When sellers have too much access to information, it can actually worsen outcomes for platforms, sellers, and customers alike.
Authored by SHIN Dongwook
It used to be that if you made a customer happy, they would tell five friends. Now with the megaphone of the internet, whether online customer reviews or social media, they can tell 5,000 friends…Rather than inferior products shouting louder, we have sort of a product meritocracy. It’s very good for customers, it’s very good for the companies that embrace it — and it’s very good for society. — Jeff BEZOS Founder of Amazon
That’s the promise of review-driven platforms like Amazon, JD.com, Taobao, and countless others. When prospective customers can see other buyers’ reviews, manufacturers are incentivized to boost quality, platforms sell more products, and everyone wins. But do these digital marketplaces actually foster the “product meritocracy” that Bezos envisions?
My new research suggests that it’s not so clear-cut. While the increased transparency these platforms offer can enable customers to make more informed buying decisions, it can also create incentives that actually end up driving up prices and reducing product quality — which ultimately harms customers, platforms, and sellers alike.
These counterintuitive dynamics emerge because customer reviews are in fact only one of many sources of data that sellers have access to. In addition to reviews, platforms have enormous troves of data regarding metrics such as sales velocity, customer demographics, with manufacturers (typically for a fee). In other words, reviews are far from sellers’ only window into customers’ feelings about their products. In fact, product reviews provide sellers with even greater visibility into their products, building on their substantial existing insight into customer behavior…and this additional information may or may not ultimately benefit them.
A Transparency Goldilocks’ Effect
In theory, one might assume that there’s no such thing as “too much” transparency. Isn’t more information always better? But in practice, my analysis suggests that there is a Goldilocks’ Effect when it comes to providing manufacturers with visibility into customer data: Too little transparency, and manufacturers can’t learn enough to design high-quality products, so they hedge by just selling cheap, low-quality products. But too much transparency, and they increase their prices so opportunistically that platforms can’t make a profit.
In other words, when sellers already have access to reviews, additional visibility into highly granular customer data gives them so much information that platforms opt to reduce access to customer data and increase prices to account for sellers’ ability to price more opportunistically. As a result, manufacturers also raise their prices and are discouraged from investing in quality. This, in turn, means that customers end up buying lower-quality products at higher prices.
For example, imagine that Acme Inc. sells widgets on Amazon. If Amazon provides Acme with some customer data, Acme can use that data to build widgets with the features that customers seem to like the best, and they can also adjust their wholesale prices to reflect their customers’ price sensitivities, thus boosting both their own profits and the quality of the widgets that platforms and customers receive.
But if Acme has access to much more extensive customer data, then they will be able to adjust their wholesale prices much more opportunistically, for example by increasing prices in response to positive reviews or decreasing them in response to negative ones, which cuts into Amazon’s margins. Anticipating this effect, Amazon may choose to reduce the amount or quality of the customer data it shares with Acme, limiting Acme’s ability to improve its product quality and pricing in response to data.
To be sure, this should not be misconstrued as suggesting that transparency is always harmful. However, it does illustrate the pitfalls of excessive transparency. Platforms that publish product reviews may think of those reviews as largely benefiting customers, but they also function as an information source for sellers, helping them to estimate demand. As a result, the additional benefit of getting private platform data diminishes, while the potential for manufacturers to renegotiate prices based on that data grows. In other words, from the platform’s viewpoint, sharing additional customer information becomes mostly downside, so the platform is incentivized to reduce access to that information. Unfortunately, without that data, manufacturers face greater uncertainty about customer preferences, which they hedge against by lowering production costs (often by lowering quality) and increasing wholesale prices.
Mitigating the Risk of Too Much Transparency
In most cases, it’s not realistic or advisable for platforms to limit manufacturers’ visibility into customer reviews. After all, as Bezos has aptly noted, these reviews are the lifeblood of many platforms — and there’s not generally a way to ensure that only customers (but not sellers) can see them. That said, there are two levers that platforms can pull to navigate these dynamics more effectively:
1. Shade customer information
First and foremost, my analysis demonstrates that more data is not always better. To the contrary, sharing slightly less accurate or less frequent data through a strategy I call “information shading” can increase product quality and overall efficiency.
