Fake online reviews cost $152 billion a year. There is a sub culture of review scammers who make money by working with brands who want to take risks and capitalise on a review attack or review spike. These networks can influence markets overnight and its scary how this works.
Here’s how e-commerce sites can stop them.
Online reviews will influence $3.8 trillion of global e-commerce spend in 2021. This is, in theory, a good thing. In their most ideal state, online reviews serve a valuable economic function for all parties in a transaction. These nuggets of wisdom and caution help to bridge information asymmetries between buyers and sellers providing information that may not be otherwise disclosed.
Ive worked with many ecommerce stores on review management work with me here.
However, as with many areas of the internet, while there are well-intentioned valid users, there are also malicious users and bots. In fact, this issue is significantly affecting ecommerce authenticity and even revenue.
- While there are well-intentioned valid users creating online reviews, there are also malicious users and bots.
- Translating this into economic impact, the direct influence of fake online reviews on global online spending is $152 billion.
- There are tools available to help mitigate these risks – not just on review sites but in the customer acquisition funnel and beyond.
Read how e-commerce sites can stop fake reviews, and how they can benefit from online reviews.
You’ll learn how to stop fake reviews on your e-commerce site. I’ll tell you why fake reviews are a problem, and how to stop them using AI-based tools. After reading this post, you will know how to spot fake reviews and how e-commerce sites can stop them.
Amazon has a big problem with fake reviews. The company is looking to implement machine learning in order to find and remove fake reviews. Sadly Amazon does not have a great seller support ecosystem and they believe that the customer is always right.
Overnight in Australia and United States a review attack as they are known can crash a top sellers listing in 12 hours or less. The seller has very little recourse to stop this and retain their high listing or positive seller status. Amazon sucks.
There are apps for Shopify and Software for Review management.
The booming market for fake online reviews
Why are fake online reviews so resilient?
A significant reason is because the return on investment of soliciting fake reviews make them highly profitable. Lets look at Yelp reviews, when a business listing gets an extra gold star that listing lets say its a car part dealer the listing will increase revenue on average by approx 5-9%. That figure is just one of teh increased signals a review increase can help a business.
Now in reverse that could also start to cripple a business if it happens too often. One example of someone gaming the system was a company who we can not name paying fake reviewers in excess of $250,000 that in turn resulted in this business generating just shy of $5 million in sales.
The deployment of fake reviews causes a substantial short-term rocket in organic search positions and sales ranks that can stay increased in listing visibility for weeks, even after deletion or detection. And by using fake reviews, bad actors are able to “bootstrap” their reputations with fake information.
In general, the eventual culling of fake reviews occurs after around 100 days, giving fraudsters enough time to turn a profit. Like other online markets, such as ad fraud, the economic benefits outweigh the risks and costs of being caught. This incentivizes bad actors and the ongoing growth of this underground economy.
The trading of fake online reviews has become standardized with groups,
commission structures, and loyalty schemes. Payments change hands from around 25 cents to $100 per review. Marketplaces include online private groups that increasingly solicit buyers to purchase their product and leave a five-star review in exchange for a full refund, and in some cases a $5-10 commission. Some 23 fake-review related groups operate per day.
Just one automated operation can generate an unlimited number of fake reviews across the e-commerce landscape – and it doesn’t even require programming skills.
The $152 billion problem of fake reviews
Using official figures and self-reporting by the world’s leading e-commerce sites (including Trip Advisor, Yelp, TrustPilot and Amazon) on average we find that 4% of all online reviews are fake.
Translating this into economic impact, the direct influence of fake online reviews on global online spending is $152 billion.
This problem can be further broken down into the impact of fake reviews on review-influenced e-commerce in some of the biggest e-commerce countries. Fake online reviews influence $791 billion of e-commerce spending annually in the US, $6.4 billion in Japan, $5 billion in the UK, $2.3 billion in Canada and $900 million in Australia.
What can e-commerce businesses do?
