Beyond Share of Voice: What Reddit Reveals About Brands and Decision-Making
By YouScan
- article
- AI
- Social Media Listening/Intelligence
Reddit is where people often say what they think unfiltered. People go there to share opinions and ask for help, and the answers can be raw and detailed. In categories like beauty, Reddit also functions as a large-scale “what should I buy” forum where people swap recommendations and compare products in a way that many users trust.
For anyone doing social listening, the practical implication is simple. If you are not including Reddit, you may miss some of the most honest conversations in your category.
This article covers part of the webinar “What If Your Best Actionable Insights Are Hiding on Reddit?”, which was part of The Next Generation Insights Summit held in April of 2026. Rewatch the entire webinar here:
What If Your Best Actionable Insights Are Hiding on Reddit?
The Context for Our Use Case
In the session, we combined two perspectives.
Anna introduced YouScan as an AI-powered social listening platform that can monitor and analyse data from forums, blogs, review sites, social networks, and news sources. Instead of adding more dashboards, the solution reduces manual sorting, so more time can go into insight generation, brainstorming, and strategy work. A key capability for the analysis we shared is the ability to work across text, visual content, and audience characteristics.
Alex then walked through a concrete case study using beauty conversations on Reddit. The purpose of the example was to show how you can move from a large corpus of posts and comments to a set of practical takeaways, and how those takeaways can change once you segment by community rather than only looking at an overall number.
What We Analysed
The analysis focused on beauty discussions across the top 55 beauty subreddits. In the dataset Alex reviewed, there were just under 9,000 posts and more than 60,000 comments, totalling roughly 71,000 mentions.
At a headline level, the overall tone of conversation was mostly neutral or positive, with a net neutral and positive score of just under 83%. That framing matters because, particularly on Reddit, a neutral mention is often still a signal of relevance and consideration even if it is not explicitly praise.
Alex also highlighted that one brand, NARS, was the most mentioned in terms of raw volume, while another, Saie, had the highest net neutral and positive score among the brands reviewed. That contrast set up one of the main points of the session.
Three Takeaways for Social Listening Work
Alex shared three takeaways that are useful as a checklist for anyone doing social listening work.
1. Share of voice is not share of love
Large numbers are not automatically “good”. When you report share of voice, you still need to check whether the conversation is favourable and what’s driving it. In Alex’s analysis, the brand with the most mentions also had the lowest net neutral and positive score among the brands highlighted.
Alex also tested the idea that volume and sentiment might move together. When he expanded the analysis to brands mentioned at least 30 times, he found a mild anti-correlation between volume and sentiment. He suggested three plausible mechanisms in the dataset:
- Volume dilution, where higher mention volume brings a broader distribution of opinions, including detractors.
- Category composition, because beauty conversations are often highly passionate, and that passion includes negative experiences.
- An underdog narrative, where smaller indie brands can be treated more favourably.
The point is not the precise statistics. The point is that you should avoid drawing conclusions from volume alone.
2. A loud minority can drive a lot of the conversation
Social listening is selective. It reflects a subset of people who both have an experience and choose to write about it. In this dataset, Alex highlighted a power-law pattern: a tiny core of prolific posters drove a large share of the content, while most authors posted only once.
In practical terms, that means you may be able to learn a great deal by studying a small number of influential contributors. Alex framed this as moving away from a hub-and-spoke mental model of social media towards something closer to a broadcast tower. Even if that loud minority isn’t representative of the general population, the general population can still be influenced by what those people say.
3. The word “natural” means many things
In the dataset, 3.7% of the corpus, just under 2,600 mentions, included the word “natural”. Alex’s point was that “natural” doesn’t have just one meaning. It can refer to natural lighting, a natural bridge, a natural finish, or a natural look, and each of those is different.
If you’re planning messaging that uses “natural”, you need to be clear which lane you are in and avoid blending meanings in a way that reads as generic corporate language. This isn’t only a brand positioning issue, it’s also a measurement issue because if you treat every “natural” mention as the same, your analysis will be muddied.
Conversation Themes
Alex also built a composite view based on a set of themes he identified, and several macro forces showed up across the sample.
Economic pressure, value and the desire to make products last
Macroeconomic pressure came through as people talked about price and value. Alex pointed to “dupe culture” accelerating and spilling into larger beauty communities. A related idea appeared in “no buy” and “project pan” conversations, where people try to use products down to the very end.
