How to spot fake online product reviews: The dark side of e-commerce
The author digs into the dark world of paid, fake online reviews - what they learned is astounding.
As marketers, we’ve heard that the customer journey is changing for years now. We ourselves have experienced this change as buyers – I strive to make purchase decisions without ever needing to speak to a human, whether in person or over the phone.
Overall, customers are more informed now than ever before in history. And this shift has created an ever-shrinking window for brands to interact with their prospects and customers.
Think about when you want to purchase a new product or try a new restaurant… do you check the product reviews on Amazon.com, Yelp, or even TikTok (the preferred search engine for Gen Z) for what others thought on their most recent experience? I know I do.
Customers are more informed: Brands need to up their game
With many customers now making purchase decisions well before they come to a brand’s store or website, organizations must take complete advantage of the limited time they have to interact with customers. That said, most brands understand the importance of making the most out of every interaction they have with customers.
As customers are getting smarter, brands need to respond with more intelligent marketing by:
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Listening to consumers
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Identifying opportunities
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Then proactively addressing the needs and interests of their customers
For example, the San Jose Sharks, a National Hockey League team, is using machine learning capabilities for marketing to engage fans – particularly season ticket holders – to extend retention offers and special giveaways, thereby increasing guest satisfaction and decreasing attrition.
The future of sports: Digital experiences for the win
The future of sports requires great digital experiences that meld physical and online worlds, as well as a deep understanding of fans.
Through data analysis, the Sharks discovered that key indicators for attrition weren’t demographics (age, gender, income, or location). Rather, behavioral factors proved to be the strongest indication of attrition, such as late arrivals to the game, not being able to resell game tickets, or lack of engagement with the organization for 30 days or more. So, the Sharks started to monitor its high-value season ticket holders for these behaviors.
Utilizing machine-learning capabilities, the Sharks started to track and identify high-risk fans, taking a proactive approach to extend retention offers like special seating or free give-a-ways to retain fans.
Happy Marketing!
