The traffic of a product can be summarized as: ranking traffic + search traffic + advertising traffic + non-advertising related traffic.
Ranking traffic refers to when the product ranking enters the “Best Sellers” (the best sales), “New Releases” (the latest hot sales), and “Most Wished For” (the most people want) in the Amazon product category table , “Gift Ideas” (gift list) and “Movers & Shakers” (sales surge) and other ranking lists are in the top 100. Customers see your product by browsing the ranking list and click on the link to generate traffic. (Women' ;s Blouses & Button-Down Shirts category ranking:
The meaning of search traffic is that customers enter certain keywords through search engines. If your product is exposed on the search interface, then because of the user The traffic generated by clicks is search traffic. For example, when we enter women’shenley shirt.
Advertising traffic is divided into headline advertising traffic, top3 column manual advertising traffic, and associated recommendation advertising traffic.
Finally, there is non-advertising related traffic, which is mainly generated by the A9 search engine’s recommendation exposure based on similar product attributes. This article will focus on how to analyze the source of product traffic to promote the exposure and sales of new products.
< p>First of all, it needs to be clear that most of a product’s natural traffic comes from search traffic, while ranking traffic and non-advertising-related traffic are generally not the main traffic sources. Because of this, we can use this rule to gain access to certain new products. Additional exposure.
We first search for t-shirts in the Women category on the Amazon platform.
Except for the first 4 advertising “Sponsored” links. The title is “YunJey Short Sleeve Round Neck Triple Color Block Stripe T-Shirt Casual Blouse”. This indicates that Amazon’s A9 algorithm believes that this product is the most suitable product to be pushed to the first column, that is, the product is related to the keyword “t-shirt” The best match.
Then click the link and you will see the following non-advertising-related product link bar.
Generally, under this kind of hot-selling listing, there are two non-advertising-related links. The two are related, that is to say, when product A is related to product B, product B will also be related to product A. Click on the first related link, whose title is “Romwe Women’s Color Block Blouse Short Sleeve Casual Tee Shirts.” TunicTops”, you can see that there is also a product link column for non-advertising related push under its listing.
You can notice that the first associated link under its listing is the link that ranked first when t-shirt was used as the keyword under the women’s category we just searched. Then you can find a pattern: the pictures pushed by the associated link are inconsistent with the pictures pushed by the search.
From the aforementioned mathematical derivation of A9, it can be seen that the A9 algorithm cannot randomly push pictures meaninglessly. The search results push pictures with the “Red” color under its listing must be because this sub-variant has the highest conversion rate. and order turnover. Analyze the order proportion based on the review distribution under its listing.
This is a screenshot of the reviews under this listing on March 20, 2018. There are a total of 290 reviews, of which the number of reviews in the “Red” color is 109, accounting for 37.5%, and other colors account for 62.5%. There are 11 color sub-variants in this listing, and a single color should account for about 9.1% based on average sales. However, through statistics, it is found that the review ratio of the “Red” color has far exceeded this ratio, so it can be concluded that this color is the most popular color. This is consistent with the previous push results through the A9 search algorithm. We can calculate the number of negative reviews. verify this conclusion. There are 53 negative reviews in total for this listing, of which 18 are for the “Red” color, accounting for 33.96%, and 66.04% for other colors. Theoretically, the average ratio of each color should be about 9.1%, and the “Red” color has 18 negative reviews, accounting for 33.96%. The review ratio has far exceeded this ratio, and the conclusion is established.
Then, a question can be raised based on the above inference: Why does the Amazon A9 algorithm not use the “Red” color sub-variant with the highest conversion rate and order amount as the main image of the push link in non-advertising related push? ?The reasons are as follows:
(1) The main image of the push image and its corresponding link have a high correlation rate, which can encourage customers to purchase two items at the same time. Just like the column name “Customers who bought this item” also bought”.
(2) The original link traffic relies heavily on search traffic rather than associated traffic, so the main image of the associated link is displayed randomly.
If it is (1), the associated order rate will not exceed 15%~2.5% based on the operating experience of clothing products. This is still the associated transaction rate of the first-ranked product, that is, the customer purchases 100 original linked products. On average, 1.5~2.5 related products will be purchased. Improving order efficiency through product association is very low, so it is unrealistic to rely on association to achieve a significant increase in orders.
If it is (2), it proves that the traffic of the original product listing basically does not rely on associated traffic, but on search traffic.
So how to use non-advertising related push to expose new products?
The method is very simple, insert variations one by one. However, there are some details that need to be paid attention to when inserting variants here:
Find the sub-variant attribute corresponding to the main image of the non-advertising affiliate link. In the field of clothing, the sub-variant attribute is basically color. Take the case of “YunJey Short Sleeve Round Neck Triple Color Block Stripe T-Shirt CasualBlouse” as an example. “Black” is the corresponding sub-variant attribute.
Then add variants according to alphabetical order. The variant image pushed by non-advertising associations is generally the main image of the attribute before the atomic variant. Let’s take the link just now as an example. Now “Black” is used as the main image of the associated link. Then there needs to be a sub-variant ranked before “Black” before it can be displayed. According to the sorting of English letters A~Z, it can be displayed before the variant attribute. Add the “A-” annotation, so that the newly added variant can successfully occupy the main image of the associated link, thereby obtaining a large amount of exposure (if the exposure has no obvious effect, you can immediately replace the new product image to improve the efficiency of product testing).
Finally, you need to observe the changes in sales of the original listing product. If the newly added variant is successfully sold and the number of orders gradually increases, it proves that the new product has good selling points and market potential, and it can be put on the shelf separately and sold. , which makes it easier for Amazon’s A9 algorithm to help it find suitable search fields, thereby maximizing sales.