Today in Digital Marketing - What Science Says the "Perfect" Instagram Image Looks Like

Episode Date: February 4, 2022

Is there a formula for the "perfect" composition of an Instagram image that will get your brand more engagement?Dr. Gijs Overgoor of the Rochester Institute of Technology set out to discover that formula.In this sample of a Premium weekend edition, Tod spoke with Dr. Overgoor about his findings.Our Sponsors:* Check out Kinsta: https://kinsta.comPrivacy & Opt-Out: https://redcircle.com/privacy

Hello, hello, it's Todd, and this is a special episode today. Normally, Monday to Friday, we do the daily newscast. But today, a sample of one of our weekend editions, where we do a deep dive into a specific topic. Weekend editions are exclusive to the premium feed, and in the weeks ahead, we'll be exploring whether having an Amazon storefront cannibalizes sales on your own website, the future of marketing to genetic predispositions, and how your brand's

logo can inoculate you against negative attention. These are interviews you will not hear on other marketing podcasts. You will only hear them on our premium feed. Tap the link in the show notes or go to todayindigital.com slash premium. And now, here is a free sample of one such weekend edition. This is a premium exclusive. Well, if there's one thing digital marketers are looking for, it's the perfect formula. A cheat sheet that we can rely on to generate better results.

Take, for instance, engagement. We know there are some things social media managers can do in a post to juice engagement. Asking for it, for one, in the form of, tell us what you think below. Engagement's important, not only for the social proof, but also that as social platforms like Facebook continue to remove interests, we rely on likes and comments and shares as indicators of interest, proxies, data points that we can use to move people from a prospecting ad set into mid-funnel. At most organizations, the one thing content managers sweat over

are the images, especially on Instagram. So is there a formula for the composition of the Instagram images we create, one that, if followed, would get us more likes? That's what Heiss Overgur, Assistant Professor of Marketing at the Rochester Institute of Technology, set out to discover. He and his colleagues analyzed more than 150,000 images from 633 brands across 27 different industries. Dr. Overgur joins me now. Welcome. Thank you. So in the research space, there are really two opposing views on image creation. One faction thinks that simple images work better.

The other says more complex images. Can you walk us briefly through that debate? Yeah, so this debate traditionally comes from advertising, where we have this one stream of research that say, remove all the clutter, make it as simple as possible, straight to the point so that the consumer knows what you're talking about. And that way you can capture the attention and capture the interest and engagement of a consumer. And then the other stream of research says, well, what if we clutter our advertising? What if we fill it up with a lot of complexity and a lot of different types of content? And that really makes them stop and look a little bit closer and kind of draws the consumer in that way. And we picked up on these two streams of

research and we were like, well, one, what does that mean? It can't be that they're both right or maybe they are both right. And then what does this look like on social media? I want to get to the results in a moment, but can you talk me through your process? How was this done? Was it done manually or an automated tool? We opted for algorithms. So the two streams of research that we just discussed, they went for the manual route. So they had a couple of grad students code a bunch of advertisements, or they had specific trained judges kind of go through these type of content and judge them manually on different types of aspect of related to complexity. And we said, well, that is kind of

subjective. So what if we take it and take algorithms and construct a more objective view of this matter. So we set out to construct these automatically. And once we have algorithms, we can basically study as many as we want all at once. I know some of the things you were looking for were things like how symmetrical an image was, whether there was a human face. Can you walk us through what else you looked for? Yeah. So we started with this concept that is called visual complexity. And visual complexity basically captures any kind of intricacies and detail within an image related to several different aspects. And what we found is that we can split this up into two categories in the terms of images.

So one of these is what we call the feature complexity. And the feature complexity encapsulates all the inherent variation within an image. So really on like a pixel level, pixel to pixel basis, how much variation is there between these pixels? So you can think of color, you can think of how much detail and contrast there is within an image, or how much variation there is between bright and not bright. So that is feature complexity on the one hand, so that you analyze pixels specifically and variation between pixels. Then on the other hand, we have complexity in terms of design.

So the complexity in terms of design has everything to do with objects in an image. So the story of an image, where are these objects located? And are they, for example, very scattered across this image? Are they asymmetrically structured? And how many objects are there? And then the more objects there are and the more irregular they are, the higher the design complexity. Okay, well, let's start with the first one. What did you find was the optimum level of feature complexity, that being the pixel elements? We found that the optimal level is somewhere in, that being the pixel elements?

