5 Must-have Things to Include in your Retail Business Analytics Platform

If you are pursuing a data analytics course in Bangalore, identifying the top trends and challenges in retail marketing can help you build your project in the curriculum. This article has been crafted to assist you with must-have features that you should focus on to build your retail analytics platform.

Retail marketing and business analysts are on top of the industry that has seen a rampant rise in the way data is collected, analyzed, and used to make services and products better. One of the chief consumers of data analytics in the retail eCommerce marketing team that is using the plethora of data related to stock prices, product reviews, recommendations, and shopper behavior patterns, extracted from diverse sources like social media, video advertising, digital ads, and brick and mortar stores. There seems to be more information available than human managers can possibly consume! What to do with the heaps of information in such cases?

 Look to technology and let it do the job. That’s where data analytics within retail business comes into the picture.

Here’s how–

Competitor Intelligence

What the competitors are doing in the market with their products is information not many can derive from just looking. It needs a solid knowledge of data science and competitor intelligence. While it works just for them (your competitors) like it does for you– so protect the most vital piece of data you leak out to the market. The easiest templates you can use to track competitor performance could be, the number of matching products, price ranges, discounts offered and the number of days it takes to deliver, and other policies.

On-trend Analytics

What is shopping most likely to buy? What draws Gen Z shoppers to a certain kind of product? Why do Pet Feed products do just as well in E-commerce as electronics and merchandise?

These are some of the key pointers you can design in your retail analytics platform. Segmenting the products based on their reach, sale and popularity can really make any retail marketing team stand up and look to multiply the outcomes. On-trend analytics also ensure that the Supply Chain stays strong and the “Out of Stock” pop-ups don’t cripple the user experience during the mega sales seasons!

Product Recommendations

Automated product recommendations based on Machine Learning is rocking the industry with its relevance and time focused targeting. Yes, it’s true that shoppers are most like to check reviews and ratings before putting their cash into the product.

What is the real goal of using retail analytics?

The number one target is to increase sales. Second, the e-commerce company should be able to reduce the number of abandoned carts and failures at checkout / payments.

That’s why it’s important to optimize product recommendations based on popularity, price drops, and discounts available. Product recommendations is possible only when you have Big Data on your product inventory, catalog, and optimize user experience data to suit how they fit into the overall business process.