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Granted, many of us have probably had an inkling that fake feedback was on the rise. Perhaps we noticed an oddly mechanical turn of phrase in that Yelp review, or an unnerving pattern in the precise features that keep popping up. And of course, if it’s long been possible to buy Facebook likes and Instagram followers, it’s only natural that an entire industry of review farms has risen to the demand for positive reviews. 

Online reviews are believed to influence up to $28B USD in annual UK booking transactions alone.

But beyond being annoying, how does that surge in unreliable, qualitative data impact customer trust and loyalty? And more importantly: how can brands be vigilant in understanding their data in order to maintain credibility and spot real issues as they arise? 

1. How fake reviews break more than consumer trust

One of the most obvious impacts of fake reviews, of course, is the erosion of customer trust. And that’s no small hit, especially for customer experience professionals. Trust, in many ways, has become the ultimate currency – not to mention the most direct pathway to customer loyalty. In fact, a recent study by PR Firm Edelman shows that 80% of global respondents named brand trust as either a deal-breaker, or a deciding factor in their purchasing decision. So if customers think you’re feeding them fake information to bolster your sales, you’re likely to see the ripple effects on your conversion rates and bottom line. 

But beyond the decrease in consumer trust, fake reviews also impact your company’s ability to make strategic and informed business decisions. Recent numbers show that 65% of marketers world-wide see improved data analysis capabilities as their top priority. And as most brands move towards a data-driven approach to product development and customer experience, they rely heavily on the accuracy of their qualitative data to improve every touchpoint, minimize friction, and identify opportunities to improve and bolster revenue streams. You can see then, how  data failing to discern and tag legitimate pain points, will once again risk chipping away at your customer’s trust. But how can brands make sure they’re not letting fake, unreliable data sway or cloud their decision-making? By training their keen eye – and sophisticated AI – to recognize the signs.

80% of global respondents named brand trust as either a deal-breaker, or a deciding factor in their purchasing decision.

2. How AI can filter out fake reviews

Though deciphering fake reviews can prove much more complex and complicated than you might think, there are nonetheless a few tell-tale signs that can help you raise a red flag. 

“Research has shown that people who are posting fake reviews haven’t actually bought the product,” says Keatext CTO, Charles-Olivier Simard. “So their way of describing the product is different, a bit more elusive, a bit more generic.” 

But beyond being imprecise – an experiment by Cornell University showed humans were able to spot fake reviews with less than 50% accuracy – relying on human detection makes it nearly impossible to scale. That’s where text analytics comes in. Through powerful AI and continuous training, text analytics algorithms are capable of deciphering and tagging patterns that might not be embedded in the meta-data used by traditional algorithms. 

Text analytics algorithms are capable of deciphering and tagging patterns that might not be embedded in the meta-data used by traditional algorithms.

For example, reviews generated through promotional channels or incentives will sometimes be identified through text in the post itself. The post might, for example, include a disclaimer stating “this review was gathered through a promotional initiative.” While that linguistic pattern would not necessarily be identified or flagged by review platforms, text analytics technology like Keatext can easily be trained to pick up on those unique word combinations. In fact, that sort of analysis is what text analytics does best.

Another thing to look out for? Duplication. If the same text is found across multiple sources, it’s usually worth flagging for further validation. Often, you’ll find the same review posted on different versions of the same website or across multiple review channels; signalling that at the very least, the business is likely to have duplicated an existing review to bolster their ratings. But of course, to be able to pick up on those cross-channel patterns and trends, you first have to centralize and de-silo your data. As things stand, 30% of organizations state data silos and fragmentation as one of the biggest challenges to implementing a data-driven customer experience – and fake review farms have quickly learned to use this to their advantage.

“If you focus on one source,” says Simard, “this won’t seem like an issue – but you’re dealing with a dangerous lack of visibility across other platforms where people are talking about you. That’s why we work across multiple sources to allow for monitoring and visibility across all of those channels; and that can mean up to 40 or 45 channels for some of our current clients. With the rise of fake reviews, it becomes a must-have to look at your reviews across multiple properties to see if you can notice any of those patterns emerge.”

