Brands are collecting volumes of data generated by a variety of sources at extreme volume, velocity, variety and veracity. This entire collection of digital data is big data. Big data can be generated from CRMs, social media activities, sensors, web search, video analytics and more. With the current rate of data generation, there is seemingly endless amount of data to be collected. Leading retailers adopt advanced technologies to generate insights from big data to help them enhance the customer experience, increase operational efficiency, understand buying patterns as well as shifting customer trends.
Where does big data come from?
Big data which comes in the form of structured and unstructured data is categorized into 2 varieties – analytical and transactional. For a long time, brands make business decisions based on transactional data. This well-defined and structured information includes customer information from CRM systems, transactional ERP data as well as in-store and web-store purchases. When big data is distilled and analyzed in combination with analytical data, businesses will develop a more thorough and insightful understanding of their business.
Some examples of analytical data are social media data and machine data. Social media provides insights on consumer behavior and sentiments that can be integrated with CRM data for analysis. Everywhere we visit on social media, we leave digital breadcrumbs that point to collective trends. Social networks such as Facebook and Pinterest are achieving firmer grips on users’ profiles, interests, spending habits and more to provide users with personalized content such as targeted advertisements. While social media data provides insights on the customer persona which includes demographics, interests and more, machine data provides an understanding of customer behavior. Machine data involves sensor data, web logs and data from other hardware and software. Real-time data are obtained from various devices to track customer behavior. Web-based data mining such as web logs data are obtained through browser cookies while a more advanced technology involves the use of digital sensors to obtain highly valuable and specific information.
Customer intelligence
The use of big data would revolutionise the way retailers market their products and understand their customers. With vast amount of insights provided from big data and the complexity of it, this information must be curated and organized to have a complete understanding of the customers. Big data can be used to predict purchases, analyse shopping behavior, be aware of current trends, understand in-store behavior and more. However, understanding the behavior of customers is half the story. Retailers need to know the human element of their audience which includes their demographics, sources of influence, motivators, income, interests and lifestyle. There are various solutions to collect such information through internal and external sources. A non-intrusive method of collecting customer demographics would be the use of audience profiling video analytics through surveillance cameras that analyses and records the age, gender and emotions of in-store customers.
With efficient profiling of customers through big data, retailers can further know which customers within their database are the most valuable customers. Traditionally, brands define their valuable customers by the amount of money spent within a period of time. However, this method of calculation is not accurate. Some factors to consider include average purchase value, lifetime expenditure, acquisition costs and retention costs. The average purchase value refers to the amount spent per product. For example, a customer could be spending $100 on a new arrival dress while another customer could be spending $100 on 5 sale items. Though the total amount spent is the same, the value that both customers bring for the brand differs. Using the right metrics and big data, retailers would be able to properly define their valuable customers and build good relationships with them.
The volume of big data is on an upward trend and the adoption of it will accelerate. Big data will continue to transform and alter businesses across all industries and the retail industry is no exception. Brands have been harnessing the power of big data to gain business and customer insights. Today, it dictates the structure of the retail industry to steer towards a customer-centric model and will continue to reshape the physical and online retail platforms. Big data give businesses valuable information; however it does not act like a crystal ball that unveils business recipes to success. It requires tools to analyse and make sense out of it and should not be understood as some sort of technological solution. As big data starts to go mainstream, the secret to big data is to first identify the problems faced before streamlining solutions that answer these questions that were previously considered beyond reach. To put it simply, big data is an opportunity to find insights in new and emerging types of data and content, to help businesses achieve success efficiently.