Walmart is in the process of creating the biggest private cloud in the world with the power to process 2.5 petabytes every hour. And why shouldn’t they? After all, the worldwide market for big data analytics may be worth almost $100 billion by 2020.

But this also means that big data has attained critical mass, and a revolution is underway when it comes to how businesses gather, store, retrieve, and process data.

The latest developments in processing speeds (Qualcomm 845, I’m looking at you), storage costs, and compression technology means that retail businesses now have the opportunity to combine everyday tasks with big data analytics.

A New Approach to Big Data

Contextual data, especially the lack of it, has always proven to be a major hindrance to big data solutions. Video is the only media format that is capable of advancing these solutions, and retail executives and business managers are finally beginning to understand the same.

As a result, they are now attempting to mix big data analytics with context-rich video to get actionable insights into real-world business issues. For example, most retailers now enjoy access to various data search, visualization, and mining tools that enable them to recognize outliers and patterns in data.

This lets them safeguard their business and increase the bottom line by investing in innovative marketing campaigns and improving operations.

What ExtremeLocation Teaches Us

For example, ExtremeLocation is using contextual analytics and big data to engage in-store visitors, optimize the layout of their store, and improve revenue channels.

The program was inspired by the growing number of consumers who use smartphones during shopping in brick-and-mortar shops and added mobile touchpoints along the purchase journey.

Parent company Extreme Networks, Inc. wanted to provide retailers with a unique experience where they could identify, engage, and offer personalized guest experience to their in-store shoppers through mobile gadgets.

By gathering contextual analytics from both BLE beacon technology and WiFi, stores and retailers can better understand what the interests of their current crop of visitors are, what sort of behavior they can expect, along with their current in-store location.

How Does This Help?

As a result, store associates can directly engage customers in a better way, the workflow and store layout gets optimized, and sales are greatly increased. The contextual campaign engine even allows retailers to engage shoppers via personalized push messages.

This way, thanks to the ownership and access of their own set of shopper-centric analytics, brick-and-mortar retailers now have a chance at entering the same playing ground as online-only retailers by tracking online behavior as well as in-store activity. In the end, they manage to improve both customer and brand loyalty while enhancing in-store sales.

What Benefits Do We Get From Contextual Analytics?

Contextual analytics are used in the conversion of in-store surveillance videos and other sources of data into accessible and meaningful information that enhances assist and security management and help businesses make more analytic-based decisions. RetailZipline, for instance, delivers the power of integrated communications bundled with contextual analytics, enabling retailers to achieve more accurate reporting, accelerated workflows, and operational enhancements.

Also, when it comes to marketing, the retail industry is starting to utilize contextual analytics for protecting against external and internal shrink. The combination of in-store video surveillance with event data like POS, loyalty cards, inventory, and transactional significantly improves the data value from an existing business cost.

Facilitating Big Data Handling

There are plenty of technologies being used nowadays that promote public and point-of-sale interaction locations, such as activity alarms, facial recognition, programmed events, perimeter tracks, inferred monitoring, license plate magnification, emergency service alerts, access control technology, and shipping GPS. Individually, each of these advancements offers a lot of timely and valuable business details.

But when combined with one another in the contextual analytics program, the result is a powerful tool for business that is capable of both predicting as well as resolving business activities in the future. This program is also quite important for multi-unit retailers since it helps with the management of distributed data sources from one centralized source.

Conclusion

Contextual analytics and big data technology are already being used by retail markets but has plenty of potential to grow. If businesses take the initiative to ensure efficient store communication and task management to combine disparate data sources, then they will be successful in taking the next step forward towards business intelligence.