With the rise of fake reviews, it becomes a must-have to look at your reviews across multiple properties to see if you can notice any of those patterns emerge.

According to Simard, once all of your feedback and review data is centralized, insights become a simple matter of how you filter and compare your analytics. 

“We like to keep fake reviews as part of the customer data sets so they can review and flag them if ever our AI made a bad call – which then allows us to readjust the backend. But on our dashboard, users have the ability to slice and dice their data not only based on themes, number of stars, or location, but also based on user score. If a review is flagged as a duplicate, the user score will take a serious hit, which means you can then use that metric to filter out unreliable or fake reviews.’ 

3. Fighting the good fight

 Brands are best served by investing in the right tools and technology to have full context and visibility over their own feedback analysis and qualitative data.

While quirky experiments like London’s top-rated, non-existent restaurant serve as a light-hearted reminder to stay cautious and critical online, the issue of fake and unreliable customer experience data has very real impacts on the services we provide and the products we build. And of course, as Simard points out, the more complex our solutions to counter it, the more creative the fraudsters will become in their quest to fool sophisticated algorithms. That’s why, beyond relying on platforms like Trip Advisor to single-handedly tackle the problem, brands are best served by investing in the right tools and technology to have full context and visibility over their own feedback analysis and qualitative data. With that internal capacity, companies can not only decipher fake reviews and have them taken down by the respective review platforms, but they’re equipped to focus on what truly matters: improving customer experiences by responding more nimbly and quickly to legitimate issues and criticism. 

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Text analytics tools provide the analytical horsepower and the highly trained algorithms that hospitality brands need to break down the silos, cut through the noise and get to the great ideas. Once those great ideas are found, sentiment analysis metrics help brands test, iterate and fine-tune new services in response to real-time feedback and reviews.
Here’s why text analytics can be such a powerful tool in the development, testing and co-creation of innovative new hospitality experience, products, and services.

Reason 1: People are literally telling you what they want

It’s been proven time and time again that consumers are willing to share valuable information about their demographics, their preferences and their consumption habits, but they expect something in return: an improved experience with your brand. In fact, numbers show that 80 percent of consumers are willing to share personal data if they receive offers or benefits such as reward points or well-curated recommendations. So it has become more than necessary to decipher consumers’ wants and needs in order to continuously come up with the creative perks and benefits that will keep them around. The good news is guests and potential customers are more than willing to tell exactly what they want.

In many ways, text analytics tools act like highly sophisticated versions of the “suggestion boxes” you once found in your dentist’s office or scattered across your workplace. But by processing and analyzing large quantities of unstructured data in real time, text analytics tools are able to consolidate multiple valuable data sources and provide context with a speed and unbiased accuracy only possible thanks to the latest breakthroughs in deep learning and AI. As algorithms learn to discern the nuances in the audience’s channels and linguistics, they also separate feedback into categories.

Text analytics is the direct pathway to great new services guests actually want.

Text analytics is the direct pathway to great new services guests actually want. Often, common criticisms can be addressed with quick fixes, which have the potential to turn into value-added premium products or services that can be implemented at little cost.

Reason 2: People will tell you (quickly) when you’re wrong

To adapt to a much quicker pace of change and innovation, the hospitality industry has looked to other industries for tried-and-tested technologies and methodologies. One practice that’s been adopted is “failing fast,” which comes from the startup world and encourages innovative teams to propel interesting ideas up the implementation and testing funnel in order to rapidly identify and expand on the ones that bubble up to the top—all the while minimizing the losses from those that don’t. It’s been well recorded that the world’s most successful brands run thousands of failed experiments every year. For example, Intuit averages 1300, Procter & Gamble between 7000 and 10,000, Google 7000, and Netflix 1000.

In order for the experiments to bear fruit, each of the companies needs the capacity to understand, contextualize and quantify huge bodies of unstructured feedback from multiple channels and experiments at a time. Only with these insights can brands truly evaluate the potential and impact of new products and services.

