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Opportunity sizing

As explained in Effective Data Analysis

Opportunity sizing is the process of estimating the value, costs, and expected effects of pursuing a product-development opportunity.

Opportunity sizing estimates the value, costs, and expected effects of pursuing a product or organizational opportunity. It compares likely benefits with the work required. section 1.1.4 section 3.1.1

A practical sizing effort combines:

  • External research and organizational context. section 3.1.1
  • Exploration of available data before project-specific exploratory data analysis. section 3.1.1
  • Measurements such as means, medians, totals, and purpose-built success metrics. section 1.1.4
  • An estimated range of affected users, customers, behaviors, or business outcomes. section 3.1.1

Redesigning a confusing website experience

A user segment shows low weekly engagement, unusually high support-ticket volume, and frequent subscription cancellations. A redesign addressing common sources of confusion can be assessed by estimating the business loss of inaction, labor costs, and possible improvements in engagement. section 1.1.4

Engaging adolescent volunteers

Historical reports, evaluations, grant applications, and city grant data help estimate the potential effect of an adolescent volunteering initiative. Declining active volunteers, lower registrations, and volunteer tenure provide evidence for sizing the opportunity. section 3.1.1

How Effective Data Analysis covers it

How deep it goes
Combines context, available-data exploration, measurements, and success metrics, stopping at estimated effects rather than demonstrated causal results.
Examples
Uses website redesign evidence and volunteer-program records to estimate affected users, behaviors, costs, and potential benefits.
What it assumes
Assumes access to organizational data and external context while teaching how to explore available data before project analysis.
What you can do after
Evaluate and prioritize projects using comparable estimates of affected populations, expected outcomes, and required effort.
How it connects
Connects product management, product analytics, exploratory data analysis, metrics, external research, and prioritization.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.