Understanding your target audience is crucial to the success of any new product or service. Reviews provide helpful information on user experience, general functionality, and what features potential customers want from a product or service. When used properly, they can help improve the message of your product or service and help you craft a better messaging strategy. Review mining is a process that analyzes competitor reviews to gain valuable insight into your target audience.
NLP tools help identify positive, neutral, and negative sentiment
Companies need a solid technological foundation to effectively mine online reviews, including new data sets and sophisticated natural-language-processing (NLP) tools. These tools can help companies analyze free-text comments about a prouct, allowing them to determine whether a product is perceived as positive, negative, or neutral. They can also help businesses identify trends and other information about a product or service, guiding product development and advertising campaigns.
Once a sentiment analysis tool has been set up, the next step is to train it for specific business criteria and terminology. This requires a lot of time and money, and a simple solution can take anywhere from 4-6 months to complete. However, companies should remember that building their tool will require regular updates and monitoring and will only be known to the tech team that created it. Even after implementation, it is crucial to remember that sentiment analysis tools require regular updates and monitoring to remain effective and efficient.
To classify online reviews accurately, NLP tools use sentiment analysis techniques.
For example, the NLP tools Yotpo uses work on more than 30 million reviews to train their technology. Using this data, they grouped topics based on similar meanings and scored them on a scale of -100 to +100. They also used sentiment analysis techniques to extract subjective qualities from the text. While this approach has limited applications, it makes sentiment analysis an invaluable tool for companies seeking to improve their product or service.
Sentiment analysis helps product managers understand the impact of a specific product feature on customer sentiment. Customer sentiment is essential in product development, as it can directly influence sales and churn rates. This method also allows companies to respond to negative reviews and strengthen their brand’s reputation among new customers. The positive and negative sentiment about a product is reflected in the star ratings. The most advanced NLP tools can even detect underlying behavioral motivations.
For example, Atom bank used sentiment analysis to improve its ratings on the App Store by 4.7/5 and reduced the number of failed contact centers by 30%. These tools use machine learning and NLP techniques to process natural-language text. These techniques include tokenization, named entity extraction, and parsing. Ultimately, these tools allow companies to leverage the power of their online communities to improve their customer experiences.
They can extract data from video-based content.
Videos can be a rich data source, but they can also present some unique challenges. For instance, a video that appeals to a political family could not be as engaging to a satirical audience. In such cases, a team needs to analyze a range of possible audiences to identify the video’s components that appeal to different groups. If they do this, they can save good ideas from the scrap heap and bring them to life.
Videos grab attention more than written content. They allow customers to engage with a brand in a short amount of time, creating a more profound brand affinity. However, to drive ROI from these videos, brands must ensure that the visual elements are on point. According to Mordecai Holtz, Co-Founder and Chief Digital Strategist at Blue Thread Marketing, video-based content will continue to evolve. Facebook Live video is an example of an authentic way to interact with an audience and answer questions on demand.
Another advantage of video is that it enables storytelling at scale.
CEOs, of course, can tell their product vision story better than anyone else. However, product leaders can’t attend every sales interaction. Fortunately, video allows product leaders to engage with their customers in a way you cannot do with written content. And as the demand for videos rises, companies can use the information collected from the video-based range to help improve their product.
With the growth of video content, brands can use it to boost their brand awareness and improve sales. MoAs a result, more companies are building in-house video production capacity and integrating video into all forms of communication – from press releases and product demos to culture-building and testimonials. As a result, video is expected to account for 83% of all web traffic by the end of 2023.
The next evolution of video marketing will be increasingly focused on using personalized content. A video-based content strategy can be used at any customer lifecycle stage, from pre-purchase to product development. While video use in marketing is increasingly important, you should still use it in conjunction with other forms of content. In 2018, the video will become a more significant part of a content marketing strategy. Personalized videos, for example, can increase the number of visitors to a website. This way, a company can determine how to use videos in product development.
They can speed up product development.
While a company may have difficulty controlling the online content, it is still possible to gain insight from these reviews. Product teams can improve their offerings and reverse declining market share trends by tracking reviews and mining review text for valuable insights. In addition, they can unlock new product categories and drive organic growth. But how do they do it? This article will outline a few ways product teams can mine online reviews.
First, companies can mine online reviews for product development by analyzing their customer reviews. While previous methods focused on individual features and attributes, user preferences are expressed across several aspects. By researching online reviews and developing product improvement strategies, manufacturers can identify the most effective products and prioritize their development. Researchers developed an algorithm to analyze the considerations to make this process more efficient. Using this analysis, they could identify similar needs among many consumers in twenty percent fewer sentences than before.
Mining customer feedback is helpful for e-commerce.
Aside from Amazon reviews, companies can also mine online consumer feedback to improve their e-commerce business. The information available from online reviews can help a company improve its operational processes, identify product-market gaps, and improve customer experience. For example, Amazon reviews can help a company develop more customer personas. In addition to revealing product differentiation and competitive analysis, these reviews can help an organization understand the motivations of different types of customers.
Companies should develop a technology foundation for the process of online mining reviews for product development. These enablers include sophisticated natural language processing (NLP) tools and new data sets. Such devices are capable of mining free-text comments in web reviews and other sources. They can identify neutral and positive sentiments in reviews and categorize and classify product attributes. Companies can refine their message and improve their product offerings with this knowledge.
They can improve messaging.
To be successful, companies should develop a robust audience research strategy. Gathering enough feedback from potential customers can give them a pulse on what their target audience wants, and the insights gained from this process can help them develop a more vital message. In addition, this type of audience research is crucial to the success of any new business, as it provides valuable insight into user experience, general functionality, and the desire for additional features.
The process of mining reviews is relatively simple, but it’s not free. For instance, the research team created an algorithm to prescreen reviews, removing non-informative and redundant sentences. Non-informative sentences make up nearly half of the corpus and don’t promote product innovation. Redundant reviews mention the same thing over. Once the reviews were prescreened, analysts could uncover the same customer needs in 20 percent fewer sentences.