Having been actively involved in what we call Web 1.0, it’s astounding to realize the changes that have taken place since first implementing a commerce site in 1995. Innovators in the space realized early on that getting shoppers to the right product in the fewest amount of clicks helped increase order frequency and volume. Technology developed around counting site visitors and analyzing logs in order to help retailers understand what people wanted. Since then, retailers have always strived to more effectively leverage data science.
In contrast to today’s technology, the 90’s seem like the dark ages — especially when it comes to the ability to collect and analyze data. Specialized roles for Data Scientists have emerged at retailers and many other types of businesses and their teams spend countless hours mining the available data and building predictive models that can better align the business with the target customer.
And therein lies the challenge those retailers outside the top tier. It takes time and resources to collect, analyze, and act upon the data being collected. So such a business has traditionally had two paths: Invest in a data scientist, or; accept the fact that they won’t be effective luring customers away from the behemoths. Given the pace of technology advancement in the past decade, another option has emerged that helps business of any size leverage data science without the traditional expense.
Data Science, Simplified.
When Granulytic was founded, we had one idea in mind. Data Science, Simplified. In our founder’s careers of eCommerce consulting, hundreds of companies were seen to struggle with applying business intelligence. It took them away from their core competencies, and introduced costs many were not able to bear. Gladly, we were able to conceive of, and implement a solution to help retailers of any size be competitive.
We call it Merchandising Intelligence. MI consists of consuming data from real-time interactions from shoppers on your site and using machine learning algorithms and complex data models, to determine exactly what actions need to be taken to maximize revenue. And given that shopper behavior can change, you need to be ready to respond with changes that match their current expectations.
Contact us to find out more about Granulytic and how you can maximize your revenue without breaking the bank.