Calculate the Error Rate:
Use the values from the columns "[Column with number of errors]" and "[Column with total number of user interactions]" in the attached CSV to calculate the Error Rate using the following formula:
Error Rate = (Number of Errors ÷ Total Number of User Interactions) × 100
After calculating the Error Rate, report where it lands on the following scale (lower is better):
Very Good = below 5%
Good = 5–9%
Average = 10–19%
Poor = 20–29%
Very Poor = 30% and above
Next, analyze the responses in the column "[Column with qualitative explanations]" in the CSV (e.g., error notes, open-text feedback) to identify the most common error types and root causes, and provide clear, actionable suggestions for how the Error Rate could be reduced.
Error Rate — Example Report
Summary
Error Rate: 7.5% → Good
Total User Interactions: 40,000
Number of Errors: 3,000
Calculation
Error Rate
=
3,000
40,000
×
100
=
7.5
%
Error Rate=
40,000
3,000
×100=7.5%
Scale placement: 7.5% falls in Good (5–9%).
Key Breakdowns (from CSV)
By device
Mobile: 26,000 interactions / 2,300 errors → 8.8% (Good)
Desktop: 14,000 interactions / 700 errors → 5.0% (Good–Very Good threshold)
By step in flow
Step Interactions Errors Error Rate Rating
Step 1: Login 10,000 800 8.0% Good
Step 2: Product Configurator 9,000 1,100 12.2% Average
Step 3: Cart 8,000 400 5.0% Good
Step 4: Payment 7,000 550 7.9% Good
Step 5: Confirmation 6,000 150 2.5% Very Good
Highest error concentration: Step 2 (Product Configurator) at 12.2% (Average).
Qualitative Insights (from explanations column)
Top themes and sample quotes:
Configurator option errors (invalid combos) — 38%
“Kept saying color not available with that size, but it showed as selectable.”
Validation errors on login — 22%
“Didn’t know if I needed username or email—error was vague.”
Payment declines unclear — 18%
“Just said ‘transaction failed’ with no explanation.”
Cart promo code rejections — 12%
“Said invalid, but code was copy-pasted from email.”
Minor field formatting issues — 10%
“Phone field wouldn’t accept dashes.”
Recommendations (prioritized)
Strengthen configurator logic: disable invalid options instead of surfacing errors after selection.
Clarify login requirements: unify to email-only, add placeholder text, and show error copy like “Enter your account email.”
Improve payment error messaging: differentiate between bank decline, expired card, or insufficient funds.
Audit promo code UX: allow paste with spaces, add clear eligibility messaging upfront.
Standardize field formatting: auto-format inputs (phone, zip) and normalize before validation.
Next Checks
Track error rate per step weekly to validate configurator fixes.
Run usability test focused on login and configurator error handling.
Target: reduce configurator error rate from 12.2% → ≤6%, bringing total Error Rate down to ≤5% (Very Good).