Applications of Big Data in Retail, Informatics for Technology LLC | Oman
New Technologies

Applications of Big Data in Retail

In the world of retailers, there’s a lot of obstacles they face  every day from managing their business, getting customers, and improving the business.

From a customer point of view, the needs are finding the wanted product fast, having a good customer experience, and getting through the checkout process without any complications.

In all of these operations we need data to enhance each one of them, but not any kind of data, we need Big Data.

The reason for needing big data is that the typical data can not help in these complex tasks. For example, if we want to have a good customer experience, we need to identify patterns, know how customers interact with our website, and more.

We need big data in order to help in the product recommendation system and show products that the customer might buy the most. And if we’re in a big market, we need a way to stand out between competitors, whether it’s the dynamic pricing system, the services we provide, etc.

The world of retail with Big Data

Every business wants its employees to take decisions confidently and understand their customers deeply. They can do that by taking advantage of the power of Big Data in taking such decisions to do that.

Here are some real world use cases that apply big data technology in retail field:

1- Customer Behavior Analytics

All online retailers are struggling to find what makes the customers leave their website without purchasing. Is there any functionality problem in the website? Are the buttons for purchasing and browsing need to be bigger? Can the customers find their wanted products easily?

Like these and other questions, they’re falling under the customer behaviour analysis. That’s why it’s important to know how the customer thinks and how he interacts with the store. 

The big data concept comes when we analyze data through many interaction points together at the same time. As the customer interacts with the business through mobile, social media, online stores, and more, that increases the complexity and the variety of data collected through all these channels.

That’s why big data is a very important part in a lot of businesses that want to be ready for the future.

2- Personalize the in-store experience with big data

For a lot of customers, they want to visit the physical store in order to see the product in real life and then take the decision of buying online later. 

By personalizing the in-store experience for customers, the business can drive more sales and success. In order to do that, data engineering plays a good role in turning all of those data from nonsense into helpful data that can increase the effectiveness.

These data can be gathered from websites, mobile applications, in-store sensors, and cameras.

3- Increasing conversion rates

Through our helpful data insights, businesses can target customers effectively with promotions for example. 

This data collected is not small as you might think, it’s a 360-degree view of the customers in order to understand their lifecycle, what they do, their habits, and more.

4- Customer journey analytics

For marketers, there’s a famous model called AIDA which represents the customer lifecycle through Awareness, Interest, Desire, and Action. 

In the real world, the customer doesn’t follow this cycle exactly as he/she might see a photo of your business on your Instagram account, then goes to your Facebook page in order to know more about you, then visits your physical store and see the products, and lastly take the purchase action online.

That’s why we need big data to know more about the customer journey.

5- Supply chain management

For a lot of retailers, the supply process is a little bit challenging. They may supply a huge amount of a certain product then only a few people buy it.

In this complex process and with the help of market conditions and other kinds of data, the big data and data engineering teams can do a great job providing good insights and predictions.

References

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