ViableView's Market Analysis Data: Explained

A Data Revolution:

Let’s explain what the ViableView data products do and how it all works in as simple terms as possible.

A good place to start would be our flagship product, the Opportunity Finder.


So what does the Opportunity Finder Do?


Simply put - It informs entrepreneurs to what markets and niches have the most profit to be made in - often ones they didn't even know existed.

An opportunity or niche is a group of products that are all similar to each other and would normally be purchased by the same specific type of buyers.


What are some of the clear indication metrics we project for users so they know what niches are best?
  • Opportunity Score
  • Competition Score
  • Monthly Max Profit
  • Monthly Target Profit
  • Monthly Revenue
  • Cost Margins
  • Profit Margin %
  • Historical Trends

How does that work?

We collect a-lot of little pieces information and data on products and services that people sell.

We then take all of that data and run it though what we call a Viability Simulation.

So we simulate (replicate the operation of a real-world process) to see how much profit and at what margin you would make if you advertised a product in the current market condition. We then output indication metrics to gauge if it is viable (capable of working successfully; feasible).

The Viability Simulation ultimately takes a-lot of messy un-usable data and turns it into actionable insight for entrepreneurs via the indication metrics mentioned above.


The fundamental principles behind this are:




Identifying the niches/products that have the highest demand and the lowest supply - and/or those that have the lowest Cost Per Acquisition (CPA)  in relation to the highest life time value (LTV) in the biggest serviceable addressable market (SAM).



This all starts with product data, which  amalgamates to market data.




Let’s reverse engineer a bit of how it all works starting with some of the raw data we collect and process for products are:


  • Average Order Value / Life Time Value
  • # of Sales / Users
  • Revenue

  • Cost Per Click

  • Conversion Rate

  • # of Reviews
  • Estimated Margin


All of these data points are collected on a recurring bases with enables us to simulate and chart data over time along with make predictive models and identify and account for outliers.



Some of the outputs we are able to leverage from that data for each level of the market that play a critical role in the final indication metrics are:


  • Total Rev
  • Total Addressable Market (TAM)
  • Serviceable Addressable Market (SAM)
  • Ave Cost Per Acquisition
  • Ave Cost Per Customer
  • Ave Profit Margin %
  • Ave Cost Margin %
  • How long sellers have been selling
  • Number of sellers
  • How much of the market sellers are capturing
  • Sellers performance overtime



How do we get our data?

We collect our data through a wide range of sources, predominantly from publicly available sources and proprietary algorithms. The more data we collect over time, the more accurate our projections become.


Are your projections 100% accurate?

Our projections are based on an ideal set of market conditions and are weighted to the better performing sellers. Therefore, our max profit derived from our industry average KPIs for opportunities, insinuates that all aspects of the transactions will be most similar to the best performing sellers.That being said, a huge aspect of the the ability to achieve our projection weighs on the variable of the sellers actions.

Our other products:

Our other data related products such as the product and keyword finder, tracker, and market overview still leverage all of these same aspects, the core differentiators are the ability to analyse more granular or broad.