Online marketplaces could be the modern-day equivalent of Lewis Carroll’s Alice in Wonderland. They are magical lands where millions of buyers and vendors converge. And, with plenty of convenient rabbit holes to go down, they have completely transformed the retail experience. It’s no wonder then that E-commerce platforms account for over 50% of all digital purchases worldwide with consumers averaging an annual spending of $2 trillion on them. However, just like Alice’s Wonderland, these vast shopping utopias come with significant risks. That’s why monitoring vendors is a strategic imperative to ensure the safety, quality, and reliability of the products sold, as well as the integrity of the marketplace itself. This blog explores the necessity of vendor monitoring and how AI and unstructured data can revolutionize this process.
The need for vendor monitoring on E-commerce platforms
Combating fraud and ensuring product safety and quality
One of the primary concerns for E-commerce platforms is the safety, quality, and integrity of products on their site. A recent survey on online shoppers found that around 40% of marketplace users were scammed at one point. That is a disturbing statistic that underscores the importance of rigorous vendor monitoring. Fraudulent activities such as manipulating reviews and selling non-existent, counterfeit, or substandard products can erode consumer trust quickly.
Take the example of Laurel & Wolf, an online interior design marketplace. Despite its innovative concept, upbeat marketing image, and fast growth, the company was dogged by a slew of one-star reviews. Customers complained of subpar products sold through their site, undelivered items, poor vendor service, and rejected refund requests. The ensuing negative publicity was a major contributing factor to the marketplace’s unraveling in 2019.
Legal and regulatory compliance risks
Apart from reputational issues, sub-par vendors can also open a marketplace to legal and regulatory compliance risks. Different regions have specific regulations concerning consumer protection, product safety, and fair-trade practices. Platforms must ensure vendor compliance with these laws to avoid legal repercussions. Non-compliance can result in significant fines, legal actions, and even bans from operating in certain markets. In 2021, Amazon was fined a whopping $877 million for alleged data privacy violations sending a clear signal that regulators now expect companies to be responsible for how their third parties handle data.
Crucially, marketplaces can also be held liable for selling defective products even if it is through a third party. In 2020, a California court sided with a consumer who filed a suit against Amazon for facilitating the sale of a laptop battery that exploded after she had purchased it from a third-party seller on the platform. More recently, an Indian court also held Amazon liable in a case where a third-party seller canceled a student’s purchase of a discounted laptop after the order was confirmed. The marketplace was directed to pay INR 45,000 as compensation to the student.
The role of unstructured data and AI in vendor monitoring
Here’s another alarming statistic – Only 39% of companies assess more than three-fourths of their third-party vendors. In other words, most companies do not monitor all their vendors. And when they do, it is usually a point-in-time analysis based on past data. These gaps in monitoring are mostly due to time and/or resource constraints. For an online marketplace, the sheer number of third parties involved can compound these gaps exponentially. On average, such marketplaces have thousands of sellers and millions of listings. Keeping tabs on all of them is an impossible manual task. This is where AI and unstructured data can change the game taking vendor monitoring from an incomplete one-and-done occurrence to a complete, daily process.
LLM & NLP for customer feedback analysis
Customer reviews, ratings, and feedback can provide a wealth of information about vendor performance and product quality. Additionally, it can be sourced on demand and is an early risk indicator, making it a good metric for proactive, daily vendor monitoring. Using LLM and NLP to aggregate and analyze these insights, eCommerce platforms can identify high-risk vendors who have a large volume of negative feedback, recurring service issues, or persistent operational problems. It can also be used to monitor vendor communication and customer interactions to flag fraudulent activities or scam attempts.
Sentiment analysis for online media monitoring
Several studies have explored the correlation between online sentiment and business health. One study indicated a strong cross-correlation between social media sentiment and stock market returns. Another found that online opinions could effectively predict a company’s financial outcomes. E-commerce platforms can leverage this by using AI-driven sentiment analysis to monitor social media platforms, news media, online discussion forums, etc for mentions of vendors and products, giving them real-time insights into public perception. This can help them detect emerging issues and respond quickly to negative sentiments.
Machine learning for predictive analysis
In the past, risk predictions relied primarily on historical financial data. With the advent of AI, these limitations are no longer necessary. One of the key benefits of AI is its ability to analyze all kinds of data and identify patterns in them quickly. E-commerce marketplaces can use Machine Learning algorithms to leverage data from various sources such as a vendor’s transaction data, behavioral data, customer feedback data, online footprint data, and historical data including past incidents of fraud or policy violations. This data inclusivity helps paint a more accurate picture of a vendor’s current and future risk probability, effectively allowing platforms to proactively address potential issues before they escalate.
Automated checks and daily risk scoring
Automation is key to monitoring vendors in a marketplace with millions of listings. AI can automate most if not all the vendor-monitoring processes. For example, it can quickly identify non-compliant listings by cross-referencing product descriptions, certifications, and vendor credentials with regulatory databases. This reduces the burden on human auditors and ensures consistent enforcement of regulations. In addition, due to its ability to track and analyze data quickly and in real time, AI-enabled vendor monitoring can calculate risk scores daily, making it easy for platforms to keep an eye on sub-par vendors continuously no matter how small or big their marketplace is.
Conclusion
Monitoring marketplace vendors across various parameters and on a daily basis is a complex task. But, with AI-driven technology it doesn’t have to be. By using AI to incorporate real-time monitoring, advanced predictive data analysis, and sentiment scoring, marketplaces can foster a safer shopping environment with greater efficiency and accuracy.
Discover how TRaiCE can help you gain a competitive edge with effective daily and complete vendor monitoring. Schedule a demo today with our team to know more.
