Learn How Web Data Can Uncover Consumer Sentiment

Customer Reviews in the Marketplace 

Customer reviews are arguably the most valuable sentiment data any digital retail business can collect and analyze. Here, buyers share exactly what they like or dislike about a product, allowing a company to stay customer-focused. For example, a company that sells portable speakers. By analyzing customer reviews, they can learn that most consumers prefer speakers that are:

• waterproof
• have long battery life (24 hours+)
• designed for outdoor use. 

While a fair share of customers express dislike for:

• Large devices
• Complicated setup and Bluetooth pairing
• Speakers that lack sound customization options (e.g., equalizers, bass dominance, etc.).

These consumer reviews are considered the most accurate of all available to companies. By analyzing them alongside factors like geography and age range, companies can decide how to use this information. They might choose to optimize product features or adjust ad campaigns and marketing to highlight what the target audience is looking for.

Social Media Sentiment 

Social media is another place where consumer sentiment can be identified. This can be expressed through:

• A product review or unboxing video on YouTube
• A social media post describing the experience of using a new product
• A dedicated Reddit thread discussing an entire product category, such as "Gas grills - pros and cons."

By analyzing videos, posts, and threads for recurring "social sentiment indicators," companies can begin to form a user-generated picture of the market. For instance, if unboxing videos are generating increasing negativity or frustration due to a missing accessory, such as a cable. Companies can address this by sending the missing part to the disappointed influencer, who can then go live and leave a positive review of the company. 

Search Engine Trends 

How people interact with search engines is a powerful way to gauge sentiment. Most consumer journeys begin with a search query. When aggregated and analyzed, a clear picture emerges of the collective questions being asked in an industry at any given time. For example, a company in the automotive industry might find that consumers in Russia are searching for:

• Are electric vehicles (EVs) really cheaper than gas?
• Does the government offer tax incentives for EVs?
• What is the difference in carbon footprint between an EV and a gas car? 

This reveals that many consumers are skeptical about or unaware of the benefits of owning a non-fossil fuel vehicle. This insight can allow EV companies to create educational webinars, blog posts, and courses to help inform and ultimately convince the target audience. 

Competitor Listing Analysis 

Another rich source of consumer sentiment data is analyzing competitor listing performance data. This can include:

• seasonal sales trends
• correlation between promotions and sell-through rates (STR)
• seller ratings. 

This data can provide insights into what consumers think about certain sellers, as well as what external factors influence positive purchase decisions (especially emotional or impulse buys). Sometimes a correlation can be found between two data points that indicate unplanned purchase decisions, such as:

• a change in product image in the listing and a sales spike
• price fluctuations and an increase/decrease in STR
• a change in seller rating corresponding to higher-than-usual click-through rates (CTR).

Conclusion

The ability to collect consumer sentiment data enables companies to:

• Optimize product features and marketing campaigns
• Identify negative feedback with the ability to adjust course 
• Spot information gaps, giving companies the opportunity to educate and convert the target audience
• Benchmark competitor data, uncovering new paths to positive purchase decisions. 

However, manual data collection can be resource-intensive, so many companies opt for fully automated data collection solutions.