Which came first, the chicken or the egg?
This is a philosophical question that can be applied to many life experiences; Amazon selling being no different.
Ask any seasoned Amazon seller veteran about the early days and they will tell you that when they started, they didn’t need all the fancy software and tools available today. However, things were arguably easier for them then as well.
So, did the onslaught of competition come because of the leg-up software tools gave seller hopefuls, or did they simply meet the growing demand?
We may never know. One thing is for sure though; the space is forever changed because of them.
I remember when JungleScout first entered the scene. The first of its kind, the Chrome extension was simple enough, but boy did it make waves. All it really did was aggregate a bunch of available data and apply an extremely simple (at the time) algorithm to predict a likely revenue amount based on product BSR at that moment.
It was rudimentary, but revolutionary at the same time.
As sellers became more sophisticated, and competition rose, better data and competitor analytics were required. The next evolution of the “at-a-glance” product research and competitor analysis Chrome extension came with BSR averages to get a more realistic idea of revenue. After all, basing your estimate on a snapshot in time of BSR was going to produce a lot of unreliable results given how easily BSR can be manipulated these days.
These new tools not only applied intelligent equations to make the data more realistic but they also essentially plugged gaps in the data from Amazon, allowing sellers a more robust view of their market. And this is the caliber of tool we have grown accustomed to.
Ecommerce SAAS Technology Is Still Evolving
So, phase one aggregated the data. Phase two filled in gaps in the data. Is that enough for sellers today?
As sellers begin to run their Amazon stores more like “real businesses” and expand their offerings and distribution channels, it looks like it is time for a further evolution in the space. One that not only provides the necessary data, but also allows for “predictive analytics.”
The concept of using data to predict future movement has been around for quite some time. Every trader of stocks and commodities uses this type of strategy when they apply “technical analysis” to their trading charts.
By using simple moving averages, and calculating upper and lower boundaries of movement of a stock price, traders make predictions as to where they will likely be most profitable.
But can the same type of analysis help an ecommerce seller?
Well I ask….why not?
SixLeaf recently released information about its latest tool, the Phoenix Chrome extension powered by RISE. While it is unique and competitively accurate, the implications for its featureset, as well as the direction of the type of analytics it provides, is what shows the most promise (imho).
How To Apply Technical Analysis to Amazon Products
First, let’s look at the moving averages.
Many tools offer a simple 7-day average of BSR for 30/60/90 day intervals, but that would never be enough for a stock trader, and it shouldn’t be enough for you either.
Within the Phoenix Chrome extension, you have the ability to adjust your time period of analysis to whatever interval you choose. Further, you can compare a 7-day and 30-day EMA (estimated moving average). This way, you can look at convergence and separation of the two averages to better determine trends in sales. This is a powerful method of figuring the impact of seasonality or the strength or weakness of a competitor.
This kind of flexibility in technical analysis alone will put you head and shoulders above would-be competitors, however Phoenix has another trick up its sleeve.
Next we’ll look at BSR consistency.
This is a unique metric as it actually calculates the common “range” of BSR. Essentially it looks at the average of BSR, then upper and lower bands over time, to determine an expected “bandwidth” that illustrates how consistent a BSR is.
Why is consistency important?
Because it lets you see at a glance whether a listing is well established and stable or fluctuating in its sales volume. This is a great way to see if a listing is rooted in its place, with regard to rank and sales, or if you are dealing with a newer competitor that may be struggling through launch processes.
It is also a good way to determine the volatility of a market. Overall BSR consistency is a much needed metric for the intelligent product and competitor researcher.
Combine these predictive analysis tools with other metrics offered by Phoenix, such as review velocity, listing strength, and age, and you have a brilliantly robust view of a niche and where it is headed.
And that is what we mean when we refer to Phoenix as evolutionary. This amazing tool will usher in the next evolution of research software and raise the bar for SAAS products in our space.
If you would like to see the results Phoenix got with over 11,000 days and over 1500 products, click HERE!
If you would like to learn about the machine learning artificial intelligence that powers Phoenix, click HERE!