HolonIQ has developed a proprietary methodology for market sizing, combining our ‘top-down’ global education economic model, powered by insights from market experts from around the world, with cutting-edge ‘bottom-up’ machine-learning driven revenue estimates for tens of thousands of institutions and firms.

HolonIQ has adopted the World Bank regional classification system. Some regions and sub-regions are modified to better reflect social and economic groupings more relevant to education.

See Definitions for Sectors, Sub Sectors, and Clusters which are part of HolonIQ market sizing that are available on the HolonIQ Platform.

We model expenditure from three main sources.

Public & Private Institutions - Governments, NGOs, Public and Private Schools and Universities.

Corporates. Private organizations, generally businesses, that purchase education and training related goods, services, technologies and equipment for their employees and other stakeholders.

Consumer - Individuals who purchase education products and services for their own learning including parents spending on their children's education and adults investing in personal education and upskilling.

We model three types of spending.

HolonIQ uses a combination proprietary 'top-down' and 'bottom-up' methods to size markets.

HolonIQ has a proprietary top-down global economic model that builds market estimates independent of but still leveraging OECD/UNESCO/World Bank assumptions for government and private sector spending.

We also index of thousands of public market sizing estimates for the global, regional and individual countries and categories. These estimates are weighted by source reliability for education market estimates (reputation, trustworthiness, historical accuracy, reliability) and information reliability (triangulates with other sources, no doubts on authenticity, no contradictions). Our consensus algorithms build a weighted 'outside-in', 'top-down' market size for defined splits leveraging this consensus approach.

These two methods, the global economic model combined with our consensus algorithm form our top-down methodology.

HolonIQ's bottom-up market sizing methodology is primarily driven by proprietary revenue estimates for education and training providers around the world.

We leverage machine learning to estimate the revenue of companies in each market based on a wide range of variables including but not limited to country headquarters, number and growth of employees, known revenue sizing points from public disclosures, web traffic, threshold roles and hiring, web technology spend and other proprietary signals useful in predicting an organisations revenue.

Each of the 11 Sub Sectors and 50 Clusters are then classified by industry concentration to estimate total revenue from the long the tail of un-identified companies (using the Herfindahl–Hirschman Index score and internal algorithms).

These two methods, individual company revenue estimates combined with industry concentration based 'tail revenue' estimates form our bottom-up methodology.

HolonIQ has a deep global network of experts from all geographies and sectors. Interviews with experts around the world help us close out any reconciliation issues when we merge the top-down and bottom-up methodology.

This process often involves triangulating volume and price assumptions in large value and volume categories and benchmarking against peer industries such as healthcare and other comparable benchmarks.

The result is a point estimate in each of the segments identified below.

Our annual market-sizing update currently builds 1,170 discrete estimates. The most recent build includes 585 estimates for 2019 expenditure and 585 estimates for 2025 expenditure. These two data points represent a compound annual growth rate for each of the 585 dimensions.

The 585 dimensions cover the following dimensions

Geographic Granularity

4 Sectors

11 Sub Sectors

50 Clusters

See the Global Learning Landscape