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[Guide] The Social Media Management Maturity Model

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Guide

How to Use Reddit for Product & Consumer Research

Learn how to use Reddit data to inform your competitive intelligence, customer experience, audience analysis, and influencer research activities.

GuideHow to Use Reddit for Product & Consumer Research
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The value of Reddit data

With hundreds of millions of monthly active users, Reddit is ranked among the most popular websites in the world.

Reddit has over 50 million daily active users who spend an average of 15 minutes a month on Reddit actively posting and engaging in conversations.

That’s 750 million minutes of conversation where people discuss topics in depth. More businesses want to understand where their target audiences spend time, and it’s become evident that Reddit is one of the top platforms where they can learn more about their customers. 

Reddit has also developed a reputation for being the place where trends begin. Before the latest rumors, memes, or social movements hit the mainstream media, they’ve already been circulating on social. But before they reached the “big” social networks, they were most likely already being discussed on Reddit.

Reddit is different from other popular social networks because its users tend to have their finger on the pulse. It’s “home to thousands of communities, endless conversation, and authentic human connection.” In this guide, we explore how the unique qualities of Reddit make it an ideal data source for product and consumer research. 

Let’s take you through one of the most insightful consumer forums in the world and help you master your Reddit analytics skills, unlocking a rich new source of consumer intelligence to inform your product development and marketing.

Brandwatch & Reddit

Brandwatch has also entered into Reddit’s Official Partner program, providing broad access to the most insightful data, trends, and insights from the most compelling subreddits across the Reddit platform. 

Boasting more than 100,000 communities dedicated to interests, hobbies, brands, or even specific products, Reddit provides Brandwatch clients with the ability to listen and learn from some of the internet’s most spirited and fast-moving discussions. 

This partnership is another step in Brandwatch’s dedication and commitment to empowering our clients to move at the speed of social, enabling higher accuracy levels.

What we’ve built with Reddit:

  • Full coverage of all public subreddits
  • Historical data as far back as 2011 
  • Reddit-specific query operators to search for content in specific subreddits or engagement with specific topics, threads, and authors
  • Charting and filtering with a wide range of metadata, including Reddit Score, Subreddits, Subscribers, Votes, and Karma
  • A subreddit component that allows you to see where topics and brands are being discussed at a glance
  • Audience Analysis features, which include rich author metadata and, coming soon, the ability to create social panels of these authors to deep dive into custom and pre-built audiences like generational and interest groups

Audience analysis

There’s barely a hobby, topic, or interest that doesn’t have a subreddit dedicated to it. And even then there are usually even more granular subreddits within them too.

This offers a unique opportunity for product and consumer research. People have organically formed themselves into active and communicative groups with a shared interest. That means you can take the members of one set of subreddits, look at the other subreddits they’re active in, and get an understanding of the kind of people they are.

Let’s take fishing as an example. To start we’ll collate a list of fishing-related subreddits to find our active Reddit fishing community. You’ll see what we mean by granular subreddits.

  • Fishing
  • Flyfishing
  • BassFishing
  • Fishing_Gear
  • KayakFishing
  • Carpfishing
  • TroutFishing
  • Microfishing
  • Icefishing
  • Surffishing
  • FishingForBeginners
  • Fishingmemes
  • Tacklebox
  • SaltwaterFishing
  • Bowfishing
  • Offshorefishing

While we’ll be looking at active members across all the subreddits at once, you can see how these more niche subreddits offer an easy way to analyze the fishing enthusiasts by type without manual segmentation.

The process

We start by creating a query that tracks all posts to the aforementioned subreddits. For 2022, we found 878.3k mentions. But it’s the nearly 98.66k unique authors we want. This is a valuable group who have shown without a shadow of a doubt that fishing is of interest to them.

We want to refine this group, though. For this example, we want to look at the hardcore fishing enthusiasts. That means we want authors who have posted regularly over the year rather than just once or twice.

This is an easy job with the top authors component in Consumer Research. We took the 250 most active users (who all posted at least 150 times in fishing subreddits in 2022), uploaded this list, created a Social Panel of them, and then ran a query to see all of their activity on Reddit.

Now we can use the top subreddit component to understand where these authors spend their time and what their interests are.

We can learn a lot from these subreddits our authors are active in.

  1. We can see the most popular forms of fishing and discover new important subreddits we didn’t know about. For example, this group of fishing influencers post a lot in bass fishing and fly fishing subreddits. The second aspect is the ability to look at their other interests.
  2. For our fishing posters, we found that news, politics, stocks, and investing were quite popular, with martial arts and firearms subreddits also showing up in the data. 

To look deeper at the topics these redditors talk about, we can study these conversations using the word cloud.

