You’re talking to your CEO and product team who are eager to find out what issues to prioritize on the product roadmap to improve people sharing via word of mouth.

Have we looked at the product reviews?
We could do some sort of analysis
Extract the most common issues and see what patterns there are
Then we know what to prioritize


Product reviews are important for every marketer to read. Any customer who has taken the time to leave a positive or negative review is doing you a favor by doing your research for you. However it can be time consuming to read every review for your product. You might have hundreds or maybe even thousands of reviews, and each review could be anything from a few lines or thousands of words. Often you just want to extract the relevant information from each review and summarize any patterns you find.
This used to be a highly manual process, but now has become relatively trivial with the advent of AI models like GPT-3 from OpenAI. These large language models (LLMs) excel at lots of tasks, but in particular they can handle arbitrary classification tasks with very few examples given. Using GPT-3 in Google Sheets can make the process far simpler to get results across a wide range of inputs, and get back structured results for use later.

Summarize Product Reviews

00:01 Hey, let me show you how to use G P T three in a Google sheet in order to categorize product reviews.
00:09 So the, if you're not familiar with G PT three it's an AI from the company open ai where you can send it some texts and then you get a response back.
00:19 And normally you have to do that through an api, like send, you know, use code to send the data or you have to kind of like log into the system and use it.
00:29 But that's kind of limiting because quite often I wanna be able to do this scale, like in the spreadsheet or something.
00:36 So what I've developed is basically a Google Apps extension that gives you the function for GT three as like a Google Sheet function.
00:46 So if you wrote like one to write a story once, one time then you could post equal as GT three.
00:56 You can see the function here has a little description. So you just need to input basically the prompt. And then the max tokens that's an optional one.
01:10 You'd have to worry about that. And then the temperature also optional. For now, let's just input this text once upon a time and then close it.
01:19 And it's just gonna complete that text, so it's gonna load, It only takes a few seconds usually. And there we go, once upon a time in a village called Bird Cottage in the Village of Water back West Yorkshire, England.
01:33 Okay. Right? And, you know, if we, you know, wanted to do this a bunch of times, you know, we could just drag this formula down and then now it's made, you know, all these API calls for us to GT three, and it's gonna come up with different different completions.
01:50 So that makes the usage, I think, of GT three much cleaner and easier. And yeah, anyone non-technical can use it.
01:58 So there's a little bit of a setup, which we're gonna go through in second, But first let me show you the actual kind of issue that we're trying to solve.
02:06 Okay? So we have a bunch of predator reviews. You know, this is for a smart a smart baby product. And you know, it helps check for newborn's breathing.
02:16 And and, and you know, like this these are kind of real product reviews. And what, you know, we have 30 here, but you could imagine having hundreds of these, right?
02:27 And we want to really get a sense of like, what are the main issues that people are finding with the product?
02:32 Like, what can we improve for our product development? So what I've done is I, you know, this is a specific review here and then I've just manually tagged it with, like, this person talked about alarm failure, talked about connecting to the sensor.
02:47 They talked about bugs in the app, and they talked about it being expensive. This person talked about the alarm volume, like it should be louder.
02:56 And this one didn't mention any issues. So five star review, they were really happy.  So I just put none. And we're using these as examples to pass to GT three because you know, if I just say, you know, GT three if I just say, you know, tell me the issues mentioned in this product review and then I, you insert the proof review.
03:27 So put amand and then click into here. So let's kinda make this all one piece of text. Let me space there, and then, and then I wanna close it out and say issues.
03:42 That's called a zero shot completion. Meaning we're not giving you any examples, We're just saying, you know, we're just talking to it like a, we were a human, right?
03:50 We're saying, just tell me what are the issues here, and it will do a reasonable job. You'll see. So yeah, it says, app seems to be a bit buggy.
03:58 It has no way to telling their batteries low. There's some repetition here. But in, in general one problem with zero shot is that it doesn't really give you back the structure of data that you want to be useful.
