Opportunity Cost Analytics

Opportunity Cost of Not Implementing Actionable Analytics

Granulytic - Actionable AnalyticsTraditional analytics often fall short for online retailers. Offering a glimpse of the activity from days or weeks ago, most solutions leave it to retailers to make sense of and derive their own actions from the data. Recently solutions have emerged that take the guesswork out of interpreting analytics for online retail. Most ask what it costs to implement such a solution but the better question is, “What is the cost of not implementing actionable analytics?”

A great use case for demonstrating the opportunity cost pertains to search. Let’s suppose you are an online retailer with more than a few SKUs in your catalog and thousands of people arrive at your storefront each day and search for various keywords. Most of those keywords will return a list of products or content that relate to the keyword. However, some won’t return anything and that could be a lost opportunity.

Capitalize on Trends

Shoppers will arrive at your site due to various reasons: A google search, social media recommendation, advertisement, or even a national or regional event that spurs interest in your products. Many of the more popular trends are short-lived and should be capitalized on as they are happening.

Knowing when these trends are occurring and exactly how to respond is critical.  If your site suddenly receives thousands of searches for a given product, where should that product be located on the site to get the most traction? If a product is taking up valuable space on the home page or category page and isn’t producing as well as another product, should a swap be made? Are there products that shoppers think you should be carrying and you’re not?

Granulytic solves these problems by using proprietary algorithms based on predictive models that provide insights into trends as they happen. It’s real-time, actionable analytics, specifically designed for the merchandiser. And it won’t break the bank.

In fact, Granulytic pays for itself after as little as a 0.1% increase in orders in most cases. And catching problems related to search, product placement, category listing, product bundling, and more, could increase orders substantially.

Sound like magic? It isn’t. It’s Data Science, Simplified.

Leave a Reply

Your email address will not be published. Required fields are marked *