
With 5.07B people using social media, there’s no surprise at the amount of noise on the various social channels out there. Keep in mind that most users have multiple social media accounts, averaging 6.7 per person—ranging from Facebook and Twitter to TikTok, YouTube, Instagram, LinkedIn, and WhatsApp. How can brands enhance their product offerings by leveraging the insights gained from social listening tools?
This blog post explores the limitations of social listening data compared to consumer review insights, highlighting why relying solely on social listening may not be enough.
Social Listening Data Sources
There are many components to social listening platforms, and they should have a place in your marketing tech stack. However, their capabilities are limited by the data sets available to them. Most platforms will look at the following metrics:
- Interactions (such as likes)
- Followers
- Hashtags
- Shares
- Comments
These metrics capture various quantitative and qualitative data around the posts. The more comments, interactions, shares, and hashtags used, the more positive the engagement is likely to be. Even if a user interacts with an angry face, it indicates that the post elicited a response from your audience—they were not ambivalent to your content. While the number of comments reflects quantity, comments are also a great source of qualitative data. The language used by followers can help quantify sentiment using natural language processing (NLP) algorithms. Although sentiment analysis is conducted on individual posts, social media managers can focus on specific time frames to better align sentiment with the overall brand. This is also true for hashtags that may be brand-generated as part of a product launch or special campaign. However, oftentimes, the users themselves actually create them.
For instance, the new Beats wireless headphones were released on May 2nd and many of the new buyers use the #BeatsSolo4 hashtag to share unboxing videos and other content. The filtered posts can be packaged together to get indication of the sentiment around the launch.
Using the Social Media Data Set
Digital Marketers use the social media data set regularly to optimize content, as well as optimizing their ad budget. They look at where their primary audience is, and ways to identify influencers.
But can the data help with the innovation use case? Let’s think about our earlier example, the Beats Solo 4. We can get some indication of sentiment about the product using the hashtag, however it’s very limiting. For instance, can we get quantitative and qualitative data for each variation of the headset. Think about it—the Beats Solo 4 has three color variations: Black, Slate Blue, and Cloud Pink. How can a brand determine sentiment for each variant if there is no hashtag?
Another big data limitation is understanding who is the audience that is posting the hashtag. Are they consumers? If you look at the filtered list with all the videos, many of the original posters were technology journalists writing a product write up for other potential users. The poster didn’t necessarily buy the product. Usually big brands send journalists the product for free. This is where social listening falls short. Basically anyone can be writing about this headset and there’s no way to verify whether they actually purchased them..
Finally, let’s have a look at some of the comments to understand if they can support an innovation use case. When you scroll down the comments of the embedded video, note that the majority of comments are emojis. One comment stands out: “They nailed their packaging🔥” because it is connected to a product attribute. However, the viewer is referring to what he saw in the video, and not necessarily that he himself owned it, and had the same experience.
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Consumer Review Data Sources
Product review data is another thing altogether. The data source is from social media, but rather e-commerce sites like Amazon, Walgreens, Target, to name a few. There are limited options for consumers, leaving a rating, a review, or both. This provides business with both quantitative and qualitative data. The example below highlights the Remington hair dryer which has a 4.4 star rating and over 2,500 reviews on Amazon.
The reviews themselves are written by genuine consumers who purchased the product, and either wrote the review organically or because they were incentivized to do so as a result of a promotion.
At the end of 2023, Amazon started implementing generative AI on consumer reviews to enhance consumer decision making. Continuing the hair dryer example, we see a succinct summary based on the feedback from thousands of consumers. The AI also showcases key product attributes that consumers mentioned. It’s crucial to understand that a single review may contain several opinions about a product – it may discuss, quality, performance, smell, shipping and the product. When clicking on the “Performance” attribute, Amazon displays a sampling of the reviews that discussed the product performance.
Using the Product Review Data Set
Now let’s look at another product, Nexxus Slick Stick, through the lens of consumer reviews, and consider the following questions:
- How can brands gain a competitive advantage?
- How can the data be used for innovation?
- How can consumer reviews strengthen marketing campaigns?
Competitive Advantage
Unlike social listening, consumer reviews can be aggregated across an entire product category to provide unparalleled consumer insights. That means you have the ability to look at the sentiment and feedback around your brand as well as your competitors.
As we take a closer look at the Styling subcategory, we see that the average consumer sentiment across all brands is 77%. The below chart showcases the positioning of 30 leading styling brands. When we isolate the Nexxus brand, we see that the sentiment is 75%, which is two points below the category average. Given its position across the ecosystem, Nexxus can begin to look at the major competitors to understand how it can improve the product, and its overall brand positioning.
Nexxus can get granular with the review data by delving into product topics against major competitors. With one click, Nexxus can see the scores and the ‘Why’ behind the numbers. Earlier we looked at the consumer sentiment from reviews across the entire styling category. Below we go a step further, delving into the various product topics and their respective sentiment for Nexxus, plus three additional competing brands. We see that Nexxus has neutral sentiment around most of the topics, but they stand out around the “Is it recommended” topic, scoring 87% – higher than the industry benchmark of 82%.
Already the Nexxus brand can identify areas of improvement to try to meet the industry, benchmark and topple competitors. Nexxus has to optimize many facets of the product to surpass competitors.
Innovation
In reviewing the competitive landscape of the styling products, we got a sense of the average sentiment plus the attributes where Nexxus needs to improve. What about the Nexxus Slick Stick itself? What will it take to get it back on top? What is it that consumers want?
In looking at the reviews, using the generative AI, we’re able to identify some trends:
- Consumers wish the product had a twist-up mechanism instead of a push to dispense.
- Consumers prefer the product to have a rounded top like deodorant.
- Some consumers hope that other options of the product are better.
- Consumers wish the product came in a jar instead of a stick.
- Consumers desire for the product to be more long-lasting.
- Some consumers would have liked the product to have a stronger fragrance or be scented.
- Others wish the product was unscented.
This feedback can support the Nexxus brand in improving their current product offering plus helps build a clearly defined roadmap to broaden their product line. For instance, the two conflicting opinions of having a scented and unscented version of the product already provide very specific direction to the brand for expansion.
Marketing
Consumer reviews can also support the brand and boost product marketing efforts. The below chart breaks down the topics from the consumer opinions. There are many topics where Nexxus excels and other areas where they didn’t make the grade. What stands out is the neutral sentiment around performance.
The Nexxus Slick Stick is a product that launched in 2024. We can see that “Performance” ranked highly when it was first released in the market as indicated by the green bars. However, consumers are less enamored with the product in April as we start to see sentiment turn negative.
To bolster marketing efforts, we can pull out key themes around performance and utilize them in a number of ways. When we delve into the term cloud, the green words are the ones with positive consumer sentiment. Again, this is based on the feedback in their review. We see words like hair, easy, wonder, help, super, love, soft, color, and others that can be incorporated into marketing campaigns and used to optimize product description pages. Most social listening tools provide some kind of word cloud, but it’s usually on the brand level and may not be data from consumers. These are two significant differentiating factors.
Conclusion
In the ever-evolving landscape of consumer behavior, relying solely on social listening tools falls short in providing a comprehensive understanding of market dynamics. While social media data offers valuable insights, it often lacks the depth and specificity needed for product innovation and strategic decision-making. By incorporating consumer reviews into your analytics framework, you gain access to genuine, detailed feedback from actual purchasers, enabling you to identify key trends, improve product offerings, and enhance your overall brand strategy. To stay ahead in the competitive market, it’s crucial to leverage the rich, actionable insights derived from consumer reviews, ensuring your brand not only meets but exceeds customer expectations.