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  2024 Conversion Benchmark Report

Methodology

CBR-2024-methodology-hero-graphic_mobile

  About our data

Overview

The conversion data analyzed in the report comes from July 23, 2023 to July 23, 2024. The benchmarks are derived from over 464 million unique visitors which resulted in 57 million conversions across over 41 thousand different landing pages.
Depending on the data available, some conversion benchmarks are represented as medians and others are represented as averages. Please reference the report to confirm how each data point is represented. Here are quick definitions of both:
  • Median: The value that lies exactly in the middle of the data set. The median is particularly useful in wider data sets because it is not affected by extreme values (outliers).
  • Average: The sum of all numbers in the data set divided by the total number of values. It provides a general sense of a data set’s central value.

Industry analysis

Industries were identified by using machine learning to sort pages into topics and the output was refined to match those topics to the industries included in the report.
The following criteria was used to clean the data:
  • Any industries, subcategories, or conversion types with fewer than 400 pages were excluded.
  • Pages with fewer than 50 visitors or no conversions were excluded.
  • Subcategories that could skew results like political campaigns, religious topics, terms and conditions were excluded.

Readability

We’ve developed a multi-faceted approach to analyzing readability in our Conversion Benchmark Report, thanks to the power of multiple metrics (which means we don’t rely on just one readability score). Instead, we use a combination of well-established readability metrics to provide a comprehensive view of your content’s accessibility.
Our analysis includes:
  1. Flesch-Kincaid Grade Level: Estimates the U.S. grade level needed to understand the text.
  2. Flesch Reading Ease: Provides a score from 0-100, with higher scores indicating easier readability.
  3. SMOG Index: Estimates the years of education needed to understand a piece of writing.
  4. Coleman-Liau Index: Another grade-level estimator that estimates U.S. grade level based on characters per word and sentences per word, rather than syllables.
  5. Automated Readability Index: Factors in characters per word and words per sentence.
  6. Dale-Chall Readability Score: Uses a list of 3,000 words that 80% of U.S. 4th-grade students understand.
  7. Linsear Write Formula: Originally developed for the U.S. Air Force to calculate the readability of technical manuals.
  8. Gunning Fog Index: Estimates the years of formal education needed to understand the text on first reading.
Rather than calculating these scores independently, our algorithm takes all these metrics into account to reach a “readability consensus.” 
Here’s how it works:
  1. We calculate each readability score for your content.
  2. We convert each score into a grade level range.
  3. We then find the most common grade level across all metrics.
  4. This consensus grade level becomes your content’s “Unbounce Readability Grade.”
If you’re wondering what you can do to improve your copy’s readability, keep in mind that it isn’t just about using simpler words—you need a multi-faceted approach. Our analysis shows that several key factors influence both readability and the impact on your conversion rates:
  • Word complexity: The number of “difficult” words (typically those with three or more syllables) can increase reading difficulty.
  • Word count: While longer content can be more informative, it can also be more challenging to read. Our data shows that word count has a direct impact on conversion rates, with optimal lengths varying by industry.
  • Reading time: Closely related to word count, the estimated time it takes to read your content can affect user engagement and conversion rates. Shorter reading times often correlate with higher conversion rates.

Contributors

Ana Nieto, Caoimhe Rainey, Cecilia Martinez, Danielle Torrie, Jorge Puebla, Josh Gallant, Mitesh Raniga, Paul Park, Pooja Dahiya, Sukh Singh, Tony Mayer.
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