What’s the Big deal with Big Data in Retail?
Consumers are undergoing a shift in perceived value, putting less emphasis on traditional “transactional value” based consumption, thereby changing their focus on more “personal values”. This greatly affects and led to a change in business and marketing strategy, moving from traditional mass-media, mass-marketing towards a more personalized one. To reach that goal, however, companies and retailers cannot continue to rely on their traditional ways of gathering data, i.e., market surveys and feedback forms. Big data, therefore, is increasingly favored as the “new” standard.
What is big data?
Big data, as its name suggest is a term that describes a large volume of data; structured, unstructured, random and noticeable. There 4 different steps before a company can fully make use of these data in business strategy namely:
Step 1) Collecting Data
Step 2) Storing Data
Step 3) Analysing Data
Step 4) Visualising and Presenting Data
The collection, storing, analyzing and presenting data is not a new trend. Traditional retailers have always been applying the original concepts of data collections – Volume, Variety and Velocity. Volume refers to the amount of data extracted, Variety refers to the different unique types of data and Velocity refers to the speed of the data collected (which therefore affects the relevancy of data). With the rapid advancement in technology, however, Big Data tends to now refer to the extraction of value from all the “noise and buzz".
The availability of datasets is rapidly growing. This is compounded by the increasing trend of Internet-of-Things (IoT) interfaces, RFID equipment, smartphones, internet television, closed circuits cameras etc. These interfaces increase the parameters through which businesses can collect data, be it in Volume, Variety, or even Velocity.
However, a major but often overlooked part in which businesses successfully use Big Data is the Storing of data itself. Data storage centers are getting exponentially bigger and better. The recent “Project Natick” by Microsoft to test the viability of putting a datacentre underwater is a tilt towards better storage at a lower energy cost.
These data sets will continue to rapidly grow and the surging growth of using big data in decision making is to be expected. The beauty of big data and high-powered analytics is the ability to search for new points of correlation and data clusters that are nigh impossible to spot with the technology of the past. Even now, businesses are only analysing a tiny fraction of what is available.
But that is about to change.
Big Data in Retail
Ecommerce orientated businesses though, have a definite unfair advantage in getting data in an easier and natural way compared to traditional brick-and-mortar retail stores as the main focal interface between business and customer lies on a data-gathering interface.
However, with recent Retail trends moving towards a more Online-to-Offline model, big data is increasingly used in every single touchpoint of the retail industry. Back-end application of big data includes supply chain and logistics management while front-end application can be segments such as sales prediction and customer loyalty.
Customer-centric data driven analysis leading to predictive sales
Businesses can finally have a new (and highly advanced) window to understand customers that are previously unknown – even to the consumer himself. With a greater understanding of customers’ pain points and needs, customized and personalized sales and products can be arranged and hence be extremely attractive. Cross-selling and the arranging of complementary products and services in various steps of the customer journey can potentially increase sales too.
However, collecting personal data can be easily be perceived in a bad light by consumers. The collection and using of data should be as unobtrusive and as helpful as possible; with the end goal of the collection to understand and improve the customers’ experience, driving customer retention and loyalty to the brand.
Improving supply chain
Big data analytics can also improve on internal process of the supply chain. Inventory and stock analysis can help ensure that there is always a constant supply of inventory, all without the risks of over or understocking. The ability to forecast demand of raw materials and inventory allows a more efficient workflow, leading to reduction in costs and avoiding potential pitfalls. Furthermore, with real time updates and visualisation of SKUs and logistical functions, it can be further optimized and making the system highly adaptable to market conditions.
How can Retail companies get on the bandwagon?
It is still a challenge for retailers; especially traditional ones to be able to combine and manage data from multiple channels and sources. The surge in the volume of consumer and sales data from omni-channels might be somewhat confusing. Many have taken to hire data scientists to try and make sense of the vast amount of data available.
However, that is just a stop gap measure as the amount of data produced and stored continues to grow steadily day by day. With an increased trend in automation recently, we can see retailers moving towards adapting cutting-edge technologies. Instead of producing the required technology internally, which will be extremely costly monetary and in work hours, many traditional retailers opt to partner or use the services of startups.
Startups are well positioned in this industry; being at the frontier of new technologies and trends. Here are some examples of some startups in the Chinese market:
Nexttao is an omni-channel retail technology service provider dedicated to providing data-centric omni-channel retail business management platform for brand users and providing accurate integration of omn-channel data and data-driven services for the apparel, food and catering and retail service industries.
ZMT alliance is a small and medium-sized enterprise big data precision marketing service platform with a focus on offline big data application area, providing enterprises with a set of services (big data mining, synthesis and application) as a whole industrial chain. IT covers resources processing, data visuallisation, real-time management, establish user labelling systems, insight into the needs of the user by user portrait; enterprise-class management self-marketing solutions and services platform
When 1 + 1 = 3
Big data will continue to be highly relevant in the retail market as we transition towards a more immersive consumer journey. It is high time for the players and stakeholders on both sides of the corporate-startup spectrum to come together and create exponential value for both corporates, startups, and an enriching experience for users.
Know thy self and Know thy consumers. A thousand customer, a thousand sales.
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