Exploring Camping and Hiking Brand Reddits

If you are a camper or a hiker, you need to get the lowdown on the best gear. Your life may depend on it. Reddit (opens in a new tab) has a lot of great community discussions about camping and hiking brands. The r/CampingandHiking (opens in a new tab) subreddit can answer a lot of questions. Specific brands like Arc'teryx (opens in a new tab) have subreddits dedicated to their products.

Let's see how we can use Teleoscope to make sense of the thousands of documents that are in those subreddits. Using an old data dump, we have 8234 posts about Arc'teryx and 32089 posts in the general r/CampingandHiking subreddit. That's about 40k posts to go through. Teleoscope can make that a breeze.

Getting our data prepared

Finding usable data

Teleoscope is designed to work with any text data that is between one sentence and a few paragraphs. Right now, the model only works with English and Chinese, but we will be updating to include more languages soon. For now, data needs to be in a CSV file to upload to Teleoscope. You can export from an Excel document. Many other apps also work directly with CSV documents, so see if there's a CSV export option from whatever data app you're using.

All that Teleoscope needs at a minium is a text and title field. All the other metatdata is optional.

Uploading data to Teleoscope

Once you have your data prepared, you can upload it to Teleoscope by going to the sidebar, clicking on Data and then Open CSV Uploader. A window will pop up and allow you to browse for your file from your computer. Once you have selected your file, a preview of your data will open in the same upload popup.

You can then choose which columms from your CSV will be used for the text and title. The uploader will select anything called "text" or "title" by default, but you can choose any of your columns, no matter what they are named.

Other options for metadata will automatically be selected as well, but they are not required. For example, the dataset below has a column called metadata.id and that has been automatically chosen as a Unique ID column. It's not required, but can be helpful later when sorting through your data.

Developing our curiosity

Browsing Data

Now that we have our data uploaded, we can start to browse through to get an intuition for what might be interesting.

The first post is a post from a moderator. The preview of the post is on the sidebar. We can skim to see that this looks something like a recurring post that probably doesn't give us very much information about the brand itself. We can bookmark it for later, or just ignore it.

We can use the up ⬆️ or down ⬇️ arrows on our keyboard to quickly navigate through any list. As the document is highlighted, the preview shows up in the sidebar. If there's anything interesting, we can bookmark with Enter or Return on the keyboard. From a quick read, it looks like the first few posts are about needs of police officers, repairs for burn holes, and care instructions for a jacket. Let's bookmark the post about police officers, then keep skimming.

After a minute or two of browsing, we can start to get an idea of initial themes that might be worth exploring. Without too much thinking or theorizing, we can see that a lot of posts are about durability, style, weather-appropriateness, comfort, and a variety of specific use cases, like our police officer.

This gives us enough to start thinking about potential areas for further exploration. Each of these documents could be the nucleus of a new idea, theme, or search. Our intuition about this dataset could lead us to believe that the highest-value posts are going to be about brand threats and brand opportunities. Let's start getting more specific.

Making a theme for broad categories

Let's focus on broad categories first. The theme of durability seems to be present in the first few posts. Let's bookmark everything that has to do with durability. After about two minutes of browsing, we have a small sample of durability-related posts in our bookmarks. Let's make a group from them.

You can turn any set of bookmarks into a named group.

The point of creating this small sample of documents is that we can now use the Rank to find the most similar documents. Let's connect the group to a Rank. Now all of the documents from the whole dataset are ordered from most to least similar in our Rank. We can browse again to see what themes might be popping up when the broad concept of durability is our focus.

Using keyword Search for concrete categories

After skimming for less than a minute again, we can see that "delamination" and "wetting out" are two common phrases that come up when people talk about durability issues. To make sure that we look at every single instance where someone talks about "delamination", we can use a keyword Search.

Now we have a concrete category of delamination-related issues because of a keyword. That's not the only durability issue, so we should keep on browsing and categorizing to develop deeper themes.

Already getting reportable and analyzable data

This is one possible stopping point for our Teleoscope thematic data exploration. As we can see from the search, there are 107 possible documents that we may want to drill down on. That could include using Teleoscope to drill down further, exporting into a different software, or simply taking notes on all of the issues that customers face with delamination. If we wanted to report to the quality assurance department that delamination is a problem, we would have concrete data points to pass along to our engineers and designers now. If we wanted to report to the marketing department that this is a threat to our brand, we could summarize quotes. A quick skim would let us know that trusting the warranty is a big issue for customers on this subreddit, and make recommendations.

As a brief summary, we did the following:

1. Started with an unknown dataset with a vague research question of figuring out what customers have say about this brand.
2. Discovered a few potential themes within a few minutes of exploring the dataset.
3. Drilled down on durability as a broad category to find "delamination" as a key problem that customers talk about.
4. Filtered to a small dataset of 107 documents for further analysis from a dataset of over 40K posts.
5. Found concrete reportable insights in less than 10 minutes.

For example, we found this great quote about a good experience with warranties:

Recently the seams have begun to delaminate and he sent it back to have it repaired. A week later he gets a call telling him that the gortex has reached the end of its serviceable life and is not repairable. They are sending him a brand new beta ar shell! Go arcteryx!!!

And this great quote about this bad experience with warranties:

I paid to ship my quite new jacket for repair, Arcteryx destroyed it, promised me a replacement and basically ghosted me.

These quotes could easily become critical incident reports for a quality assurance team to learn from.

Let's see what happens if we go deeper with our themes.

Developing deeper themes