Brands invest significant time and money on Instagram, trying to maximize consumer interactions with their content. But while they can use Instagram’s standard analytics and third-party data to study the effectiveness of post timing and promotional spend, it’s harder to find answers to the most important question: what kinds of images generate the most engagement?
Methodology: Working with data partner Unmetric, L2 processed over 40,000 Instagram posts from 15 beauty brands. We captured:
- Spectrum, segmented by 25 colors and shades
- 450 different objects or environments
- Time and date of posting
- Engagement rate (calculated as interactions divided by follower count)
- Three primary types of imagery
- Presence of logo
- Presence of text
- Presence of human face
Note: Based on a randomized QA process, we believe that this analysis produces correct classification in 71% of cases.
To test a range of strategies, we analyzed brands that varied in price point, product offering, and digital sophistication. Findings, therefore, are not necessarily representative of the beauty category overall.
- Brighter colors tend to drive higher levels of engagement.
- Brands with higher levels of palette discipline (i.e. they stick to a consistent palette) also tend to see more engagement.
- Text and logos within images tend to drive higher levels of engagement, while human faces appear to impact engagement negatively.
- Seasonal and product-related imagery tends to have higher engagement rates.
Looking at all posts across the 15 beauty accounts, a few key trends emerge. First, brighter colors tend to be most impactful in driving engagement. Using color saturations as independent variables and engagement as the dependent, the “driver” of engagement represents the normalized betas from a linear regression. Among the brands investigated, bright red, bright pink, bright magenta, bright yellow, and bright blue drove engagement at the highest levels. However, many brands focus their Instagram color palettes around shades that do not drive high engagement levels. For example, the analyzed beauty brands frequently used dark red, orange, and dark blue, even though these colors typically result in low engagement.
Inclusion of text or logos in the image produced significantly positive results in post engagement. The presence of models in an image, however, produced negative results, which we believe may be a category-specific issue. The vast majority of Instagram posts in the beauty category have close-up images of human faces, so these posts may simply blend together.
To analyze a broader range of image types, we compared post engagement rates with a specific image subject and the standard deviation of those engagement rates with the image being analyzed. This allowed us to assess not only whether specific images are related to higher engagement but also how consistently they are related (a lower standard deviation representing greater consistency). Since most posts include imagery of multiple subjects in one frame (i.e. one post can include both eyeshadow and eyelashes), we focused on the interactions between two or more image types.
The data set includes over a year’s worth of posts. Interestingly, those related to winter and holidays did well in terms of driving engagement, and posts that included a home or other type of residential property performed best. This may be a relic of the category, as the beauty sector sees a consistent uptick in sales in the run-up to the holidays and marketing collateral likely follows suit.
Next steps: We believe this initial exercise shows promising capabilities for deeper analysis within a category. As we continue to experiment, we will specifically look to:
- Incorporate post frequency and hashtag text analysis
- Create a scorecard for competitive benchmarking
- Dive deeper into outlier analysis
- Expand brand and category list
- Develop an optimization model