This involves intentionally limiting the precision, timeliness, or scope of the data shared with partners. For instance, a platform might provide weekly rather than daily performance metrics, aggregate data by region instead of city or zip code, or delay access to certain conversion rate analytics.
Real-world platforms are already exploring various approaches to shading customer information. Alibaba’s Taobao and Tmall, for example, offer tiered analytics packages, where sellers have to pay more for more-granular data. JD.com only shares traffic and conversion reports and keeps deeper behavioral analytics private, offering suppliers enough insight to improve products while maintaining its own strategic position.
Limiting data in these ways reduces the potential for manufacturers to adjust prices or quality opportunistically. While it’s important to provide enough data that sellers can learn to design high-quality products, information shading helps platforms offer the “just right” amount of transparency, which will depend on the type of product, customers’ sensitivity to reviews, and the maturity of partner relationships.
2. Leverage the advantages of AI ethically.
In addition, this Goldilocks’ Effect only emerges when seller-platform contracts are structured with a standard wholesale model. When the manufacturer sells at a fixed wholesale price and the platform adds a retail markup, each firm optimizes for its own margin, not total value creation. As such, when possible, avoiding this model entirely can help better align everyone’s incentives.
Specifically, shifting to a commission model where the manufacturer retains pricing power and pays the platform a fixed percentage of every transaction means sellers feel both the benefits and costs of pricing decisions, giving them more reason to invest in quality. Similarly, because the platform earns a cut of every sale, it benefits from higher demand and customer satisfaction rather than higher margins. This ultimately means that information sharing becomes less risky, since the manufacturer can’t manipulate wholesale prices, so all parties can benefit from greater access to data without falling prey to the misaligned incentives that emerge in a wholesale arrangement.
For example, in contrast to its wholesale Vendor Central platform, Amazon’s Seller Central operates on a commissions basis, naturally encouraging higher product quality and more efficient pricing. Because the platform and manufacturers share incentives around demand rather than markups, they tend to compete on quality and customer experience rather than inflating prices. As a result, the platform, seller, and customer all benefit.
The Impact of Transparency Depends on the Structure of the Marketplace
Ultimately, our research highlights that the true impact of transparency depends on the structure of a given marketplace. In a commission-based market, more transparency is generally better — but in a wholesale market, a Goldilocks’ Effect may take hold, in which too much transparency actually worsens outcomes for everyone.
Interestingly, similar dynamics may come into play beyond e-commerce. Travel platforms, fintech systems, and gig economies where riders rate drivers, clients review freelancers, or borrowers review lenders all produce public signals that can influence internal contracts and trigger inefficiencies. We can even see comparable patterns in industrial supply chains: Automotive OEMs typically share performance data with suppliers, but too much visibility can tempt price hikes or opportunistic behavior. Relatedly, well-intentioned regulatory pushes for transparency such as open banking or health data portability can face the same issue: When information flows freely without thoughtfully constructed incentives, firms may underinvest in quality, security, or innovation.
Transparency Is a Powerful Tool. Use It Wisely.
The goal is not secrecy. Transparency is an important ingredient of a healthy ecosystem — but it’s not the only ingredient. Platforms thrive when their ecosystems reward quality and reliability. Manufacturers succeed with access to relevant insights that guide product development. Customers win when reviews offer meaningful information.
To ensure incentives are aligned (and stay aligned), business leaders must recognize the true complexity of transparency. They must design systems intentionally, acknowledging the ways in which sharing information may negatively influence product quality and price, and where possible, they should consider either shading information or switching to a commission-based model to better align incentives. After all, in a platform economy, transparency is power…but only if you know how to wield it.
Shin Dongwook is an associate professor of operations management at HKUST, focusing on e-commerce, operations management, revenue management, probability models, and online social network systems.
This article draws on the research paper “Product Quality and Information Sharing in the Presence of Reviews,” authored by SHIN Dongwook and Assaf ZEEVI.