Optimizing Prices through Data Science
If you’re reading this blog, you are here to find how advanced tech solutions are used for price optimization and why the future belongs to human made ML driven decisions.
Following is the question every retail team dreads the most- What is a fair price for this product considering the market, current time of the year, demand and physical attributes?
What’s wrong in answering the above question? The factors! They are constantly changing. If we take in consideration just the e-commerce Market, the above factors change in a matter of minutes. Hence the reason why prices are changed almost a million times in a day on any leading e-commerce platform.
But what about the retailers that are not as large as Amazon or Alibaba? They often use the traditional approach to pricing thus relying completely on human powered decisions.
Take for example two medium sized retail outlets- one which uses technology to estimate the correct price for a product while the other asks its managers to set a price.
In the latter case, more or less only 3 pricing or non-pricing factors are taken into consideration by the manager- Cost, Competitor and Business KPIs. In contrast to this the first retail outlet’s ML Algorithms consider up to 60 factors while deciding a price- Consumer Behavior, Brand Perception, Elasticity, Seasonality, Positioning, Advertising Channels and much more.
What’s the loss then? Bad pricing decisions cost businesses big money. Most don’t even know how much they’re losing due to bad pricing.
And the solution?
That’s Rubikon Labs for you!
What we do at Rubikon is a systematic approach towards estimating a near perfect price for your product. Our Machine Learning principles utilize complex algorithms in order to consider a myriad of factors and come up with the right prices for thousands of products.
Here’s the process that Rubikon Labs follows to improve your pricing strategy-
Gather Data => Define Goals and limits => Choose an algorithm =>Modeling and Training => Execution and Adjustment
It’s true that the Machine Learning models are not highly accurate, but it’s also proven that you can determine the level of accuracy that’s appropriate for your needs. Do you need to be 99% certain of a conclusion or just 90%? Adjustable confidence gives you more options.