We found that the optimal level is somewhere in the mid-regions. And specifically, we find that we can find this optimum for each of the three features. So the color complexity, the luminance or brightness complexity, and then the edge density or the amount of detail in an image. And those were in the mid region. So you need a little bit, and this is what we found also in the opposing research, is that you need enough to kind of engage the senses to capture the eye. But it can't be too much that it overwhelms the brain in terms of processing and the way that we want to, the brain kind of decides whether or not we're going to look at it or not. See, that's interesting because when you say mid, that surprised me

because in sort of my own brain, having been in the ad business for almost 30 years now, the things that I resonate with from an advertiser creative type point of view are arresting images, almost know, almost jarring. These days, we sometimes we refer to it as thumb stopping. And those tend to be images not sort of in the middle. They tend to be visually either quite, quite stark or lots of blacks or lots of high contrast. But you're saying that those don't perform as well. You are certainly right.

There's there's first of all, there's personal preferences like there always is. And we try to kind of capture across the whole space. But when you look at these kind of stark differences, that does mean that there might be a lot of detail in the middle of the picture, but then a little bit less detail around it. Or there might be a lot of color in the focus area and a little bit less around it. And we actually also controlled for some kind of these photography elements. And even after we added all those elements, these color complexity or the feature complexity in general, the optimal state's still in the middle. So I think we're still on the same page here in the middle so i think we're we're still on on the

same page here um in terms of expectations yeah okay so that's feature complexity looking at the at the individual pixels something that like a photoshop nerd would be interested in let's talk about the art director's brain here what did you find was the optimum level of design complexity so there we actually find the the opposite, where when we talk about the feature complexity, we say an inverted U-shape. So there's an optimal point in the middle. Now we have an area that's kind of like a canyon, right, where the tops are on both ends of the spectrum. And there we see that it's either a single object or a very symmetrical and regular arrangement of objects within an image. So there is a clear focus, if you will, and a clear

story that there is to tell. Or we go to the other end of the spectrum where there's a lot of variation and a lot of different objects and perhaps a lot of creativity, right? And those are the two optimals there, where the middle is kind of misses the mark a little bit, where it's like, it's not necessarily clear what the story is about, but it also doesn't have the engaging or the gluing qualities of something very creative. Did you find any ideal positioning or representation of a human face in these high-performing images? So we did not look at where the face was located specifically. We did find we added an indicator of whether a face or not was in there. And we know from previous research that phases are actually engaging, but across all the brands that we had and all the images that we looked at, we actually

found a slight negative impact on the engagement. But this was very minor. So that could have to do a little bit with the distribution across brands. So I would be cautious with interpreting that result. And it was not the main focus of our study either. Right. And, you know, those brands that you mentioned, there were 633, I think of them, and they were big brands, right? Do you think that there would be a difference in how images from small businesses might perform? Yes. So I think it will be wise for any brand using these kind of metrics and using this framework to figure out what the optimum is for their brands and for their audience. And you know that very well is that you need to study your audience and you need to figure out what works best for you. And that can be a certain color palette.

It can be a very coherent story. So you need to find the optimum there. We focused on very broadly identifying a general kind of statistic that works well. But obviously, there are nuances for big brands versus small brands that we have not looked into specifically, but that we definitely added into the recommendation section of our paper to look at specifically. The data set that you got from Instagram were images from 2015 and 2016. Do you think there's a chance that your findings would be different if you'd have studied more recent images? I would like to say no, but I am not. I'm obviously not sure.

And it's something that as a field in the academics of marketing, we struggle with a little bit. Sometimes these review processes for the papers are a little bit lengthy. So then once a paper comes out, you're like, well, the type of content got a lot more dynamic over time. Whereas when we studied it, it was one single image and that was it. Now we have carousels, we have reels, we have stories, we have videos. So it would be interesting to see what kind of effects hold there.

