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Introduction
Have you ever thought to yourself: “how is it possible that over 10,000 people have rated this (insert product or service) five stars? How in the heck am I supposed to make a decision with this?” Us too. Let’s explore the problems with customer reviews, their impact, and what you can do to make better decisions, especially when it comes to choosing critical B2B professional services partners.
As a society, we have historically relied on the opinion of others in all facets of our lives. From who we pick as romantic partners, to what kind of toaster we should choose. And, as we choose more and more of these online, there has been a deluge of data on the opinions of others in all of these decisions. In the earlier days of the internet, these reviews could be helpful; people who had purchased that toaster or that blender wrote reviews and gave their opinions on what worked and what didn’t. Today, the game has changed. What was once a reliable and helpful transfer of trust has become a manipulative tool for marketing.
What’s wrong with reviews?
To illustrate this point, an amazon.com search for a toaster yields the following top 3 results:
Figure 1: manipulated search results
The key question is: are these ratings helping anyone anymore? Out of these three, two are “sponsored” - conflating the seller’s paid results with the best options, and all three have over 15,000 reviews with an average of 4+ stars - does this smell fishy to anybody else? Did fifteen-thousand actual humans care enough about a $16 purchase to write a real and thoughtful review? Doubtful. This is indicative of some of the key problems we’ll explore in this post:
The impact on professional services vendor selection
Taking this problem into our area of expertise, professional services (proserv), let’s examine these issues and their impact on how businesses find proserv partners:
Figure 2: review farm in action
Figure 3: Pay for reviews
How can we solve it?
This is where we believe that AI can serve a valuable role as an unbiased aggregator and analyzer of all the public data points on a given company. We built sc0red to contend with just this challenge. Similar to gold standard rating guides such as Consumer Reports, the Wirecutter, the Michelin Guide, etc., sc0red uses rating criteria from industry veterans, scaled with the power of AI, to provide helpful, clear, and evidence based ratings. sc0red works by:
Let’s take an example to compare and contrast a review site with sc0red. Looking at the top mobile app development companies on G2.com, we see the following:
Figure 4: top mobile app providers from G2.com
Taking one of the top providers we see a perfect 5 star score based on the reviews provided. When asked “what do you dislike about the company”, the following is common:
While nice, it doesn’t exactly help the service seeker make an informed decision. So what does sc0red have to say?
Figure 5: sc0red review of a top G2 pick
While not bad at 3 out of 5 stars, it highlights several areas of assessment that the service seeker may want to delve deeper into. This gives them insight into potential blind spots that could be missed if only going off biased reviews.
Conclusion
We are in an epidemic of fictitious and misleading review data, forcing service seekers to find alternative means of finding and selecting partners for critical projects. Especially when the decision is more important than finding the right toaster, but rather - will this partner enable my business to grow or threaten its viability. We need a better way to sort the wheat from the chaff, the sheep from the goats, or the raisins from the bran. Today’s review sites have become an “advocate for hire” rather than an unbiased guide to find your best-fit proserv partner, and we need a better way.
We hope that you give sc0red a try and drive progress through partnerships.
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