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Greg Mercer of Jungle Scout is with us to talk through the accuracy of Amazon Product Research Tools and his approach to assessing this using real data in as transparent a manner as he can find so people can make their own judgements.
- The accuracy of Amazon Product Research tools is the focus
- Case study with spreadsheets and analysis to assess tools
The Case Study
- Jungle Scout estimates the sales of any product on Amazon
- First one to come out with it and the algorithms for estimating have changed over the years
- The sales estimates are a key metric for users
- Have invested in the data science around this with our internal data nerd team
- How to prove the quality and accuracy of the algorithm
- How to make a bullet-proof case study
Real Data against Tools
- Got a bunch of Amazon Sellers to donate their data to JungleScout
- Letting JS log into their Seller Central accounts of 50 Sellers with real data
- Posted in FB groups and ads for people to join the sae study
- Purchased all of the competitor tools and went into each person’s account and ran the tools against the data
- Pretty hard to poke holes in this 80hrs of tools testing against the real sales data
- Hired VAs to go through and run all the different tools
- With the data we’re then able to show it to our customers
- Had 50 Sellers in total share their data to assess the accuracy of amazon product research tools
- Full breakdown of very detailed analysis
- 100% transparent as we’re 100% confident
- An overall error percentage with Jungle Scout coming out on top
Data Results
- Unicorn Smasher error percentage was 92% so about as useful as flipping a coin
- Estimating 1,000 units a month would really be somewhere between 80 and 2,920
- So a free tool with data that is so far out is hardly worth it
- Jungle Scout (25%)
- Viral Launch is 2nd (34.34%)
- Amaze Owl 3rd (44.29%)
- Helium 10 4th (46.44%)
- A re-cut of the data will be coming out again in Spring 2019
Tools Don’t equal success
- Having the tool does not guarantee success but gives better insights into what products to select
- Helps understand the demand in the market and niche
- Using Unicorn Smasher with such a high margin of error impacts your ability to be successful in that niche
The Spreadsheets that assess the accuracy of Amazon Product Research Tools
- You are welcome to use the data in any way you like to make your own assessment of the accuracy of amazon product research tools
- Started with 20 different graphs and analysis of the categories
- Decided the overall errors percentage was the best metric to help identify this
- Presented with PHD level analysis in the spreadsheets and also presented more for the layman as well
- You can do your own analysis of this data
- The median overall error percentage is the best metric we’ve come up with
Jungle Scout Improves Accuracy
- Going from 25% to around 12% on Jungle Scout
- So plus or minus 12% on the data
- Good to see Viral Launch in 2nd as a new entrant
- Nice to see competition and raising people’s games
- Jungle Scout has 3 x Full Time Data Scientists and one part time with 3 x PHDs
- About £1m per year just to run these systems for sales estimates
- The bigger players can invest more in this
Jungle Scout Supplier Database released
- Releasing the Supplier Database feature in Jungle Scout this week
- Import data into the US released through the Freedom to Data info
- To let you know who the best factories are for them
- Helps to find new factories and sources
- Anyone with a Jungle Scout subscription will get access
- Find out which factories your competitor is using
- Look for best rated product on Amazon
- Figure out their legal entity
- Search in the Supplier Database for the factories as you know they are high quality already
Factors to look for
- Quality is high
- Trusted supplier
- High quality product
- Pricing quotes based on the competitor’s sell price
- Speeds up the whole process and removes a pain point for Sellers
More Case Studies coming
- Data Driven case studies coming
- Launch tests on Giveaways, PPC, etc.
- Actually doing the launches and reporting the data back as case studies
- Do dispel myths and prove what works