Amazon Has a Problem With Your New Product Launch

Don’t worry. It’s not personal. Amazon has the same problem with all new listings.

At the heart of Amazon’s ranking algorithm is a preference for behavioral signals. This includes how frequently a listing is clicked, purchased, returned, reviews posted, and a host of lesser signals such as browsing behavior. Products with a robust history of sales and positive interactions tend to receive favorable treatment in search results and recommendations. This makes sense because Amazon wants to put products in front of a consumer that they are most likely to purchase.

This predisposition inherently disadvantages new product listings, which lack the track record to leverage these behavioral cues effectively. Consequently, even if a new product is superior in quality, innovation, or price it may struggle to gain traction against established competitors with a proven sales history. Amazon recognizes that this isn’t ideal and refers to this issue as the “Cold Start” problem.

To combat this, Amazon gives a new listing a “chance” to compete against more established listings by ranking it high for searches it thinks are relevant for the listing. During this period of time which can vary from 2 to 6 weeks, a new listing can find itself shoulder to shoulder with the most established products for a given search. Amazon is diligently collecting behavioral data about the new listing during this period. This is commonly referred to as the “honeymoon period” because the listing is treated so favorably.

Eventually Amazon decides it has enough data to compare the listing effectively and ceases this artificial “boost”. It’s important when launching new products that you make the most of this period to help deliver the best behavioral signals possible. If this period is ignored, a listing can get on a specific trajectory that’s difficult to change. This involves producing the best possible listing, delivering over the top customer service, and driving traffic to the listing that you know are more likely to convert (like existing customers).

Much of the recent research in this domain revolves around leveraging various attributes of the product listing to predict its likelihood of success even without behavioral signals. These attributes encompass elements such as the quality of images, the efficacy of the title and bullet points, and the richness of the product content. Again placing en emphasis on creating the best listing possible from the very start.

Moreover, researchers also delve into auxiliary details about the seller, including their reputation, fulfillment capabilities, and customer service standards. By synthesizing these disparate data points, analysts seek to construct models that estimate the probability of a user’s interest in the product. This predictive framework then informs Amazon’s algorithms, allowing for a more nuanced integration of new listings into the platform’s ecosystem.

While Amazon’s preference for behavioral signals poses a significant challenge for new listings, it’s not an insurmountable obstacle. Understanding the dynamics of the “Cold Start” problem is crucial for sellers looking to make their mark in the competitive landscape of Amazon. By recognizing the importance of the initial “honeymoon period” and maximizing the impact of behavioral signals, sellers can position their products for success from the outset.

Amazon’s concerted efforts to address this issue, including the temporary boost in ranking for new listings and ongoing research into predictive modeling, highlight the platform’s commitment to fostering a fair and dynamic marketplace. However, the onus remains on sellers to optimize their listings, provide exceptional customer service, and drive targeted traffic to capitalize on this opportunity effectively.