By providing real-time analysis of unstructured feedback and brand sentiment metrics, text analytics tools give companies early visibility into how guests and potential customers are reacting to new services or approaches. As trends emerge, development teams can rapidly decide whether to nip an idea in the bud, pivot it in a new direction or tweak it slightly to keep improving it. As the best ideas make their way through every new level of testing and iteration, departments are able to dramatically shorten their development cycle while minimizing costs and constantly churning through fresh batches of precious feedback and ideas. That, in turn, means they’re able to offer exciting new perks to their guests much faster—giving them a first-mover advantage and the opportunity to delight and surprise guests before expectations change again.

Reason 3: Co-create new services with your guests

While the hospitality industry has always juggled with the subtleties of balancing warm human interactions with privacy, new trends and technologies are pushing industry professionals to now consider the balance between careful curation and autonomy for the hospitality experience.

According to the Deloitte article “Next gen hotel guests have checked in”, “this change—from a hotel offering pay-per-view movies to a hotel providing free high-speed Internet that enables guests to manage their own entertainment content—is only one example of the larger trend toward the hotel as ‘enabler’ of a ‘partnership ecosystem’ that extends beyond its walls. This principle is seen in the “choreographer” integrator type within our perspective on the hotel of the future.”

57 percent of American travelers want brands to use their data to create personalized travel recommendations

Leveraging technology and data to foster autonomy is just another way of catering to an increasing consumer desire for customization and a personalized hospitality experience. In fact, numbers show that 57 percent of American travelers want brands to use their data to create personalized travel recommendations, while 36 percent would be willing to pay more for the additional service. This gives the hospitality industry a unique opportunity to identify and curate complimentary services based on an entire ecosystem of brands and experiences that connect to theirs in a multitude of ways.

With every new add-on and complementary feature you try, a powerful text analytics tool can easily keep a constant eye on specific themes, keywords and topics in order to track the emotional impact of the small touches and details. Are you noticing an upward shift in comments about your location now that you’ve introduced your guests to that hidden spot around the block? Powerful algorithms are perfectly equipped to help you evaluate which features and services are resonating with your guests and tangibly moving the needle, and which are better left behind.

Hungry for more? We’ve just completed a deep dive into the ways text analytics can serve the hospitality industry by improving companies’ abilities to adapt to new customer demands, tap into audience insights and come up with new products that will help their brand stand apart. Read our new Hospitality guide Improve your guest satisfaction with text analytics for more.

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In the hospitality industry, as Deloitte’s 2019 US Travel and Hospitality Outlook report outlines, customer experience across almost all departments and systems faces several challenges: rising operating costs are forcing companies to look for areas where they can become more efficient; risks to a company’s reputation are high, especially in times of crisis management; and an increase in competition has lead to a decrease in loyalty. Each of these challenges requires improved planning and practices—and the tools to get out ahead. A CX strategy that incorporates text analytics lets companies recognize previously unknown data-gathering opportunities and transform their guests’ comments and insights into loyalty-building experiences with their brand.

Making CX personal in hospitality

As hospitality organizations rush to personalize services and products, they may lose sight of either the big picture or one of the thousands of individuals who constitute it. But it’s important for them to understand that the connection between customer experience and brand loyalty depends more than ever on what guests are saying about them. In this time of online feedback and data analysis, making the most of that connection depends on data.

Deloitte’s “Next-gen hotel guests have checked in: The changing guest experience” reports that guests with a higher loyalty status indicated they were 75 percent satisfied with the company, while guests with a lower loyalty status were only 61 percent satisfied. The report goes on to highlight five areas that link loyalty with CX: engaging with guests, empowering them, delighting them, listening to them and really getting to know them. And while the report states that 80 percent of all guests who responded to Deloitte’s research survey were satisfied with the friendliness and attentiveness of the service they received, only 60 percent and 65 percent of them, respectively, were satisfied with the proactive communication and personalization of the experiences. That represents a huge value-tied opportunity for hospitality industry decisions-makers.