1. Identify and filter invalid reviews
Since reviews are often placed by bots rather than valid users, it is important to identify these invalid users and mitigate risk accordingly. The key is to get ahead and stop the reviews from happening in the first place.
Knowing where the risks come from and acting fast are key. The most basic fake review bot attack could be created and executed within a few hours.
Businesses can retaliate by ensuring that fake users on review sites aren’t making their way to a company’s site and by deploying customer acquisition security technology to ensure marketing efforts are going towards potential customers rather than malicious bots.
2. Protect global economic growth and small businesses
The direct costs of fake online reviews refer to the immediate losses imposed on society by the crime or attack. However, the US government has suggested that to best understand the cost of harm, estimates should consider both the financial and non-monetary effects.
By gaining a better understanding of how fake online reviews affect different industries and markets, the risk they pose to a specific business can be more accurately gauged and will reveal how that has a domino effect on other businesses and revenues. Education can be the first step toward better protecting an online reputation.
3. Get ahead of enforcement costs
The high price of court cases brought about – both by public bodies and competitors fighting each other’s alleged treatment of fake reviews – is a major indirect cost that could be added to the $152 billion direct loss. The costs of enforcement, lawyer’s fees, court time, executive and employee headspace, and litigating over fake reviews can stretch into millions of dollars.
When businesses protect their reputations by blocking online reviews through a few simple development steps, these costs can be minimized. The problem will only multiply if left to fester, as new technology brings new bots into the area. But if companies actively filter out these kinds of reviews they can ensure enforcement costs are as low as possible.
4. Prioritize action against fake reviews
Negative reviews can cause a negative economic impact on businesses, and when they are created by bots and invalid users they can be even more frustrating.In one case in Australia a plastic surgeon claimed that his business dropped by 23% in the week after a fake review was posted.
In a court case in 2018 (United States District Court Eastern District of California) California based Super Mario Plumbing said their business dropped 25% and were forced to reduce the work of two employees as a result of a competitor’s fake review. Elsewhere, an Ottawa shop selling dumplings cited the time and money they spent: “It took 10 days for 13 years’ worth of work to break down [the issue]”, says Kamilla Riabko of Ottawa Perogies.
By protecting online reputations and by blocking and filtering online reviews businesses protect their revenues. It’s understandable that unsatisfied customers might leave some constructive criticism here and there, but if bots are taking over your review platforms, then minimizing fake reviews should be a priority.
5. Maintain trust
The presence of fake reviews causes a loss of confidence, with 85% of consumers believing the reviews they read were “sometimes or often fake”. Elsewhere, Bazaarvoice found 28% of respondents said a fake review would cause them to distrust other reviews, and 26% said it would lead them to distrust the brand.
Eliminating fake reviews when possible doesn’t just help with revenue and authenticity, but it also helps maintain trust among your existing and future customers. When consumers realize that a brand cares about promoting and championing the truth, they tend to want to continue to buy the service.
It’s time to turn off fake online reviews
The ease and access to fake reviews is only increasing whether by humans or botnets, click farms or groups. This makes online reviews one of the largest markets hit by bad actors and among the costliest in economies where e-commerce has been further expanding during the COVID-19 pandemic and continues to grow.
One tactic I have been enlisting across several client sites where we are getting attacked by fake reviews (competitors) we have been disabling reviews on the Facebook pages. This has slowed down the fake reviews but not solved the problem.
It has become a clear economic necessity to act against fake online reviews and further diminish the reach of bad actors undermining online buying decisions.
Taking necessary steps to minimise these risks positions brands as trustworthy and authentic and helps to expand businesses among authentic users.
Invalid traffic exists everywhere – but there are tools available to help mitigate these risks – not just on review sites but in the customer acquisition funnel and beyond. Businesses should act now.
As a ecommerce store owner myself I find it incredibly annoying having to mitigate invalid reviews, many platforms do not have very good removal features and have a strong line when removing reviews. This is such a headache for me and I felt that it might help many of you that do have your own ecommerce stores that get fake reviews. I hope it helps.
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