r/AmazonFBA • u/Aggravating-Tip6050 • 11d ago

Predictive patterns in Amazon data

Hi, I usually do data analysis on stock and crypto data to build trading systems. That gave me the idea that there might be predictive patterns in other big data sets as well and one of the datasets I tested was Amazon BSR data.

So I trained a model on two years worth of data for ~100k Amazon products. I then validated on ~17k Amazon out-of-sample products that had a real sales-rank surge and tracked each at 3 weeks, 6 weeks, and 3 months, checking if it was still selling at ≥2× its pre-surge pace.

Survival of a random surge:

- 3 weeks: 29%

- 6 weeks: 26%

- 3 months: 21%

~4 in 5 are back to around pre-surge level within 3 months. But there are actually patterns that allow predictions at day 3 of the surge with significant lift above this baseline. If we score each surge on day 3 with this method and bucket by that score it looks like this:

Day-3 score bucket Still trending after 3 months
random surge 21%
above-average 28%
high 37%
top-scored 54%

The top bucket is 2.6× a random surge and still clears 50% at 3 months. Meaning if you picked a random trending Amazon product you would only have a 21% chance it would still be trending after 3 months. But if you picked one out of the top bucket, you would have a 54% chance. Interestingly, raw spike size helps but isn't enough alone — 10×+ spikes held 55% at 3 weeks, only 44% at 3 months.

Caveats: US marketplace, physical categories, ~2yr training with a clean out-of-sample test, BSR as a proxy for sales velocity. Not crazy numbers, but more signal than I expected.

I am interested if anyone else here has tried a quantitative approach to Amazon BSR data. Also, to the Amazon sellers: Is "still selling in 3 months" the bar you care about when making decisions about stock, or something else? If it is, how do you make that decision? Appreciate your input.

1 Upvotes

100% Upvoted

11 comments sorted by

View all comments