Business intelligence has seen unprecedented growth in the retail industry, as brands focus on equipping their employees in every department with one-tap analytics. Several channels feeding big data analytics solutions have evolved, while some new data channels (like IoT) have also entered the data-gathering scenario.
Here’s a quick look at the trends dominating retail analytics in 2017:
- Advanced analytics for everyone
Gone are the days when only specialized data analysts sat poring over huge data sets trying to derive trends that influence the market and consumer behavior. Nowadays, any shop assistant can conclusively remark upon the best selling item by supporting it with number-of-items sold per region figures. Retail brands are shelling outrageous amounts of money to get the right big data analytics solutions that have an elaborate dashboard that provides real time insights at macro and micro levels.
- Mobile analytics has reached maturity
The analytics dashboards are no longer limited to desktop viewing. Tailored dashboards for smartphones and tablets are fast becoming the norm in everyday working of employees across departments like storefronts, warehouses, logistics, etc. The major roadblock in mobile analytics apps was removing the information overload and just showing the necessary statistics on screen to prevent mobile analytics apps from becoming awkward replicas of their desktop versions. Actionable insights are now available even on the BYOD phones and tablets of brand employees.
- IoT helps achieve data accuracy
Indoor store navigation using beacons is no longer just a consumer-centric facility. It is the mechanism to analyze consumer behavior as they walk down aisles in malls and supermarkets. It is also the best way to track sales and inventory requirements, as seamless connectivity is ensured by Wi-fi availability, smartphones and Internet-enabled devices like beacons, RFID, Wi-fi based sensors. The information gathered from these exercises (which aisles do consumers frequent most, which items do consumers check out most, etc.) is then used to form marketing strategies and bring about store layout/display changes.
- Omnichannel data integration is the key
Offline analytics are of little use to retail chains. Real time agile analytics are the need of the hour. Every data set available from every channel needs to be updated by the second to achieve this. Hybrid hosting environments (cloud+ on-premises) pose a major channel in implementing omnichannel data integration. Using the right big data analytics solutions, successful retail chains can supply their employees with the right data sets at the right time, be it commerce-channel data, supply-chain data or customer data.
- Robots in Retail Data
Mundane tasks like conducting surveys, educating the consumers about ongoing promotions, and taking physical inventory will be increasingly delegated to robots that will serve as data touch-points, so that the employees can focus on other tasks that require intelligent analysis. While the in-store hospitality will not be affected, as employees will always be available to help the consumers, the consumer-service centric data will be gathered by robots. This has already been implemented on an experimental basis in many reputed retail chains.
- AR and VR boost sales analytics
Augmented Reality and Virtual Reality have long been introduced in the retail industry on an experimental basis. However, 2017 will see the actual adoption of these technologies in helping the consumers visualize how the merchandize will look on them (clothes/accessories) or in their homes/offices (furniture). Taking the guesswork out of the purchase cycle enables brands to convert off-hand enquiries into actual sales at the time of the first consultation itself. Also, the in-store and larger inventory can be shown to the consumer in a visually appealing layout using virtual reality, to elicit interest in items that are currently not physically on display.
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