I send out neat stuff like this all the time. Come join the party!

We’re not even going to try and bury the lede on this one. It’s been a busy spring and summer, and I finally found time to sit down and review Jungle Scout’s claims about being the most accurate product research tool.

DECEMBER 18, 2019 UPDATE: Jungle Scout’s 8 Power Features make it a force to be reckoned with. Missed the announcement? Here’s the entire list.

When it comes to finding the next big thing to sell on Amazon, determining what’s worth pursuing and what’s total trash can be a daunting task. There are literally over a hundred million listings already in existence on the platform, and many more surface daily. It’s no wonder that tools and services come in to fill the void between idea and reality, otherwise known as where the hell do I even start?


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The tools market is a crowded one, too. Helium 10. AMZScout. Viral Launch. Sellics. Sale Maker. Scope. Unicorn Smasher. Fire Sale. Zon Guru. There are so many names in this space. You might not even have noticed that two of the tools I just listed don’t even exist.

Jungle Scout is a player in this game, too. There’s room for everyone if the number of sellers on Amazon’s platform is any indicator. Participation requires being at the top of your game at all times. Sellers demand data that are not only rich and actionable but accurate. Imagine logging into your bank account online, and it says you have $39,221. In reality, it holds $96.

You’d be upset if you found that out. We should be holding our selling tools the same standard since, in a way, we’re also dealing with money: whether we make it or lose it based on the product decisions we make.

I started using Jungle Scout a few months ago. My natural curiosity about the entire Amazon landscape wandered and drifted into the mysterious and intense neighborhood of private label selling. On these streets, cheap Chinese goods can run rampant, and everyone and their mother is selling some basic kitchen knick-knack. It’s a menagerie of opportunity and sadness for those who come prepared and those who don’t.

Jungle Scout’s claim to fame is an end-to-end solution for product and niche research, supplier sourcing, and product launching/marketing. The web app holds many of the keys to the kingdom, and the Google Chrome extension is the purveyor of summarizing live, in-the-trenches search page data. Between these two components, Jungle Scout is far from being a miser of analysis and opportunity management.

Covering this much ground means the results it surfaces have to be damn straight on. Spoiler: Jungle Scout is the most accurate product research tool by a mile from the biggest names out there. (I don’t know what the conversion rate is for metric statistical distances, data meters?)

Jungle Scout’s 2019 accuracy case study dropped at the end of May, and the summary is fantastic to read. The most recent iteration of this yearly study discusses the sales prediction error rate of a group of 349 ASINs across a fleet of mainstream and popular competitors in the same space.

The goal was to get as close to zero as possible when evaluating the deviation between theoretical to actual sales numbers for a given tracked product. We’re going to get a bit math-dirty, so hang on. It’s worth mentioning that this summary data point should be a median number because we’re dealing with a range that has a strong potential for outliers. Averages can skew because of outliers, whereas medians are outlier-resistant. There are some listings in the study that were much lower or higher in both directions. Confused yet? Here’s how this breaks down:

  1. Collect the actual and predicted sales data for each ASIN in the study.
  2. Establish the difference as a percentage using |((s1-s2)/s1)|s1is the actual sales volume and s2is the predicted sales volume. The farther away from actual-predicted parity, the farther the error rate is from zero.
  3. Get the median value of all the error rates in the data set (using {(n + 1) ÷ 2}th).

This data and the results that surface come in two forms. The first is the median graph, sorted:

The accuracy percentage of jungle scout in relation to other competitor tools
source: Jungle Scout

I’d argue this is an excellent representation of the overall picture of the accuracy between products, but we can go deeper than that. While we’ve tried to avoid outliers and instabilities by displaying a single median number, there’s a better way to summarize this data that includes outliers.

The above box-and-whisker plot illustrates several things:

the accuracy of jungle scout in relation to other competitors
source: Jungle Scout
  • Blue: the outliers (and how far they drifted from zero)
  • Orange box: The range between 25th and 75th percentiles (the smaller the box, the more accurate overall).
  • Orange line: the median (from the previous graph).

I wanted to share this graph specifically–even if it’s a bit confusing to some–because having a visual representation of where each ASIN landed on the scale is essential. Think of each column as an oddly rectangular dartboard, and you’re picking members for your strange, new dartboard team. With all the darts thrown, it makes the most sense that the best throwers have the most darts closest to the center. No one would be interested in building a team full of people that aim low all the time or are all over the place, right?

Now that we’ve deviated (sorry) into and out of math let’s come back to why this is important. The same set of ASINs was tracked using the other tools, too, (you can see the entire raw dataset here). With all other things being equal, every other service on this list gave more inaccurate estimations than Jungle Scout when it came to predicting sales. Some were off by massive margins.

No tool is flawless by any stretch of the imagination. Amazon keeps sales data and algorithms a closely guarded secret. The best option we have is to track sales rank and actual order history for products. Jungle Scout’s data scientists toil over those metrics, and their effort manifests in the form of being the most accurate when compared to the competition.

We haven’t even talked about price, though would it matter? Coming back to our quadrangle darts team, you could pay less for an inferior member. Still, there’s a significant chance the team may never make it to the finals because that low aim problem is never going away.

It sounds like a strange comparison to say that predictions and niche analysis accuracies are like one’s ability to throw darts at a pillar-shaped dartboard. It may very well be, but picture each ASIN sales estimation like an attempt to hit that bullseye. Since no tool will ever be perfect, it stands to reason that the next best method of measurement is the least wrong.

Some would argue that product research tools like Jungle Scout are just one piece of the puzzle, and there’s so much more involved in finding a quality product. Those people would be right. I’ve also heard those words come from representatives of comparable tools with much higher inaccuracy ratios, too. Do your research and evaluate your needs, but never forget data accuracy and the mechanism that provides it.

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