And if we wanted to explore specific topics, brands, or products, we could filter or break down these conversations to understand how our audience discusses them.

In this example, we’ve studied a small niche group. It is possible to run this kind of persona research on much larger audiences as well. You can build panels of people with common interests – or you can create even more specific panels of people based on comments they’ve made in the past.

For example – by searching for people who have said they “have bought / own / are looking to buy” a “truck” – we can create a whole panel of “truck drivers” and then explore how this target audience talks about other topics. Case in point, here is what 30k truck-driving redditors talk about in relation to electric vehicles.

And we can compare their attributes to key subtopics within this conversation with how the public generally talks about them.

The options are endless, so you can get creative. And soon, you will have even more ‘ready-to-use’ social panels for Reddit, built by Brandwatch experts. This will include generational panels such as baby boomers and millennials, which are identified by our data scientists by studying their previous conversations on Reddit.

Competitor research

To explore using Reddit for competitor research we’re going to look at a topic where a lot of different brands are talked about: skincare.

Skincare is one of the biggest consumer topics on Reddit, with dedicated subreddits such as r/SkincareAddiction attracting millions of members who share advice and ask for recommendations.

Using Brandwatch Consumer Research, we built a query to capture all the conversations about skincare products going on in these varied subreddits.

Exploring a topic

At first glance we can see the nature of these conversations. The community talks a lot about different skincare products such as cleansers, moisturizers, and sunscreens.

The top topic is ‘routine’. Redditors like to share their own skincare routines and ask for recommendations, and they will often talk about specific products to incorporate. We can dive into this topic by clicking on it and selecting the topics view to quickly understand the context of this specific theme.

Here we see what people talk about when discussing skincare routines:

  • Specific ingredients and products as part of the routine.
  • In particular, some of the most common ingredients discussed are hyaluronic acid (for hydrating skin) and alicylic acid (a cleanser for acne).
  • Routines for different skin types are also popular topics - in particular for dry, oily, or sensitive skin.

Going back to our broad search – a useful way of exploring topics early on is to change the type of topics in the Word Cloud. 

Looking at phrases instead of keywords, for example, gives us a different view:

  • A lot of people discuss ‘skincare products’ and look for and share ‘product recommendations’.
  • There are conversations about specific brands or products, but more about specific ingredients.

Exploring subreddits

Exploring the top subreddits can tell us a bit more about the makeup of this community.

  • The majority of conversation is in the highly popular r/SkincareAddiction subreddit.
  • This is followed by region-specific subreddits including several in Asia.
  • There are also high volumes of posts in the r/30PlusSkinCare subreddit.

Looking at the most talked about brands in these subreddits can instantly tell us more about which brands and products are being talked about or recommended by different demographics on Reddit.

Analyzing competitor themes 

You can compare the context in which your brand and competitors are being talked about to understand how your products are being discussed differently by consumers on Reddit. 

For example, using keyword rules you can do a simple comparison of conversations mentioning ‘dry’, ‘sensitive’, or ‘oily’ skin or ‘acne’ to see whether one particular brand is being recommended for particular skin types.

For this analysis we’ve made two sets of categories: one for ‘skin types’ and one for brands. With these two sets of categories we can use the chart component and select one as the X-axis and one as the breakdown.

Now that we have these categories set up we can continue to explore further. For example, by comparing how these brands are talked about in different subreddits and combining sentiment and topic analysis.

By understanding exactly how consumers in different demographics (and with different skin types) feel about your products and those of your competitors, you can build a more detailed picture of how you should be positioning yourself or developing new products to better compete in your target markets.

Customer experience and support

To show how brands can analyze customer experience using Reddit data, let's look at digital cameras as an example.

The dedicated photography communities on Reddit come together to provide support and advice for each other. These are also a fantastic source of information for brands that want to better understand the issues their customers want to solve in order to provide a better after-sales experience.

We searched for conversations about digital cameras across a list of relevant subreddits. Then we tried adding some keywords and phrases such as ‘problem’ or ‘how do I’ to focus on conversations where people were having trouble with their cameras post-purchase.

Exploring a topic

The topic cloud gives us a good, broad starting point and we can see some key themes already. And as we’d expect, the tone is generally negative.

But when we explore the data we soon find a lot of noise which we want to remove. The keyword ‘issues’, for example, has picked up people selling cameras with ‘no issues’.

We’ve got two challenges now.

First, we want to filter out all the irrelevant posts like this. We could try and do this by exploring more and making edits to our query. But this could take a long time and if we remove the phrase ‘no issues’ we risk missing posts like this one where a customer had “no issues” at first but then experienced a problem later on.

Reddit is a forum and many of the posts we’re looking for are quite long, going into detail about several different things as the user explains their problem. Adding exclusions to our query might not be the best option.