Alongside that, there was a repeated tension around wear. People want products to last, especially if they have paid good money, and they also want those products to be easy to remove. Alex noted that this came up in discussions among people in demanding professions, such as nursing and teaching.
Reformulation, discontinuation and frustration
Reformulation was rarely described as a winner. When someone finds a product that works for their skin tone or routine, a change can feel like losing something important. Alex used foundation undertones as a concrete example and described how an unwanted shift, such as moving more orange, can generate a strong reaction.
Alongside reformulation, there were two related patterns:
- Formula frustration, where people feel a brand has changed something that was “near and dear” to them.
- Discontinuation panic, where people worry a product will no longer be available, whether due to limited runs or collaborations ending.
For research teams, these themes matter because they often signal both a change in the product experience and a shift in customer anxiety. They can also show up before they become visible in more traditional feedback channels.
Shade and undertone as a persistent white space
Alex also highlighted “undertone specific demand” and the idea that some audiences feel underserved. He pointed to the OliveMUA community as a particularly telling signal in this dataset. It’s a smaller subreddit than the largest beauty communities, yet it ranked highly in mentions of the brands analysed. In Alex’s framing, niche communities can over-index on signal because they are more tightly focused on a specific need.
The broader research implication is that segmentation by community can help you find unmet needs, even when the absolute community size is not large.
Viral moments and purchasing intent
Even though highly upvoted posts weren’t common in the dataset, Alex still saw viral events act as strong drivers of purchasing intent. The practical point is that you can study “spikes” and what triggered them, but you shouldn’t assume that the average engagement level is driven by a small number of viral outliers.
Why Community Segmentation Matters
One of the more operational parts of the session was the emphasis on subreddit-level nuance.
Alex noted that large communities such as r/Sephora and MakeupAddiction carried a high share of conversation volume for certain brands. At the same time, different communities had different “champion brands”. A brand can look weak or strong depending on where you look, and the reasons will vary by audience and by context of use.
That’s why a single overall chart can be misleading. If you only report a brand’s overall mentions and overall sentiment, you can miss the communities where the story is more nuanced and where the insight is more actionable.
Where to Look for Shopping Intent and Decision Drivers
Alex also called out head-to-head comparisons as a particularly rich area for insight. Threads that compare one brand to another tend to contain direct decision criteria and explicit trade-offs. He referenced examples such as Maybelline versus NYX, MAC versus Fenty Beauty, NARS versus Saie, and e.l.f. versus Maybelline.
For research and insight teams, this style of discussion is useful because it makes the evaluation framework visible. It also tends to remain relevant over time, affecting purchasing intent weeks, months, and sometimes years later.
Practical Ways to Decide What to Monitor
In the Q&A, Cynthia asked a question many research teams recognise: how do you decide what to focus on when you can’t ingest “the entire internet” and when there is a risk of monitoring either terms that are too broad to be useful or terms that feel small until they spike?
Alex’s answer centred on iteration. Start with the best available query, review what you are capturing, and adjust as needed. The intent is to protect the signal-to-noise ratio and manage volume constraints.
Both Alex and Anna also discussed approaches that help you move beyond Boolean query logic alone, such as:
- Working with specific subreddits can help contain noise, especially when brand terms have other meanings in unrelated communities.
- Once you have the mentions, using filters to slice the data, such as questions, recommendations, and complaints, can help you focus the analysis.
- Combining those slices with thematic segmentation can give you a more pointed starting set for sentiment and semantic analysis.
The overall lesson is that the monitoring plan is not a one-off setup step. It’s part of the analysis itself and improves as you learn which communities and themes reliably produce insight.
Closing Thought
Reddit can be an unconventional source for teams who are used to focusing on more brand-led platforms. The session’s message was not that Reddit replaces other sources, but that it adds a distinct kind of signal: unfiltered, community-structured conversations where people compare options, share lived experience, and negotiate what words such as “natural” actually mean in context.
If you treat those conversations with the same rigour you bring to other data sources, and if you segment by community rather than relying on a single top-line number, you can get to practical, defensible insights.
This article covers part of the webinar “What If Your Best Actionable Insights Are Hiding on Reddit?”, which was part of The Next Generation Insights Summit held in April of 2026. Rewatch the entire webinar here:
