You Ask, I Answer: What Grade Level for Website Readability?

In today's episode, Tiff asks, at what reading level should website copy be written? Is it the same as print? So the answer to this question is dependent on two things. The first is what readability uh tool are you using, what measure are you using? And second, uh, what audience uh reading level do you have?

What does your audience read at? So there's a bunch of different ways to tackle this. Let's talk about the algorithms first. There's five major algorithms for readability. Uh there's the flesh concade uh grade level uh algorithm, which is essentially uh the number of words divided by the number of sentences minus the number of syllables in the words divided by the number of words.

All these algorithms, by the way, are done in software. You don't have to implement any of them yourself, but you do need to know which algorithm your software is using, and a lot of the times uh software will not necessarily tell you up front, you may have to ask the developers, but you're going to get different answers depending on the on the algorithm that's been used, and different algorithms are appropriate for different use cases. Uh second is the gunning fog index. This is uh words uh divided by sentences minus complex words, which are three syllables or more, divided by the number of words. Gunning fog was uh intended for uh things like healthcare, uh and the uh the same for the following measure uh because you're looking for it to try and reduce the number of complex words uh to to make something more readable.

The next one is called smog, and this is a simple measure of gobbledygook, uh which is a simplification of gunning fog. It is simply the number of complex words divided by the number of uh sentences with a minimum of 30 sentences. Now there's actually a bunch of tuning parameters that go into each of these, but again, the software you're using will have done that. I'm just giving you the the basic idea behind each of these algorithms. The fourth is the automated readability index.

This is the number of characters uh divided by number of words uh plus the number of words divided by the number of sentences. And the last one is the Coleman Liao index, which is uh character-based, and that is uh the number of letters divided by uh the number of letters in a per 100 words minus the number of sentences per 100 words. Coleman Liao was invented for uh machines, essentially machines uh read uh reading scanning. Of these different measures, the one that uh folks tend to use a lot uh you know when they're developing tools is the flesh concaid grade level score. But it's not necessarily the best choice.

And the reason for that is that if you're trying to reduce word c uh complexity of reading, it's those big polysyllabic words, those uh you know uh overly complicated words, long syllable words, that can sometimes mess up uh reading level. And for that, uh gunning fog or smog are probably the two indexes that are the better choices for reducing uh the complexity of something that you're trying to read. Uh smog is used a lot in healthcare. Gunning fog was used um was built for the US Navy to um to reduce the complexity of technical manuals, the the field manuals that uh soldiers are given is to make it easier for them to use them. So both of those indexes uh are good for marketing content, because again, marketers like to be fancy, right?

Um, and use big words and stuff to make their their uh copies sound more sophisticated, especially B2B marketers. We all love our flexible, scalable, uh turnkey interintegrated solutions, etc. And that uh as opposed to software that does the thing, and that those complex polysyllabic words are the ones that uh make readability more difficult. So for marketers, Gunningfog and Smog are probably the the two algorithms to use. So you'll want to check which uh what algorithm your software uses.

Now, how do you determine your audience? There's a couple of different ways you can do this. Number one, um, if you have access to like your customer service inbox or reviews written by your audience, things like that, you can copy and paste a whole bunch of them together and get an overall average uh readability level of the way that your audience writes. The other way is to use social media monitoring software. Um if you have a list of known customers, for example, like a Twitter list, you would put that into your social media monitoring software, and then uh extract out from that data the publications that your audience shares the most.

Go on to those each of the publications, take a sample a random sample of like 10 articles from those publications, put that through your readability software, and that will tell you like your audience is consuming and sharing content at say a sixth grade level or an eighth grade level. That way you can dial in on exactly where your audience is based on the stuff that they're already sharing. If your audience isn't active on social, then you're gonna have to you know solicit content from uh other places. A good way of doing that again is asking people in on intake forms or on customer service calls or in surveys. Uh, you know, hey, what publications do you read?

What newspapers or news sources do you read? Um, what books do you read? Um that will help you again dial in and calibrate on where your audience's reading level is uh based on their media diet. Umce you've got that, then you can start making content that's appropriate to the grade level that uh people are at are at. The other thing that you're gonna wanna do is you're gonna want to measure carefully.

Um readability is i is a metric. It is not necessarily an outcome. So one of the things to do is to look at you run a uh an attribution analysis at at the page level across your web copy and look for the pages that convert the most and then measure the readability scores and then do just a simple regression analysis to see is there any relationship between readability and conversion capability. There may be, there may not be. If you run that analysis and there's no correlation, then is readability a problem?

It wouldn't hurt to dial things into where your audience is most comfortable, but if you find that, say your top converting page is you know written at a substantially different grade level than the like your least converting page, and there's no logical relationship between the two, then don't invest a huge amount of time in in changing the readability of the rest of your site. The thing to do is would be to set up a uh a an actual experiment, like a clinical trial. Take 10 pages of or take 20 pages, ten of them have uh are gonna be ones you're gonna modify the reading level, ten of them are gonna be you're not gonna modify the reading level. They should all be roughly about the same level of conversion power within your um analysis. And then once you've made the changes, observe and see if the conversion power changes over time because you of you made those readability changes.

Don't change anything else. Just make sure it's it's you know 10 and 10 each. One's a control, one's the experiment, and see if readability changes make a difference. If they make a difference, great. Then you know, even after a couple of weeks, right, um, that readability is actually important to your audience.

If nothing happens, you probably don't need to spend a whole lot more time on it. But I would run that test. So your steps are first identify the media diet, choose the algorithm that you're going to use, then assess your site, look for that relationship, and if there is a relationship of some kind, then set up a test uh and and test and improve uh 10 pages and leave 10 pages alone and see how they interact. Um readability is part of natural language processing. There's a lot of fun things you can do with it, but that's for another time.

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