Our solution to this is going to help us tackle our second challenge as well: How can we break down this data into some meaningful categories for us to analyze? We’re going to use a Custom Classifier. 

Using Custom Classifiers

This is Brandwatch’s machine learning tool that we can train to analyze the text for us, remove irrelevant posts, and categorize the others into themes all at the same time.

We can quickly look through a random sample of posts and begin dragging and dropping posts about selling cameras or asking for recommendations etc into an ‘Irrelevant’ bucket.

Then, as we see relevant posts we can start dropping those into new buckets which we can give a thematic name. A good approach to this is to start with no preconceptions about what kinds of issues people have and see what the data tells you.

For example, with this project we found several posts about battery problems so at first we made a category called ‘batteries’. But as we continued we found different issues with things like broken lens caps, dust collection, faulty buttons, and water resistance. We made categories for each until we reached a point where it was clear these could all be grouped together as ‘Hardware’ issues, as opposed to the ‘Software’ issues we were seeing in other posts.

It’s quick and easy to consolidate all the training posts in a group of categories and very soon we had sorted our categories into an exhaustive and mutually exclusive set. This is important because Custom Classifiers will put every post into one of the categories it thinks is most relevant.

Analyzing custom categories

Now we can take a look at our categories and see what we can learn about the different types of issues camera owners have.

Showing the top topics allows us to do some qualitative research into where the most common issues are coming up. For example, in our ‘Software’ category, a lot of people are struggling to import and work with their pictures in photo editing software or losing picture quality in their videos when attempting to upload to YouTube. ‘Hardware’ issues are common with the camera lens and shutter. And people struggle to find and work with compatible 3rd party lenses for their cameras.

We can also do some quantitative research into these issues. For example, to understand which are more common when the author mentions a specific camera brand. From this analysis we could deduce that camera owners of Brand 1 would benefit from more readily available information and customer support in general while Brand 5 should focus on helping customers with software issues.

Influencer discovery

Identifying influencers is a tricky task. Discovering the right ones, especially for more niche interests, is not always easy, and then there’s the matter of ensuring their metrics are accurate when it comes to engagement and reach.

Using Reddit’s voting system, we can easily identify authors in any community who are genuinely influential. If someone’s comments and posts regularly score well, they’re obviously held in high esteem by other users, with their opinions, thoughts, and advice holding weight.

With this information you can not only approach them for the usual influencer marketing activities, but you can see what they’re talking about in real time to understand what trends may be coming, and get a better understanding of what your audience finds interesting, useful, and informative.

The process

First you need your list of subreddits. We’re going to use home baking as our example for this section, so we’re looking at:

  • Baking
  • Bakingrecipes
  • Sourdough
  • 52WeeksofBaking
  • Breadit
  • ArtisanBread
  • VeganBaking
  • bakingnoobs
  • AskBaking

The first few steps are the same as they were for our previous section on identifying personas. After creating a query tracking your chosen subreddits, we want to look at the more active users. You can choose any number you like, but for this example we went with the top 250 again. And, as before, we created a Social Panel made up of these users.

Next we want to find out which of these users received the most upvotes. To do this, we create a query using our Social Panel, but also only collect mentions from our home baking subreddits. This means we’re only getting data on the votes they received in relevant communities.

Once the query has run we now need to export the data in a CSV through the Data Download tool. When you have that opened you can go ahead and delete all the columns except the author, query ID (which we’ll use to tally the number of mentions), and Reddit score ones.

Select all of the data and then create a pivot table that shows how many mentions each author created, and their total Reddit score for these mentions. 

Now create a new column that divides the Reddit score by the number of mentions.

That leaves you with a dataset that offers two views. One a list of authors ranked by their overall Reddit score, and another ranked by their average Reddit score per post. 

The second is likely the more robust one as it will discount authors who had one or two incredibly successful posts and little else. But don’t throw out the first just yet. We recommend keeping it to look through because, while it may not give you any influencers, it’ll give insight into the types of content that work well on Reddit. This offers value for your influencer, content, and audience strategies.

Ultimately you now have a list of the most influential Reddit users for your topic, whether you pick out 10 or 100. What you do with them will depend on your strategy. 

We recommend creating a query combining these users and your chosen subreddits and analyzing the mentions. Not only will you quickly be able to identify which influencers are worth working with, you’ll get some insights into how they’re influencing the community right now. For example, over the last two weeks, our top influencers have been moving away from bread and focusing more on sweet and sugary recipes, a sign that the home baking community will soon do so too.

Now it's your turn

And there you have it, four ways to turn Reddit into a veritable data goldmine. Let us know how you get on in the Brandwatch Community.

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