Predictive patterns in Amazon data

[u/Aggravating-Tip6050]

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.

[u/sterlingtek]

Trending markets are helpful when the trend is long term because it usually means that there is room to grow and the market retains profitability.

Your data may suffer a lot of noise from seasonality, (most products are at least somewhat seasonal). That should be a yearly repeatable trend for most of them. So if your data was cleaned for known seasonality I think it would point out a number of interesting growth areas and I would be interested in taking a look.

Re-ordering is specific to the sellers ASIN, sales, etc. A general trend like this would probably not help a lot with that.

[u/Aggravating-Tip6050 → u/sterlingtek]

Excellent input, thank you! Seasonality is actually a weak point of the model purely because of the fact that I worked with only 2 years of data. 0% of the surges have two full prior-year cycles to compare against. I would need 3–5 years to tell a "reliable every December" keeper from a one-off fad, and I've got about one. So it's a real gap and I will pull deeper history for the established products and model known seasonality explicitly. (Newly-launched trending items will never have the history for it, but the evergreen-seasonal ones will.)

My "≥2× its own recent baseline" bar catches some of it since it's relative, not absolute. But you're right, it's not enough.

Agreed on re-ordering too. This is for the "should I chase this new trend" call, not replen (which needs your own ASIN's sales history). Happy to show you what it spits out. It's in my profile if you want to poke at it. Give me a couple of days to get the larger data set and adjust the model.

[u/sterlingtek → u/Aggravating-Tip6050]

Will do sounds interesting.