How to Fix Negative Sentiment Analysis Using Product Review Data

How to Fix Negative Sentiment Analysis Using Product Review Data

No business owner wants to see negative reviews, but unfortunately, even the best companies sometimes experience unwelcome feedback from customers who are not satisfied. It may not seem possible to control negative reviews, but there are some ways to mitigate their effect through sentiment analysis.

What Is Sentiment Analysis?

Sentiment analysis provides accurate insights into how consumers feel about a product or service. It uses AI technology and machine learning to interpret user-generated content, such as reviews or social media posts. Sentiment analysis using product review data is the first step towards smarter marketing research.

Sentiment analysis is a valuable method for forming an accurate picture of how consumers feel about companies because it focuses directly on the customer at a moment when they voluntarily express their views and offer feedback. Updating this data regularly is easy with the right AI tools.

Almost every business owner feels they have a clear idea of how the public views their brand. They may be surprised to see some negative reviews and see them as outliers. However, it is important to pay attention to feedback that isn’t flattering and to use it to inform business strategy. Constructive listening can prevent more negative reviews in the future.

Data analytics and sentiment analysis take subjective information and translate it into usable, objective data that can be implemented into strategies that will strengthen the company. Looking at the negative feedback with a constructive attitude can make it easier to directly address the issues and innovate in a direction that will satisfy customers.

The power of sentiment analysis can be seen in action with the comparison of brands and products. In one case, Lysol and Clorox were compared through sentiment analysis of reviews for the brands and their specific products.

The analysis categorized and rated customer reviews on these two brands based on smell, ease of use, ability to eliminate stains,  and other factors. Reviews were then used to distinguish customers’ preferences for different products within brands based on features such as whitening power, convenient packaging, and multi-purpose uses.

As seen in the above example, sentiment analysis demonstrated what worked well and what worked less well for both brands. The negative as well as the positive reviews were useful in providing the whole picture of how products and brands are seen by consumers and are instrumental in providing pointers on where to make improvements.

Reply to Negative Reviews

Analyzing negative reviews with AI sentiment analysis tools is an essential step, but it is also essential to reply to negative reviews directly on the site. This means having someone on hand to keep track of negative reviews where they appear and respond to them with constructive statements promptly.

The damage of negative reviews can be mitigated by direct interaction. Many have witnessed this dynamic directly on review sites. A reviewer is upset that a product arrived broken or damaged. If there is silence from the company, that negative comment makes an indelible impression even if it is surrounded by positive reviews about the item.

On the other hand, when the same negative comment is responded to promptly by someone who represents the company, the reader witnesses attentive customer service in action. It may have seemed more discreet to discuss the problem with the dissatisfied customer of the review site, but the choice to address the issue publicly is more effective.

The company representative can go further and openly make statements about how the service will be improved or provide an offer for a refund. When the issue is resolved publicly, the reviewer can be encouraged to add a postscript to the review and describe how the company provided prompt customer service and adequate compensation or resolved the issue.

This scenario often plays out on review sites and demonstrates companies turning negative reviews into effective customer service opportunities. They show openness and flexibility to adapt and improve.

Identify Flaws and Fix Them

Sentiment analysis highlights what is working and what is not. This can prevent the tendency to continue pursuing a particular strategy or producing items that consumers don’t really want or need. Negative reviews identify issues and can help change direction when necessary. It may be uncomfortable at first, but in such cases, change is welcome.

One famous example of a company that leveraged negative reviews to upgrade its product was Domino's Pizza. This mature company was facing increasing competition with a viewed product, quite literally, as stale. It launched a counterintuitive ad campaign that focused on and even ridiculed its product.

Rather than downplaying negative feedback, Domino’s comically admitted their pizza crust was like cardboard and won the trust of customers. Since then the company changed its recipe, re-invented itself, and customers have noticed. A company doesn’t have to go as far as Dominos and make fun of their flaws openly, but Dominos provides a valuable lesson in listening to customers.

Turning Negatives into Positives

Customer feedback is a gift to marketing research. This is true whether the input is positive or negative. The same might not be absolutely true on review sites, but even negative reviews can be turned around through interacting with customers and delivering on promises.

Sentiment analysis processes data from reviews into insights that guide a business in product development and marketing. Deriving data directly from customer statements fine-tunes business strategy and guides it in the right direction.

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