. +5-7% per year.

Recent Trends & Innovation

• Major Market Consolidation (Feb 2022): The completion of S&P Global's $44 billion acquisition of IHS Markit created a data and analytics powerhouse, significantly concentrating market power and integrating formerly disparate datasets. [Source - S&P Global, Feb 2022]
• Mainstreaming of Climate Risk Modeling (2022-Present): In response to regulatory pressure and investor demand, all Tier 1 providers have launched or significantly enhanced services that model the economic impacts of physical and transition climate risks.
• Generative AI for Analysis (2023-Present): Suppliers are beginning to integrate generative AI into their platforms, allowing users to query vast datasets using natural language and auto-generate summary reports, aiming to improve user efficiency and accessibility.
• Focus on Supply Chain Economics (2021-Present): Post-pandemic, there has been a surge in new products and models dedicated to forecasting supply chain bottlenecks, input costs, and transportation logistics, using a blend of traditional and alternative data.

Supplier Landscape

Regional Focus: North Carolina (USA)

Demand for economic forecasts in North Carolina is robust and projected to outpace the national average. This is driven by three core hubs: the Charlotte financial center (Bank of America, Truist), the Research Triangle Park (RTP) life sciences and technology cluster, and a significant advanced manufacturing sector. These industries rely on forecasts for capital allocation, risk management (especially interest rate and credit risk for banks), and R&D planning. Local supply is limited to academic analysis from universities like UNC Chapel Hill and Duke; virtually all corporate demand is served by the global Tier 1 providers. The state's favorable business climate and deep talent pool from its universities make it a key growth market for suppliers.

Risk Outlook

Actionable Sourcing Recommendations

1. Consolidate enterprise spend with one Tier 1 and one niche provider. This dual-supplier strategy secures a volume discount (est. 10-15%) from the primary provider while maintaining access to specialized or alternative methodologies from a secondary supplier. This approach mitigates supplier lock-in and provides leverage during renewal negotiations by benchmarking performance and capabilities.

2. Mandate a "capability bake-off" for any new project exceeding $250k. Require competing suppliers to model a specific, historical business problem using their platforms and data. This data-driven evaluation moves beyond sales presentations to empirically test model accuracy, data granularity, and analyst insight, ensuring the selected partner's capabilities directly align with the specific business need.