Importance Performance Analysis (IPA)
Importance-performance analysis (IPA) is a decision tool that plots attributes by their rated importance and performance on a two-dimensional grid to identify which ones to improve, maintain or deprioritize.
중요도-성과 분석(IPA)은 속성들을 평가된 중요도와 성과에 따라 2차원 격자에 배치하여 개선, 유지, 우선순위 하향 대상을 식별하는 의사결정 도구이다.
How do I decide which service attributes to improve based on customer ratings of importance and performance?
고객이 평가한 중요도와 성과를 바탕으로 어떤 서비스 속성을 개선할지 어떻게 정하나요?
Importance-performance analysis places each attribute on a grid of importance versus performance, divided into quadrants commonly labeled concentrate here, keep up the good work, low priority and possible overkill. It fits survey data where respondents rate the same attributes on both importance and satisfaction or performance.
When to use Importance Performance Analysis (IPA)
- Survey ratings of service or product attributes on importance and performance
- Setting improvement priorities for tourism, education or public services
- Comparing priorities between customer segments
When to use something else
- Importance should be derived from the effect on overall satisfaction: use key driver (regression-based) analysis
- Many criteria must be weighted for a formal choice among alternatives: use AHP or TOPSIS
Assumptions and requirements
- The same attributes rated on comparable importance and performance scales
- A justified choice of crosshair (scale midpoint or data mean)
- Adequate sample size for stable attribute means
- Attributes that are distinct and relevant to respondents
What the analysis reports
- Mean importance and performance per attribute
- IPA grid with quadrant assignment
- Importance–performance gaps
- Paired t-tests of gaps
How to report it
IPA placed [n] attributes in the 'concentrate here' quadrant, with the largest gap for [attribute] (importance M = [value], performance M = [value]).
Related methods
- Key Driver Analysis
- Analytic Hierarchy Process (AHP)
- Paired Samples t-test
Run Importance Performance Analysis (IPA) on Quorant
- Data you need
- a table (CSV or Excel) with item columns (e.g. questionnaire items) and predictor (feature) columns
- Price
- 150 credits for one analysis up to 10,000 rows (complexity level 1); larger or wider data costs more. Optional methodologist review: 900 credits.
- Checking
- Python results are cross-checked against R reference outputs in every deployment
- Before you pay
- A free pre-analysis diagnosis checks your data and gives an exact quote; nothing is deducted until you approve it.