Skari

Platform/Survey Lab/Driver Analysis
Darts landing near the center of the target
Driver Analysis

Find what to improve first.

Identify the experiences most closely related to satisfaction or loyalty, compare their impact with current performance, and turn the result into a clear improvement priority.

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Importance–PerformancePriority quadrantRegression weightsWhat-if simulationExpected liftAI interpretation
4
Priority Quadrants
1+2
Outcome + Drivers
β
Regression Weights
AI
Interpretation

A score does not show what to fix

01Average satisfaction scores do not reveal what drives the outcome.
02The loudest complaint is not always the highest-impact issue.
03The relative impact of each driver remains unclear.
04The expected impact is unknown before resources are committed.

Driver analysis ranks every driver by real impact — and simulates the lift.

FROM SETUP TO FIELDWORK

Set the outcome and drivers. See the exact rating scale respondents get.

No separate preview step — every driver you add becomes its own rating question on the right.

createsurvey · driver-analysis

Improvement Priority Analysis (IPA)

PriorityCustom tasks · min. 50 respondents

Scale

1Strongly disagree
2Disagree
3Neutral
4Agree
5Strongly agree
+ Add scale point

Outcome variable

Required · only 1
Overall satisfaction

Driver items (min. 2)

1Response speed×
2Ease of use×
3Support quality×
+ Add driver item
Apply to survey
Live preview
Question 2 of 5

Response speed *

Strongly disagree
Disagree
Neutral
Agree
Strongly agree
‹ Previous
* Required
Next ›

Left: the outcome and driver items every IPA study starts from. Right: exactly what respondents see — one driver rated at a time, on your scale.

FROM RESPONSES TO RESULTS

Improvement priorities and expected impact

Every figure below comes from one real example study — 30 respondents rating seven drivers of purchase intent, computed by the same engine your own survey runs on.

Analysis model

The outcome variable, the method, the candidate drivers and how well the model fits.

Design & model fitOLS
MethodOLS multiple regression
Candidate drivers7
Respondents (N)30
Outcome mean3.60
R²0.684
Adjusted R²0.584
F (p)6.81 (p < .001)
Multicollinearitynone

Highest-impact drivers

See how much of the explanation each driver accounts for, with significance marked.

Relative importancePratt measure
Brand trust
65.9%
Reviews
11.1%*
Warranty
7.7%*
Price
7%*
Stock
4.6%*
Features
3.8%*
Payment
0%*

* not statistically significant (p ≥ .05). Pratt measure, normalised to 100%.

High-impact, low-performance drivers

Crossing importance with current performance places each driver in one of four action-oriented quadrants.

Improvement priority matrix4 quadrants
importance →
Improve firstKeep upOver-investedWatch

Importance is the estimated effect on the outcome, not a question asked of respondents.

Improvement priorities

Combining importance with room for improvement produces a ranked list of improvement priorities.

Improvement priority indexImpact Score
Brand trust
61.50
Reviews
11.10
Warranty
6.40
Features
1.90
Stock
1.40
Price
0.00

Impact score = importance × room to improve, where room = 100% − current level.

Expected improvement impact

Select a driver and target level to estimate the expected change in the outcome.

Improvement simulationWHAT-IF
WHAT-IF
Brand trust: 3.2 → 5
Driver weight65.9%
Now → target3.2 → 5
+1.1predicted change
Purchase intent: now → predicted
3.6→4.7
min 1max 5

A linear estimate from the raw coefficients, holding the other drivers at their current means.

Recommended actions

The report summarizes practical implications supported by the analysis results.

ConclusionsN = 30
1Brand trust accounts for 65.9% of what explains purchase intent — the top driver.
2The model explains 68.4% of the variance (R² 0.684, adj 0.584) — very good fit at N = 30.
3Brand trust scores high on importance but low on current level — the clearest thing to fix.
4No multicollinearity — every driver is under VIF 5, so the coefficients read straight.

A COMPLETE REPORT

See the full report from summary to conclusion.

The six cards above are fragments of this. Scroll the real output end to end, right here on the page.

Improvement priority analysis (IPA) — purchase intent · 30 simulated respondents
Scroll to explore the full report

IN THE REPORT

From feedback to priorities

Compare impact with current performance, identify the highest-priority drivers, and estimate the value of improving them.

Derived importance

Regression on your outcome variable gives each driver a real weight.

Performance scores

Mean performance rating for every driver, on one scale.

Priority quadrant

Importance vs performance plotted into a four-quadrant action map.

Prioritize / Maintain / Monitor / Secondary

Every driver sorted into a clear action bucket.

What-if simulation

Improve any driver and preview the expected outcome lift.

Target setting

Set a target score per driver and see the impact.

Expected lift

Quantified points gained on satisfaction or NPS.

Regression diagnostics

R², coefficients, and model fit for full transparency.

AI interpretation

Plain-language summary of where to act first and why.

Segment breakdown

Re-run the driver map by age, gender, or usage group.

ALSO IN SURVEY ANALYSIS

More survey methods

Each method answers a different question — same interface, different lens.

Focus on high-impact improvements

See which drivers have the greatest impact, where performance is falling short, and what improvement could change the outcome.

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