Analytics Manager - HR here. Yes, there is a lot of contradictory information out - it is not you. This leads to one of my biggest frustrations with leaders who pull random shit and try to use it as a benchmark :) You will often times see retention rate mixed up with turnover, often because HR departments like the sound of retention and see it as a positive - so a lot of the retention rates you see are weird inverse but not quite accurate turnover statistics. So what is retention? Basic premise: On january 1, 2022, you had 100 employees. Today, OF THOSE ORIGINAL 100, you have 80. Your YTD retention is therefore 80/100. Unlike turnover, you do not factor anyone that joined during that period of time. As an equation: Retention = Number of employees active at the end of your period that were also employed at the start of the period/Number of employees at the start of the period. So the max retention rate is 100%. Another piece to note is that we are talking about headcount, not FTE (another common mistake). So what is this about a period? Retention can be calculated for any period you want really, don't know why you would want to, but you could technically calculate a 1 day retention or a 50 year retention. So using the above formula, you just plug your end and start dates. First year retention/new hire retention. This is a common one. First year retention looks only at those employees with less than one year from the hire date. So basically just filter your population, same formula/concept. To clearly answer this: I am seeing contradicting information online from new hires should be counted to no, new hires should not be counted - what is the right way to measure this? If your start period is Jan 1, 2022 and end period is Dec 31, 2022, no one hired starting on or after Jan 2, 2022 is counted. Anyone including hires in their formula is doing some weird turnover hybrid. Turnover is calculated by: Number of terminations/(avg of headcount in the period) Which means that if you started with 100, hired 10, no one termed, your denominator would be 105, so new hired would count. Here is how you get over 100% You start with 100, hire 200, but 250 people leave. Your calculation becomes: 250 terms/(100 starting - 50 ending) = 250/50 or 500% turnover. Assuming all 50 of the ending were also part of your starting group, your retention would be 50%. If only 25 of those 50 were part of the original 100, then your retention would be 25% More on reddit.com

If a "retention rate" is the percentage of users that are not new, then yes. More on reddit.com

As your current retention rate approaches 0 or 100%, the value of that calculation becomes exponentially large (or small)... so expect those cases to give you wildly inaccurate suggestions.

Other than that, if your desired retention rate and current rate are similar, then the results of the calculation will most likely be good enough; and that's about as accurate as I can get.

The calculation itself uses a mathematical model of memory that involves both storage strength and recall strength (See the works of Dr. Bjork http://gocognitive.net/interviews/new-theory-disuse). But as storage strength changes cannot be predicted mathematically (as far as I know), the calculation you quoted assumes that storage strength remains unchanged (which it does not, but this allows us to have a good-enough calculation as storage strength probably only improves by a negligible amount).

Also be aware that using an interval modifier affects all cards governed by the deck options group. Given an ideal adjustment ratio, your success rate would only match your target rate on average, and then only with enough reviews such that their average is truly representative of all possible reviews.

Storage strength changes are a big wild-card and assuming that they don't exist may lead to inaccurate conclusions for many cards. Furthermore, adjusting individual cards based on averages taken from many cards will practically guarantee that some intervals become too long, while others too short (with ideally, equal numbers of both).

Add to that Anki's fuzz-factor, which helps prevent cards from always showing up in the same session, and you can see that even a perfectly accurate calculation would give you less-than-perfect results. But, in the long run, probably better than the default settings would give you.

You might be interested in a post I wrote on this issue: Thoughts on a New Algorithm for Anki

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I'd visualize retention/churn in a table like this: https://qph.is.quoracdn.net/main-qimg-28a6780fa20b2f3e308a8aae6a182f55?convert_to_webp=true

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