And because we have all those type of content mixed in with our images, it would be interesting to see what that does with the visual complexity. But we would like to hope, or at least we hope, that it stays like that throughout, right? What made you want to study this? So that's a great question, actually. So when I started, this started out as a project for my master's thesis. And really what it came from was me coming from a fairly technical background, hence the algorithms, right, to see if we could somehow predict the number of likes on Instagram. And we found that we could actually use these computer vision type models, some deep learning to make predictions about whether or not picture A would do better than picture B. And I've explored that into a kind of almost like a product or an app for A-B testing that we can just say, you should go for picture A or picture B. And then, but that from a marketing, is not necessarily super interesting because it

doesn't tell us why. And as a creative, or in general, as marketers, we want to know, well, why is picture A better than picture B? And that's when we started looking for these frameworks. And we came across these opposing views of visual complexity, which we thought we could tap into. And then we found a stream of computer science literature that already studied how can we measure visual complexity. So those two combined formed a perfect pathway to an interesting study. I want to put you on the spot, if I may, with all of these findings in mind. If you were to design the perfect Instagram image for a brand to use, what would that image look like? Could you specify what the brand or what kind of setting we're looking at? That gives me a little bit more to work with.

Sure. Let's imagine that it's a small business, maybe two or three employees, and they sell candles. They sell candles. I'm just pulling this out of the air, but let's just say candles as an example. So in this case, the feature complexity is in that sense pretty simple because a candle and the lighted candle or their fire, those are not too high in terms of detail or in terms of colors. So it wouldn't be too difficult to make that candle very focused and very bright and the rest of the surroundings a little bit less bright so that we have this kind of optimal region of feature complexity. And then because we're looking at a specific candle, I think it makes most sense to be on the simple end of the spectrum for design complexity, where we really take this angle or the candle as the main focus in a very symmetrical display so that our customers know that this is our product and this is what we're talking about. What about a B2B type business? So not an organization that sells a product,

but maybe a large company that is trying to attract new employees? Now that gets a lot more difficult. For the feature complexity, the recommendation stays the same. It stays where you would take a picture and the feature complexity arises, and then you can determine on what Photoshop or with a simple filter to get it more towards the optimum. But then for the design complexity, that's a tough question because we will have to think about what is going to be what we want to do something with an experience of perhaps the service or a happy customer, right? Something along those lines. But this is me kind of perhaps getting a little too creative or not creative enough. That's okay. We only have another 712 more industry examples to get through, and then we'll be... With all of that said, is the image the biggest driver of likes, or are there other

factors like the account size or something that is more responsible for driving engagement? Yeah, so the biggest factor by far is the number of followers. And the number of followers drives the size of how many people can view it at the first instance. And then obviously, there are a lot of different types of content that go viral for some reason, right? But very often, that is kind of like an odd bird out or like a weird reason. But generally, if I post the same picture as Kim Kardashian, right, chances are that Kim Kardashian will get a couple more likes than I do. Well, she'll get more net likes, but will she have a higher engagement rate, like a per capita kind of measurement? Yeah, that is a great question. And then probably she will have less of an engagement rate because what we see with people with less followers, where the followers are more closely related to the person, you see higher engagement rates, right? And then there's a lot of other factors like

when was it posted? I think Sprout Social is working or did a piece on when to post recently. There's a lot of other factors. And then obviously the image is an element. And because visual content is the social object that drives Instagram, it is important. But there is other contextual variables that matter more in that sense. Well, it's very interesting research. I think that social media content managers, especially people who are getting into the business for the first time, often just need a guiding light to sort of start them off. And I think this did a great job of helping people find

that. So thank you so much for your time. Yeah, thank you for having me. That was a great discussion and great questions as well. Hi, Silver Gore is the Assistant Professor of Marketing at the Rochester Institute of Technology. So there you have it. That's the kind of thing you'll hear on our weekend editions, which are only available on our premium feed. Tap the link in the show notes or go to todayindigital.com slash premium feed for information. Today in Digital Marketing is produced by EngageQ Digital on the traditional territories of the Stunemic First Nation on Vancouver Island.

Scripting and promotional support by Steph Gunn. Podcast music licensing by Source Audio. And every now and then, our theme composer Mark Blevis gets a little bit nervous that the best of all the years have gone by. Every now and then, he gets a little bit terrified. And then he sees the look in your eyes. I'm Todd Maffin.

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