NPS is not enough

The unstructured data found in online reviews and social media sites is rife with emotional feedback: it’s where customers tell their stories and reveal their true feelings about products and services.

The net promoter score (NPS) is a popular metric of customer loyalty and satisfaction. When the NPS is integrated into a CX strategy, CX begins to become more personalized, and when it’s employed alongside other customer feedback, the ability to personalize the services increases. The more feedback received, the more accurate a company’s CX strategy can become and the more it can be adjusted to changes in consumer trends. Embedding both NPS and AI text analysis into CX strategy lets companies connect with guests on an experiential and emotional level. The unstructured data found in online reviews and social media sites is rife with emotional feedback: it’s where customers tell their stories and reveal their true feelings about products and services.

Take data out of organizational silos

The 2019 US Travel and Hospitality Outlook report recognizes that for hotels to drive capability around personalization, “ecosystem integration should be a priority,” including back-end connectivity. Data is gathered every day in almost every hotel and travel and tourism company, from the customer relations to property management departments. Rather than siloing customer information in separate databases, which presents a challenge, text analytics tools centralize relevant information by automating data collection, analysis and implementation.

Feedback analysis can be integrated with on-site data gathered by property management systems, customer relationship management, loyalty programs and other systems, making personalization opportunities easier to spot. Data analysis also connects the voice of the customer to decision-makers such as customer experience officers and guest experience officers. When guests’ preferences can be pinpointed, companies can reach out with complimentary offers both during their stay and in the digital realm through data-driven, profit-generating intervention programs. As a result, the customer feedback loop begins to close: when companies know what a variety of guests are saying, they can take action in relevant areas and promote those changes back to their guests.

One hotel’s CX transformation

Most organizations operate at or below the level of meeting customer expectations, meaning that customers felt negative functional satisfaction and would possibly never repurchase, which has an impact on brand and business performance.

With the potential for posts on social media and online feedback sites to go viral, one customer’s bad customer experience really can make a difference to a brand’s image. The recent report Wow the hospitality customers: Transforming innovation into performance through design thinking and human performance technology follows the Mira Hong Kong hotel’s journey reviewing guest feedback in several departments and widening the scope of their CX strategy in an effort to not only meet guests’ expectations but exceed them. The report points out that most organizations operate at or below the level of meeting customer expectations, meaning that customers felt negative functional satisfaction and would possibly never repurchase, which has an impact on brand and business performance.

The Mira Hong Kong hotel case study applies human-centered design thinking to CX, illustrating how exceptional customer experience is created by “injecting purpose and empathy into everything the hotel business does.” Their design thinking process of innovation followed an “ideate + prototype + test” cycle, with the test phase yielding unexpected insights from feedback, including guests’ responses to everything from check-in desk experiences to treatments at the hotel spa.

In searching for solutions that would appeal to the majority of guests, the hotel realized they had to look at a true diversity of individual perspectives, from age to income, including the extremes of the spectrum. Everyone’s experience mattered. No one’s feedback could be left out, but rather than look at all of it individually, the feedback could be analyzed together, allowing for workable conclusions to be drawn. The hotel’s experiments in personalizing CX resulted in heightened efficiency across departments and increases in both guest loyalty and revenue.

Insights through text analysis

If we expand the Mira Hong Kong hotel’s experiments to include past and future guests, people who are searching for a hotel online, and customers who have made comments on dozens of major and minor online feedback sites, enough data can be gained to provide a company with understanding and insight. AI text analysis of customer feedback identifies valuable data without losing sight of individual customers’ experiences, feelings towards a company or level of brand loyalty.

The “Next-gen hotel guests have checked in” article reports that “engaging guests meaningfully and creating lasting, positive impressions is the path to obtaining and retaining guest loyalty and advocacy.” These complex insights can drive action, letting companies tailor recommendations for guests, notify them about events happening during their travels and generally make their experience more satisfying, special and memorable. AI-aggregated data and analysis lets companies identify customers by multiple and overlapping segments, communicate with them through customized messages and offer relevant upgrades and services that suit them.

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