04:13 So you can see, like, my data is much more structured and there's specific labels. So instead of sending it that way we have a set up a template.
04:23 So we've set up a specific you know, list of examples and structure for the data. So this is a really useful prompt engineering tactic.
04:33 So we're asking a question here. We want it to extract issues from the fall product reviews and then we've got like a review, and then issues review, and then issues review, and then issues.
04:45 So we've given it three examples of the ones that I put in here.  And and when when I've, when we've done that, then it'll actually kind of understand, like, almost like, kind of training out on what type of response that we want.
05:00 So this this is what we're doing here. So we're, we're basically taking the you know, the template and then we're adding you know, and review and then and issues here.
05:14 So that, that is the, that is the prompt that we're passing. And that gets us much, much better results. So you can see here that you know, we found a lot of good issues and, and there, you know, some of them are not in like, full structure that we'd like, but this has saved us a lot of time.
05:30 And we can kind of look in here you know, some, some sometimes, like when you're evaluating your prompt you kind of wanna evaluate it like any model.
05:40 So you know, are there any false positives? So did it imagine an issue that the user didn't actually mention? Or are there any false negatives?
05:50 So there was an issue and it didn't pick up on it. So typically I would look at some of the nons when I'm testing this.
05:56 So I'd say like, this one is a non easy to use, annoying only connects to one phone. Okay? So that would be, you know, that's something that it missed, right?
06:05 So that's a, that's a false that's a, a false negative. It said there were non, but it was wrong about that.
06:13 And then you know you know, I, I don't think there'll be that many false positives here cause he's relatively conservative.
06:22 But like, let's see, this guy so they do they mention battery life? Yeah, draining battery. Do they mention connectivity? Yeah, connection drops out.
06:31 Do they mention synchronization? Yeah. So I, I imagine with this type of categorization, we're gonna get more you know, false negatives than false positives.
06:42 Although sometimes it does make up stuff, so you have to be careful. But, you know, this is saved as a bunch of time, and we're really looking at this data in aggregate anyway.
06:50 So you know, the way you'll use this in real world is you might have, you know, hundreds of thousands of these reviews and then drag this across, and then just pick up on the general themes and kind of categorize 'em from here.
07:03 Cool. So so that's that's important. Now one thing you might notice is we have some extra numbers here. So in the function, we ha we've also passed in these optional prompts, max tokens and temperature.
07:16 So what are these? So max tokens, it is basically how many tokens to return. So just to kind of give you an example here yeah, if you'll say hello.
07:28 And then if I put GT three and then put the prompt, if I, if I put max tokens of like five, it's gonna give a really short answer because and it's gonna cut off, right?
07:47 Because token is about four characters roughly, and therefore it's, you know, run out of space to respond. But if I put in, you know, ma max tokens of 50 it's gonna gimme a much longer response.
07:59 And the reason why this is important is that this changes changes the cost of, yeah. So here we go much, Okay, gimme some code here, <laugh>.
08:09 So it changes the actual cost, right?  Because you you pay by the number of tokens for T P three, it's very, very cheap.
08:17 It's something like 2 cents 2 cents an API call on average. But you know, you have to be careful. It's like, one thing to really think about is that whenever you drag this formula, or if you change something and then it recalculates, you're paying for here, you know, 30 API calls or like 27 API calls every time.
08:38 So it could add up pretty quickly if you, what you're doing. So I would say test it on just a few, and then, and then when you're really sure, then, then drag everything down.
08:47 Cool. The other thing we didn't explain is the temperature. So you could say hello, my name is Dave, and then gimme a prompt, put that into the and then let's just, let's just say max token is a hundred.
09:09 That's what we're using as default. And then if we put a temperature of nine that rate high. So that's gonna increase the randomness of the response, just waiting for that.
09:27 Okay, So what was this saying? Okay, <laugh>, I name Dave, This is my jam as it seem, at least it seems I've written the duster a few times.
