Seller Sprite's ASIN Inclusion Principle and Sales Forecasting Principle

2024-04-30| Guide|views(2613)|Comments(0)

Seller Sprite's ASIN Inclusion Principle

At the beginning of each month, we identify ASINs within the top 500,000 in the main category BSR ranking on Amazon's site, with the exception of the US site, which includes ASINs within the top 800,000.

Then, based on our proprietary sales forecasting algorithm, we predict the monthly sales of those ASINs for the previous calendar month.

This inclusion principle raises several issues:

1. For categories with numerous SKUs such as apparel and outdoor products, many ASINs may not be included.

2. New products that experience a surge in BSR rankings after the beginning of the month may not have been captured.

3. For variants that do not share a parent BSR, sales may be minimal and without a BSR, they cannot be collected.

4. Certain smaller categories, like Tablet Stands (searchable as "ipad holder" on Amazon), only have minor category BSRs and therefore are not included.

In fact, for general product selection needs, this inclusion principle suffices.

We have included approximately 20 million ASINs on Amazon US (cumulative ASINs in history total 60 million; some are from past months, such as seasonal products).

In terms of ASIN inclusion quantity, we might not match some competitors, as we consider constraints such as our own server resources and financial costs.

We aim to be a comprehensive product selection tool and are the only company to have started from scratch without venture capital funding.

As more and more users choose us, our funds are more abundant, so we will continuously improve the completeness and timeliness of ASIN data.

Seller Sprite Sales Forecasting Principle

1. Firstly, we have a massive data processing capacity crawler engine that daily retrieves basic product information from various Amazon sites, such as prices, review counts, and particularly BSR rankings.

2. Based on our accumulation of historical sales data over the past three years and real user feedback, we roughly understand the relationship between BSR and sales for each site and each main category.

3. By correlating daily sales with daily BSR, we can preliminarily predict daily sales based on the average daily BSR of an ASIN.

4. By multiplying the average daily predicted sales by the number of days in the month, we obtain the monthly sales for that month.

5. Additionally, we consider the historical sales trends of an ASIN and adjust the accuracy of sales forecasts as time progresses.

It's important to note that if a listing's child variations share a parent BSR, the sales of each child variation are the same as the parent's, meaning their sales figures will appear similar or very close.

During the sales forecasting process, the following anomalies may lead to deviations in sales forecasts:

1. Because BSR is based on order volume rather than sales volume, orders containing 10 items and those containing just 1 item have the same impact on BSR. Therefore, if users frequently purchase more than one item of a certain product, there will be a significant deviation in sales forecasts.

2. If drastic changes in BSR occur due to promotions like flash sales, for example, jumping from 100,000 to 2,000, the daily average, which is around 20,000 (calculated in four time slots per day), becomes less accurate.

3. If a product is out of stock but still has a BSR steadily decreasing with value, the system may predict sales for that day when there are actually none. This issue is partly mitigated by monitoring BuyBox.

4. Due to delayed data monitoring, some ASINs may not be monitored daily or multiple times a day, resulting in no BSR for that day, and only recent BSRs can be used to predict the daily average.

5. Certain minor categories, like Tablet Stands (searchable as "ipad holder" on Amazon), typically lack main category BSRs, making prediction based on main category BSRs impossible; if inadvertently included and predicted, sales estimates might be wildly inaccurate, and we continuously strive to eliminate such bugs.

6. A sudden overall increase in sales for a category, such as during Amazon Prime Day and Black Friday, can cause daily sales to surge compared to the previous month, making predictions less reliable.

7. For top-selling products, especially those with BSRs within the top 500, prediction errors may be larger. This is because BSR is rank-based; for instance, for the top 10 products in a category, rankings are generally stable, but if an ASIN's BSR rank moves from 8th to 5th due to several days of daily sales increasing from 300 to 1000, actual daily sales increasing from 300 to 500, the BSR rank changes accordingly.

8. If an ASIN's category suddenly changes, such as from Beauty & Personal Care to Health & Household, sales predictions may be inaccurate for several days.

There are many other factors, at least another 20 could be listed. Therefore, when examining sales figures, it's advisable to consider historical trends, which are likely to be more accurate. Many experienced sellers have stated that they never focus solely on the monthly sales of a specific ASIN; instead, they consider historical trends and the overall sales of such products.

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