Market Sizing

Market sizing functionality on the platform allows you to quickly see the size, shape and velocity of education markets at a granular level and by multiple dimensions. Filter, sort and select at the Sector, Sub-Sector or Cluster level, by key geographies.

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 uses a combination proprietary ‘top-down’ and ‘bottom-up’ methods to size markets.

Top-Down Approach

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.

Bottom-Up Approach

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.