One way is to review data collected from the point of sale, club card loyalty memberships, and social media channels aligned with the analytics arm
Recent trends indicate social media, mobility, analytics and big data actively defining and changing the retail landscape. With multiple channels, both online and offline retailers are looking to provide group and individualized offers that can be delivered anytime, anywhere. Informed and discerning, consumers are willing to use every tool to decide on quality, price, and convenience. In such a scenario, companies are increasingly recognizing the critical role of IT in their operations to leverage big data and provide a competitive edge.
How IT identifies Big Data
Big data analytics is the foreseeable next step in the evolution of retailers –pulling granular details from data to be able to make better decisions. Granular level data includes unstructured information from sensors, devices, third parties, web applications, photos, videos and social media. Retailers need to equip themselves with technologies that will help them tackle the large volume, variety and high velocity of big data being generated. The dynamic nature of the data helps retailers to learn about new trends and patterns and possibility of growing demand for a product. It also creates an ability to tap unforeseen opportunities, and implement targeted campaigns.
Retailers leverage big data to enable operational efficiencies, standardizations, cost savings, leading to better performance and higher ROI.
- IT team at Tesco reviews data collected from its analytics arm, Dunhumby along with sources cited.
- The team crunches this data to build insightful business models that tell us how we need to be selling and marketing in the future.
- It helps to understand the buying behavior of a customer thus giving a chance to customize products and its prices for customers.
Another example of where we leverage data is in the of performance efficiency of our stores. We collect in excess of 70 million refrigerator-related data points coming off its units and feed them into a dedicated data warehouse. Those data points are analyzed to keep better tabs on performance, gauge when the machines might need to be serviced and do more proactive maintenance to cut down on energy costs.
Another aspect is the use of video analytics which tells Tesco if certain product is completely depleted, near-end or full and fresh. For instance, we take millions of images from cameras looking down at the produce department. It gives us an accurate picture and the results of the analysis are played back into the ordering systems. Inventory systems only tell us what’s in stock..
How it works
However, when customers walk around and only see two heads of broccoli, they probably think it is out of stock. We don’t want to be left with just three heads of broccoli in the produce tray. This is why we use video analytics. This cannot be done manually. We also continuously update the replenishment sophisticated models to incorporate sales history and weather to only order what is required.
Challenges with big data
Some of the technical challenges to big data adoption include choosing the right technology fit for processing and analyzing the information and providing timely analytics and intelligence. It includes coming up with extensible and scalable architecture to support current and future needs economically. Another challenge is the consolidation of data at organizational level rather than limiting it to internal business unit level. These challenges are generic and are applicable in almost all industry segments though not at the same level.
Besides the data warehouses, ETL technologies, Tesco has Hadoop platform within which the company is planning to trail out different use cases of big data for retail such as personalization, energy and maintenance analytics and for operational efficiency.