How are online shopping platforms sorting low-quality merchants from high-quality merchants? Can the rebate for feedback system be successful in improving the sales of merchants and help consumers find the best products?
After searching relentlessly, through numerous e-commerce platforms, to compare and rate a certain product you’re looking for, you might come across the best priced item. However, you may doubt the authenticity of the product, once you realize that the item doesn’t have any good comments or recent sales. This question is a common experience among online shoppers, who often find deals that they think to be too good to be true, highlighting the risks of online shopping.
Sorting the good from the bad
Online shopping platforms like Taobao, Amazon and Dangdang have experienced aggressive growth in the past 20 years. Accordingly, the convenience and the variation of products, often has buyers worrying about being sold fake or low quality products. After all, consumers cannot see the product before purchasing it, and online sellers aren’t as easy to track down as someone selling products at a physical store.
As a result, the trust in such a transaction between “strangers” relies on the buyers’ honest comments and feedback, which validate the transparency of a merchant. Additionally, good feedback and comments about a merchant, gives buyers’ confidence in the purchases they make from the seller.
However, posting comments and feedback are usually time-consuming and most users don’t comment on a merchant’s product and transparency after a purchase. In order to obtain a good reputation, merchants on online shopping platforms such as Taobao, entice their customers with rebates of around $1 USD for a five star rating and a good comment.
However, this tactic by merchants has made some buyers sceptical of the actual quality of products, as users would even give bad products a five start rating for a cash incentive. Moreover, there are concerns about online shopping platforms that allow cash rebates for positive feedback, as users fear the online shopping platforms may be flooded with low quality goods.
According to economic signalling theory, high-quality merchants can distinguish themselves from low-quality merchants, through refunding unsatisfied buyers who give feedback on a product, while a low-quality merchant would instead challenge the buyer’s feedback. In this way, high-quality merchants can effectively deliver the message of product quality to potential buyers.
How does the RFF mechanism work?
Li Lingfang is the Founder of the RFF (rebate-for-feedback) mechanism and she used economic signalling theory to verify the rationality of her mechanism. Her economic logic states that compared to low-quality merchants, high-quality merchants have more regular clients so their profit compensates for the cost refunds.
Li recommend her RFF Mechanism to AliResearch at the end of 2011. Taobao has also launched a similar mechanism named “gifts for comments”, were merchants could set “refund points” for every product. Refund points come in the form of small amounts of monetary compensation, or a coupon for other items in a merchant’s shop.
The “points” transfer from the merchant’s account to the accounts’ of buyers who left valuable comments. Accordingly, the quality of a comment depends on its content and is judged by machine learning algorithms, which study dimensions including; length, content and keywords.
In order to verify the economic signalling theory and research the effects of “gifts for comments”, Li and her colleagues have partnered with Alibaba. This cooperation has led to Li’s collection of the transaction information of more than 12,000 shops’, on Taobao from September 2012 to February 2013 via random sampling. The shops had over six million products, including smartphones, SD cards, facemask and jeans.
Is the RFF mechanism effective?
Li’s research focused on two problems. Firstly, were the merchants who chose “gifts for comments”, able to convey that they were reliable shops and establish good reputations. Secondly, it addressed how the merchants promoted their sales and the quality of comments left about their products, through this mechanism.
In light of her two research questions, Li and her colleagues used regression analysis to make some breakthroughs in their studies.
Li’s research proved that merchants with lower reputations are more likely to choose “gifts for comments”, especially those who’ve just started their online merchant accounts. The results of the research correspond with expectations, as the establishment of a good reputation is integral for new merchants, who often spend large amounts of cash to increase their initial brand awareness. Nevertheless, once sales start to grow and the reputation of a merchant is established, they often no longer need “gifts for comments”.
Moreover, Li’s research pointed out that when comparing the sales before and after adopting the RFF mechanism, those who implemented the mechanism saw an increase in sales. On average adopting “gifts for comments” enables a fourfold growth in first month of its implementation, and it increases long term sales by nearly 30%.
Therefore, Li’s discoveries are aligned with the expectations of economic signalling theory, as the new model of consumer-producer feedback allows for mutual benefits. Since the consumer is able to voice their opinion on the transparency of merchants and find the best seller, while merchants benefit from increased sales and improved reputations.