How do you know if your organization is doing enough to harness data from customer reviews and other user-generated content to boost ROI?
According to Deloitte, over 25% of a company’s brand value comes from its reputation. It’s no question that word of mouth, star ratings, and written feedback (such as reviews) have a major impact on a brand or product’s bottom line.
Today, many organizations use customer reviews as a diagnostic tool to understand how a product is received by consumers. But this wasn’t always possible.
Over the past decade, text analysis technology has come to the forefront of eCommerce technology – allowing brands to understand their customers more effectively than ever before.
In this article, we’ll discuss the evolution of review & ratings platforms and the difference between review and ratings aggregation and analysis.
The Status Quo
Most consumer packaged goods (CPG) organizations aggregate customer review data using tools such as BazaarVoice, PowerReviews, and Yotpo.
These platforms specialize in aggregation and reputation management, offering functions such as gathering reviews from across the internet, responding to reviews, and review syndication. Some also offer high-level insights such as average star ratings and keyword clouds.
While these are key functions for reputation management, brands that only utilize these tools fall short when it comes to staying ahead of the competition, making customer-centric decisions, and maximizing the ROI of marketing and advertising efforts.
Using Surface-Level Analysis
First-generation tools take the data from reviews to find some basic insights. These methods give a big-picture view of a brand or product’s performance. The insights that are provided are based on some of these basic components:
- Review volume
- Average star ratings
- Basic keyword breakdowns
This type of customer review data can make some guesses about how consumers feel about a product. The star-rating system can give a spectrum of approval or disapproval, but it doesn’t show how people feel.
While breaking down review text to see how frequently certain keywords appear can be useful, it doesn’t intuitively dive into what emotions people experience in real-time. This high-level analysis leaves gaps in data usefulness and accuracy.
Focusing on Review Coverage
First-generation tools serve, in large part, to help brands obtain a minimum number of reviews across a variety of relevant retailers or platforms. This has offered value in the past, considering that businesses earn as much as 108% more in revenue when customers leave more than 25 reviews.
Having a substantial amount of reviews makes a company appear more trustworthy, and people feel more confident about buying products when others can vouch for them. In the early years of eCommerce, obtaining more reviews meant more credibility.
However, in an increasingly online and omnichannel world, consumers are starting to look more closely at review quality as opposed to quantity – making a focus on review coverage increasingly outdated.
The drive to get a certain amount of review coverage means that companies limit themselves to only considering reviews about their products from their sites. This leaves out reams of data that could be found by analyzing reviews of competitors and similar products.
Brands miss out on a gold mine when they limit review analysis to what shows up only on their brand’s website or product listings. Competitive brands harness consumer dissatisfaction from a competitor’s product to make better improvements and get ahead.
A New Era: Deep Textual Analysis that Delivers Actionable Insights
Customer review analysis determines and assigns value to how customers feel about a brand or product. This includes whether customer sentiment is positive, negative, or neutral. Forward-looking organizations use Natural Language Processing to analyze online customer reviews.
Mining data from online product reviews helps companies understand their customers in real-time and adopt an organization-wide customer-centric approach. Review & ratings analysis provides insights for developing new products and creating effective promotional strategies.
Customer review sentiment analysis, also called opinion mining, goes much deeper than what users merely say. Natural Language Processing tools determine customer sentiment by contextually analyzing text and assigning a value to it such as negative, neutral, or positive, much like a human would.
Taking this technology to its fullest potential can bring results that have huge long-term impacts on companies. These benefits may include, but are not limited to, the following:
- Cutting costs on market research efforts
- Improving customer relationships
- Clearly identifying Voice of Customer
- Increasing conversion rates
- Optimizing retailer-specific eCommerce strategies
The Value in Shared Insights
Newer innovations in sentiment analysis make it easier to produce reports and maintain organization-wide alignment with fewer meetings to drive efficiency.
Organizations make more informed decisions and take more effective action when everyone can access the same data and has a shared understanding of the customer. Deep review & ratings analysis helps CPGs centralize data and establish clear objectives and reporting.
This lets people compare results more easily, and rely on shared insights to ensure that everyone moves towards the same goals. Companies need to invest in advanced automated tools that are designed for growth-thinking and data-driven results.
Using Market-Wide Intelligence
Limiting your analysis to reviews about your company can make you lose out on valuable information. Data-driven decisions take a much broader scope. Analysts look to sentiment analysis to observe their competition.
It’s important to see how your products perform when in competition with other companies. An accurate comparison shows more insights than just looking at how consumers feel about a single brand or product.
For example, this happens when you compare tech products. Consumers openly express how they feel Samsung compares to Apple’s products.
Every time iPhones and androids roll out new upgrades, we see that they build off what their competition has been doing in an effort to outperform the competition.
Modern reviews & ratings platforms analyze market-wide data, taking into account all of a brand’s competitors and retailers. This enables brands to identify white space in the market or weaknesses in competitors in order to stay at the head of the pack.
Newer Technology for Competitive Analysis
Yogi uses Natural Language Processing technology with near human-level accuracy that is constantly improving.
Sentiment analysis identifies key themes and how customers genuinely feel about your products on a granular level. It also reveals a lot about how they connect with brands and messaging.
The goal is to understand consumers’ likes and dislikes in real time in order to produce actionable insights that can drive change quicker than ever before.
Find the Best Solutions for Increased Revenue
If your company still relies on the “status quo” methods for reviewing customer-generated data, you are leaving a lot of stones unturned. So much value can be lost if you don’t have the tools to harness it with the most up-to-date solutions.
Yogi has the expertise and products that are built on the most advanced AI tools and customer review analysis.
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Untapped Data: Why Reviews & Ratings Haven't Been Leveraged for Insights Yet
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