Canceled Flights to Insights

Transforming diverse Frontier Airlines-related social listening datasets into brand takeaways

At Infegy, we love traveling and looking for great deals when exploring (ask us about our flight perk). We'll use this love to highlight how Frontier Airlines could use diverse social listening datasets to better respond to customer complaints. We're not doing this to single them out, but because people often express their complaints about airlines online. Being stuck in an airport with internet access and nothing to do often leads to venting customers. This makes airline customer complaints a fascinating dataset to analyze.

To analyze this phenomenon, we'll compare data collected from the Frontier Airlines’ subreddit with Infegy's general social listening data. These are very different datasets, serve different purposes, but inform the same conclusions. Infegy's out-of-the-box social listening data has a much larger volume but is more general in conversation topics around airlines. On the other hand, the subreddit data is much more targeted but has a lower volume, consisting of Redditors expressing their negative experiences with Frontier Airlines. By applying suitable filters, both datasets inform each other and can provide valuable insights on how Frontier Airlines can address customer complaints effectively.

Why we're using Frontier as our example

Social media users frequently criticize budget airlines like Frontier Airlines or Spirit online. They attract travelers with low-cost fares, but there's a catch. These airlines charge extra fees for many extra services like snacks, change fees, or additional baggage. Moreover, their gate agents earn commissions by catching customers with oversized bags, resulting in hefty fees for checking those bags. As a result, social media, especially TikTok, has been flooded with posts about these practices, and many of them have gone viral with hundreds of millions of views. These emotional social media posts make an excellent target for analysis.

Figure 1: Screenshots of assorted Frontier Airlines TikTok videos

Comparing Topics across datasets

We’ll begin our analysis by comparing how people discuss Frontier Airlines' baggage fees in two places: a general social search and the Frontier Airlines subreddit. At first glance, these discussions seem different. First, Infegy's social dataset (including news stories) has 36 times higher volume than the more niche subreddit dataset. Second, the sentiments expressed in both datasets are dramatically different. In the general social dataset, 54% of the discussions about baggage fees are positive, whereas, in the subreddit, 66% of posts are negative.

The volume differences have an obvious reason. Infegy's collection engine grabs social posts at scale from all corners of the internet. Conversely, Frontier Airlines' subreddit only has 2,200 subscribers and only a few hundred comments over the last few months. The reason behind the sentimental differences is less apparent. We get our first clue from the Topics themselves: the social dataset's subjects are more elevated and appear more professional. At the same time, the subreddit's issues are minor and often express anger.

Figure 2: Top Topics across Infegy's social dataset versus Frontier Airlines (August 2021 through August 2023); Infegy Atlas data.

Why do the conversations look so different?

We'll look at a two-year channel distribution of Frontier Airlines fee conversation to dive into our hunch on the linguistic differences. Starting in August 2022 (when Infegy added news content), News makes up almost 60% of the entire Frontier Airlines conversation. While this is helpful for analysts researching Frontier's press coverage about a brand, some researchers care more about genuine, organic conversations. So, to achieve that, we'll apply a channel filter.

Figure 3: Channel distribution for Frontier Airlines fee-related conversation (August 2021 through August 2023); Infegy Atlas data.

Excluding News to reveal the organic conversation

We'll use a channel filter that excludes news sources like the New York Times or the Wall Street Journal to focus on authentic conversations. You'll notice that both datasets now appear more balanced. Our social dataset volume decreased by about 80%, indicating that people might discuss Frontier Airlines less than viral TikTok videos suggest. This drop in volume is excellent news if you're an analyst at Frontier: a significant use case of social listening data is determining whether viral moments threaten your brand or are more bark than bite.

On the other hand, sentiment also normalized. After removing News, Infegy's social dataset sentiment dropped by 23%. The Topics in the social dataset now sound more conversational and less formal, suggesting that we are visualizing more genuine, organic conversations.

Figure 4: Filtered Top Topics across Infegy's social dataset versus Frontier Airlines (August 2021 through August 2023); Infegy Atlas data.

Frontier's subreddit vs. filtered social

Now that we know we're getting more organic conversations, we'll dive into the clusters of conversations themselves. We'll do this using Infegy Atlas's Narratives, which compares cross-document topics at scale to uncover commonalities across social media posts.

Comparing the two, you'll note how Infegy's social dataset reveals much more diverse conversational clusters. They included Frontier’s rate plans, stock performance, and associated lawsuits. These give researchers and analysts a broad overview of Frontier's brand health. Conversely, the Frontier's subreddit indicates much more targeted conversation dealing with specific Frontier-related complaints. We'll now dive into each to better understand how people talk about the brand.

Figure 5: Comparing Infegy's social dataset narratives with those of the Frontier subreddit August 2021 through August 2023); Infegy Atlas data.

Using Narratives with Infegy's social dataset

First, we'll jump into organic Frontier-related conversation using Infegy's social data. You'll note large volumes and diverse conversational clusters. For example, there are large clusters of Frontier's GoWild all-you-can-fly program and the gate agent incentive program. However, when you dive into these clusters, most underlying conversation doesn't represent actual Frontier customers, but people are reacting to viral events. Getting non-customer viewpoints is okay - brand strategists love to know how prospective customers think of brands. However, to get at actual customer conversation, we'll need a targeted approach. We can get much closer with subreddit data.

Figure 6: Narratives attributed to Frontier Airlines fee conversation (August 2021 through August 2023); Infegy Atlas data.

Using Narratives with Frontier subreddit data

While Infegy's social dataset covers various topics related to Frontier Airlines and how the internet at large reacted to them, the subreddit r/FrontierAirlines mainly focuses on discrete customer complaints. Many of the labeled clusters in the subreddit discuss specific challenges that Reddit users faced with the airline, which mostly revolve around budget-related issues, as we discussed earlier. The most significant labeled clusters highlight frustrations with Frontier's baggage fees and inflexibility regarding reservations and cancellations. It's essential to remember that the volume of complaints in the subreddit is small, so analysts should be cautious not to draw sweeping conclusions. Nevertheless, by examining this more targeted audience, brand strategists can gain valuable insights into what customers, even those expressing anger, are thinking about.

Figure 7: Narratives attributed to Frontier Airlines fee-conversation on r/FrontierAirlines (August 2021 through August 2023); subreddit data.

Highlighting two different use cases

Our analysis of Frontier Airlines' customer complaints demonstrates the importance of using diverse social listening datasets to gain valuable insights for brand strategists. By comparing broader general social data from all channels with targeted subreddit data, we could draw different but complementary conclusions. The larger social dataset provided a broad overview of Frontier's brand health, while the subreddit data gave us a more focused view of specific customer complaints. Filtering out news content revealed more authentic, organic customer conversations, shedding light on customers' genuine sentiments. This comprehensive approach allows brand strategists to understand both the broader perception of the brand and the specific challenges faced by customers, enabling them to make informed decisions to improve customer satisfaction and address concerns effectively.