Metric Definition
Percentage of positive votes
Percentage of positive votes measures the proportion of knowledge base article ratings that are positive, typically captured through "Was this article helpful?" yes/no prompts. It is the most direct signal of whether self-service content is actually solving customer problems.
7 min read
What is percentage of positive votes?
Percentage of positive votes is the share of help article ratings that indicate the content was helpful. Most knowledge base platforms include a feedback mechanism at the bottom of each article, typically a binary "Was this article helpful? Yes / No" prompt or a thumbs up/thumbs down widget. The percentage of positive votes aggregates these ratings across all articles or for a specific article, category, or time period.
This metric matters because knowledge base views alone do not tell you whether customers found what they needed. A high-traffic article with a low positive vote percentage is actively failing customers: they are finding the article but not getting the help they need, which often leads to a support ticket. Conversely, an article with fewer views but a 95% positive vote rate is delivering genuine self-service value.
The metric also creates a prioritisation framework for content improvement. Articles with high traffic and low positive vote percentages are the highest-impact improvement targets. Rewriting, restructuring, or updating these articles can reduce ticket volume more effectively than creating new content for topics that already have adequate coverage.
Percentage of positive votes is a lagging indicator of content quality but a leading indicator of self-service effectiveness. When it declines, support ticket volume for the affected topics typically increases within days or weeks as customers give up on self-service and contact human agents instead.
Response rates on article feedback prompts are typically low, ranging from 2% to 8% of article viewers. This means the signal is statistically meaningful only for articles with sufficient traffic. For low-traffic articles, use qualitative feedback or support ticket analysis instead of relying on vote percentages.
How to calculate percentage of positive votes
The formula is simple, but the way you segment the data determines how actionable it becomes. Tracking the overall percentage gives a high-level view of content health, but the real value lies in article-level and category-level analysis that reveals exactly where content quality is strong or weak.
| Segmentation | Formula | Purpose |
|---|---|---|
| Overall positive vote % | (All Positive Votes / All Votes) x 100 | Executive-level view of knowledge base content quality |
| Per-article positive vote % | (Article Positive Votes / Article Total Votes) x 100 | Identifying specific articles that need rewriting or updating |
| Per-category positive vote % | (Category Positive Votes / Category Total Votes) x 100 | Finding entire topic areas where documentation is insufficient |
| Weighted by traffic | Sum of (Article Positive Vote % x Article Views) / Total Views | Understanding the experience-weighted quality, giving more importance to high-traffic articles |
Percentage of positive votes in a metric tree
The positive vote percentage decomposes into the factors that determine whether a reader finds an article helpful. These factors span content quality, content relevance, and the broader context in which the article is consumed.
This tree reveals that a low positive vote percentage is not always a writing problem. If readers arrive at an article through a misleading search result or internal link, the article may be perfectly well-written but irrelevant to their question. If the product has changed since the article was last updated, the instructions may be clear but incorrect.
When the positive vote percentage drops for an article or category, the tree provides a diagnostic checklist. Is the content still accurate? Has the product changed? Are readers arriving with the right expectations? Is the article readable on mobile? Each root cause has a different fix, and the tree prevents the common mistake of rewriting an article when the real problem is a stale screenshot or a broken search mapping.
Positive vote percentage benchmarks
| Content type | Average positive vote % | Top-performing range |
|---|---|---|
| Getting started and onboarding guides | 70% to 80% | 85% to 95% |
| How-to and procedural articles | 65% to 75% | 80% to 90% |
| Troubleshooting and error resolution | 50% to 65% | 70% to 85% |
| API and developer documentation | 55% to 70% | 75% to 85% |
| Policy and billing information | 60% to 70% | 75% to 85% |
Troubleshooting articles naturally have lower positive vote percentages than how-to guides. Customers reading troubleshooting articles are already frustrated, and the article may not cover their specific error condition. Benchmark troubleshooting content separately from procedural content to set realistic improvement targets.
How to improve percentage of positive votes
- 1
Prioritise rewrites by impact: high traffic, low positive vote %
Sort articles by the product of views and negative vote percentage. This surfaces the articles that are failing the most customers. Rewriting the top five articles on this list will improve the overall positive vote percentage more than any other single action.
- 2
Keep articles aligned with the current product
Establish a process that flags knowledge base articles for review whenever a related product feature is updated. Outdated screenshots, changed navigation paths, and deprecated settings are the most common causes of negative votes on previously helpful articles.
- 3
Add step-by-step visuals and screenshots
Articles with annotated screenshots and step-by-step visual guides consistently receive higher positive vote rates than text-only articles. Visual confirmation that the reader is on the right screen or clicking the right button reduces confusion and builds confidence.
- 4
Collect qualitative feedback alongside the vote
Add an optional text field that appears after a negative vote asking "What was missing or unhelpful?" This qualitative data reveals exactly why the article failed. Without it, you are guessing at the cause of negative votes.
- 5
Structure articles for scanning, not reading
Most knowledge base readers scan for the specific step or answer they need. Use numbered steps, clear headings, expandable sections, and bold key terms. An article that buries the answer in the fourth paragraph of continuous prose will receive more negative votes than one that surfaces it immediately.
Related metrics
Customer Satisfaction Score
CSAT
Product MetricsMetric Definition
CSAT = (Satisfied Responses / Total Responses) × 100
Customer satisfaction score measures how satisfied customers are with a specific interaction, product, or experience. Unlike NPS which measures loyalty, CSAT captures satisfaction at a moment in time, making it ideal for evaluating specific touchpoints in the customer journey.
Customer Effort Score
CES
Product MetricsMetric Definition
CES = Sum of All Effort Ratings / Number of Responses
Customer effort score measures how much effort a customer had to exert to accomplish a goal with your product or service. Research shows that reducing effort is more predictive of customer loyalty than increasing satisfaction, making CES a powerful complement to NPS and CSAT.
First Contact Resolution
Support effectiveness
Operations MetricsMetric Definition
FCR Rate = (Issues Resolved on First Contact / Total Issues Handled) × 100
First contact resolution measures the percentage of customer enquiries resolved during the first interaction without requiring follow-up contacts, transfers, or escalations. It is the single most influential metric for customer satisfaction in support operations.
Net Promoter Score
NPS
Product MetricsMetric Definition
NPS = % Promoters - % Detractors
Net Promoter Score measures customer loyalty by asking how likely a customer is to recommend your product or service. It is the most widely used customer experience metric, providing a single number that captures sentiment and predicts growth through word-of-mouth.
Connect content quality to support outcomes
Build a metric tree that links article positive vote rates to ticket deflection, self-service adoption, and customer satisfaction so your content team can prioritise the rewrites that matter most.
