Unless you’re the Tiger Woods of online arbitrage, you’ve probably at some time or another found yourself overwhelmed with data points, perhaps sometimes paying attention to metrics that don’t matter as much as others, or just getting confused with all of the potential futures of a product under consideration.  In the video above, Tiger Woods calculates a whirlwind of data points, but still narrowly misses a hole-in-one.  He laughs it off saying, “Haha – I forgot the earth’s rotation.”  Even in golf, this sort of approach is a myth.  Let’s call it the Myth of Perfect Data.

The Myth of Perfect Data suggests that if you can just get all of the right data points gathered together, calculated properly, then you can perfectly predict the future.

Don’t get me wrong.  Data is wonderful – essential.  Data is our core business here at OAXRAY – we provide high quality data to help sellers make the best decisions they can.  High quality data being crunched properly has fueled some major leaps forward in sports (think Theo Epstein’s data driven approach to transforming the Chicago Cubs), finance (see high frequency trading – ok, maybe not a leap forward for society, but profitable for those taking action on data), eCommerce (think of how much more invested Amazon is in data than its competitors) and nearly every field of human endeavor.  The extraordinary predictive powers of Nate Silver’s 538 organization alone are enough to make one a believer in the power of data to predict the future.

Yet, Nate Silver, the high-priest of data crunching and predictive analysis, has himself criticized the idea of “perfect data” and in his book The Signal and the Noise, advocates for a kind of “practical statistician” who has some intuition of what metrics may be most important, what the problems may be with various types of data collection, and what level of uncertainty is inherent in predictive analysis.  In other words, some data is better than others and we should pay attention to the right data and ignore or put in perspective the rest of the statistical data.

In our case, we’re talking about statistical analysis of products and trying to use that statistical analysis to predict the future performance of products.  If we reject the “Myth of Perfect Data” as I suggest that we should, what then should our approach be to helping us cut through the “noise” (irrelevant or under-relevant data) to get to the “signal” (useful data)?

I would suggest that even professional golfers don’t actually take the “earth’s rotation” into account when they are talking to their caddies about their next shot.  I’m not an astrophysicist, but I doubt the earth’s rotation even varies that much, if at all.  There is likewise, lots of data one can take into account when considering an Amazon purchase and some of it is less relevant than others.  We’ve designed OAXRAY to help you cut through the data.

The short answer to the question, “What do I do when the data overwhelms me?” is:  Cut out the “noise.”

OAXRAY’s tools help you overcome “analysis paralysis.”

What Amazon related data is “noise” and what is “signal” is probably the subject for another full post, but for our purposes, let’s just say that: it depends.  It depends on whether you’re going for quick flips, long-term holds, buying for Q4 or another seasonal period and any number of circumstances unique to your selling situation.  But for now, let me take you through the many tools OAXRAY has to help you cut back on the “noise” leaving only the “signal.”  And remember, sometimes the “signal” tells you what to buy, other times it tells you not to buy – that’s useful too.

1. Hide Columns – OAXRAY gives you lots of different data points – some from the source store, some from Amazon, some from stores you did not scan, and some from Keepa.  Each of these is contained in a column.  If you find one of them to consistently be a distraction (read: “noise”), then hide it.  Just hover over the column header, a hide button will appear – press it and voila, the column is gone.  You’re that much closer to getting to the “signal.”  You can reset the columns with the “Reset Columns” button.  Check out Ted’s video on it here.
2. Sort Columns – Any of the columns that contain numeric data can be sorted by clicking on the column header.  Want to sort the other way, just click the column again.  Perhaps you’re in a situation where the weight of the product is more important to you, sort the column by weight – that is “signal” data to you, whereas to another seller, it is “noise.”
3. Filter Columns – At the top of your main tab, there are three filter boxes – Rank, ROI, and NP (Net Profit) – enter a rank that you want all products to be under, an ROI % (as whole number – so 30% ROI would be 30, not .30), and a dollar amount that you want to stay above on your net profit.  Be sure to check the boxes you want to apply, then click “My Buys.”  This can cut out noise quickly.  Sometimes revealing that there is no signal.
4. Quick Buttons – Three different buttons you may find useful here for sorting through the noise quickly – “Positive ROI,” “No Match Found,” and “Currently Unavailable on Amazon.”  Filter out too much data?  Just use the “Show All Listings” button.
5. CSV Export  – Want to do some statistical analysis not available on your main tab?   Just export the data to a CSV file and open up in your own spreadsheet.  Perhaps you want to assign your own “weight” to different data points and come up with your own personal algorithm for rating products.  Rather than a flat net profit cut off at say $5.00, you may want to require items under this threshold to have a much better 90-day sales rank average.  It’s all possible with the CSV export.
6. List Making & CSV Loader – Perhaps you have a VA sourcing for you or you’re limited on income and want to buy the very best product possible – keep a list of possible products as you source and when you’re ready to make a decision, run the list through the CSV Loader and make a buying decision.  You can also use the buylist for this very thing.  Also: Watch Ted’s CSV Loader video.
7. Buylist – Save items you want to buy later, re-evaluate later and more.  And yes, another video.

When the data overwhelms you, use these tools to cut back the “noise” and see what’s left – there’s a good chance there is some “signal” there and the remaining data will be very easy to act upon.  Happy sourcing!