Social Media
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SOCIAL MEDIA STRATEGY
Marketers can now create the perfect Instagram photo, researchers say
By our News Team | 2022
Using social media can, at times, seem hit or miss for marketers. New research suggests there’s a formula for creating photos consumers love.
Instagram has become a must-have for many marketing strategies. But what is it that makes a photo stand out and resonate with the social media platform’s huge audience? Researchers think they may know.
“We are increasingly able to determine whether images included in social media messages are likely to garner interest from consumers,” says William Rand, an Associate Professor of Marketing at North Carolina State University in the US, and co-author of an academic paper on the subject.
“But a lot of the variables that we know affect public interest have very little to do with the images themselves. For example, a brand’s strength, and the number of followers it has on Instagram, is the strongest predictor of whether consumers will engage with an image. The text accompanying an image is also important.”
Photo by USA-Reiseblogger on Pixabay
He continues: “We wanted to look at the role the actual image plays, focusing specifically on how the complexity of an image drives consumer engagement. This is important information for the marketing community, because it can inform decisions about what sort of images to use [for] building a brand.”
Previous research found there are two aspects of an image that people respond to: feature complexity and design complexity. Essentially, feature complexity refers to fundamental characteristics such as colour and brightness. Design complexity refers to the actual elements found in an image and how they are arranged.
To begin their analysis of how viewers respond to image complexity, the academics identified six measures that can be used to assess various aspects of image complexity.
This included the distribution of colour and luminance in the image, the number of edges and objects within the image, and regularity of objects – which is determined by whether objects share an orientation and the extent to which they overlap. The final factor is how symmetrical the arrangement of objects is.
Scores for each of the six identified measures
The research team created a computer program to scan images and automatically generate scores for each of the six measures. They also ran a validation experiment to ensure that the image assessment program was consistent with how humans perceive complexity in images.
From this, they created a model to determine which combination of measures was most closely associated with generating ‘likes’ on Instagram. The model accounted for confounding variables, such as the number of followers a given Instagram feed has.
“We found that all six measures are important, but there were particular patterns in which images generated the most positive feedback,” Rand explains.
When it comes to feature complexity, the study found that there is a sweet spot right in the middle. Consumers preferred images with some diversity of light and colour, but not too much or too little. The opposite is true for design complexity. People preferred images that were either very simple or highly complex.
“In practical terms, we found that you could improve the number of ‘likes’ of any given image by about 3% if you applied the appropriate filter to address issues related to feature complexity,” Rand says. “That’s nothing to sneeze at, particularly since applying a filter only takes a few seconds. What’s more, our model suggests that optimising both feature and design complexity could improve consumer engagement by about 19% – after accounting for variables such as an account’s total number of followers.
“We’re putting this out there with the idea that it can be used to inform decisions made by design professionals in the marketing sector. But we’ve made the raw code for the model available. It’s not in a user-friendly format right now, but I’m sure the right tech-savvy people could use it to create a valuable tool for the industry.”
The paper was co-authored by Gijs Overgoor of the Rochester Institute of Technology; Willemijn van Dolen of the University of Amsterdam; and Masoud Mazloom of the Ferdowsi University of Mashhad.
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