
Video Drop-off Rate Calculator
Analyze audience drop-off and retention to find exactly where viewers stop watching your videos. Works for YouTube, Vimeo, TikTok, and Facebook analytics data.
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CPM Calculator
Calculate Cost Per Mille (CPM) and analyze advertising campaign performance with precision. Get comprehensive insights on media buying efficiency, budget optimization, and campaign ROI analysis.

CPV Calculator
Calculate cost per view (CPV) for your video campaigns, plus view rate, CPM, and cost per ThruPlay. Works for YouTube, Meta, and TikTok — with a clear breakdown of how each platform defines a "view".

Marketing Budget Calculator
Plan and allocate your marketing budget across channels with an interactive builder. Start from a business-type preset, split spend by channel, check it against the 70/20/10 framework, and export your allocation to CSV.
Related Terms
Population Mean
The population mean is the average value of a variable calculated using all members of a population, rather than just a sample. In digital advertising, it represents the true average value of metrics like conversion rate, CTR, or CPC across the entire audience or campaign. Unlike sample means which contain sampling error, the population mean is the actual parameter being estimated in statistical analysis, though it's often impossible to measure directly due to resource constraints.
Moving Average
A moving average is a statistical calculation that creates a series of averages from different subsets of data over time. It helps identify trends by smoothing out short-term fluctuations and random outliers in metrics like CPC, CTR, or ROAS.
Sample Size
Sample size refers to the number of observations or data points collected in a sample, and is a crucial factor in determining the precision of statistical estimates. In advertising, it directly impacts the confidence, reliability, and validity of metrics such as conversion rates, click-through rates, and return on ad spend (ROAS). The larger the sample size, the more reliable the results, as smaller samples can lead to more variability and less confidence in the conclusions drawn from the data.