09:39 <laugh>, okay. Yeah, pretty crazy response, right? So that's a very, very random response. It's a very creative response. And and then if you kind of dial it down to one you're gonna get a very boring response, a very, you know, predictable response.
09:53 So it depends on yeah, here go, I say I'm recovering alcoholic and drug addicts, right? So this is like, when it's looked in the data set, like this is probably the most popular pairing, right?
10:03 My name is Dave and I'm a alcoholic, right? <laugh>. So so this is you know, this is a very a very kind of predictable repetitive response.
10:16 You know, something that's like very highly likely to show up in the data. You know, and the data set here is like the entire internet basically.
10:24 But like as 0.9 would be something creative, something very divergent from the data set. So have a think and, you know, test a few different options out.
10:32 In in this scenario, I found that you want it to be like slightly creative, but not too much. And therefore I kind of have it at 0.4 and I found that that works better, but it's really trial and error in no hard and faster rules to this.
10:47 The default is 0.7. Cool. So that's how that works. If you you know, in terms of setup there's a couple of things you need to do.
10:57 So one is click here and set, Create an account for open ai. Get your API key and paste it in here.
11:04 My API key will be deleted once once I share this with you. So this function won't be working anymore. But what, what I will do is I'm gonna have function here and I'm just gonna delete this equal times.
11:22 So if you need to copy this function then yeah, if you need to copy this function then all you need to do is copy it and then put equals here.
11:33 And the reason why I'm gonna put it on the side here is I'm just gonna take this and then press draw, check down, and then control C to copy, and then control shift v the paste without function, right?
11:47 And formatting. So that just paces the values. And what I've done now is that now there are no you know, there are no responses back, right?
11:56 You know, that, that it's like, it's not gonna change now because GT 83 isn't running, it's just the text value that we got before.
12:05 So I recommend you do this as soon as you've got results that you like, because the worst thing you could do is like accidentally you know, delete <laugh> the function or, you know, or, or change something, and then it all reruns and you might not get actually like this predictable answer back.
12:19 There's a lot of, you know, randomness and entropy in, in, in what it sends you back. Cool. Okay. So this is how it works.
12:26 You could really use this for anything. You could use this for translation. You could use it for, you know, writing ad copy really whatever you want.
12:34 Now that you have this function, if you're interested in how this actually works then you can see there's this GT three run, GT three.
12:44 Actually if you're running into issues, just click here and click run GT three. And that should correct it. But but what this is, is this is actually being added by a Google app script.
12:54 So go to extensions and then get to App Scripts. You can see how this works. So, and, you know, feel free to copy this and use it.
13:04 This is how you would make a Google app script function. What I'm doing here is just adding the menu here, like run g PT three.
13:12 I've code have function that just calls the api with the prompt max tokens in the temperature, and then a few other hard coded settings.
13:22 And then gets the response. We send our API key alongside in the headers. That's how it knows that it's us.
13:28 And then, and that's why you never wanna share the API key cuz they could just rinse your account and spend a ton of money.
13:34 Then it makes the uses your URL app which basically makes the API call for you to the completions engine for open api.
13:45 And then when it gets it back, it takes the top choice and then returns the text. So, you know, you could also edit this if you like the, this you know, like I'll put this out open source so you guys can mess around with it if you want.
13:59 Cool. So that's how that works. You know, if it only works in this sheet. So when you, when you come in here, you wanna just make a copy and then it'll copy the script as well or you know like make a copy, copy of the script.
14:15 And if you wanna kind of edit the script, you can come in here and the Google app scripts and then copy it and kind of make changes if you like.
14:22 Cool. Okay. Hopefully that was helpful.


What’s the advantage to using GPT-3 in GSheets?

Why is it useful to do this sort of analysis of customer reviews?

What does the ‘temperature’ mean when sending a prompt to GPT-3?

It’s amazing how long it has been since most marketers last read a customer review. They’re missing out on a goldmine of insight for figuring out why customers don’t recommend your product to their friends.

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