In a fast changing business landscape, information is emerging as the new currency. Businesses today are dealing with huge amount of data every single day; industry estimates predicted that the 2014 annual internet traffic exceeded 667 exabytes. With such vast volume of information available at their disposal, businesses are increasingly looking for a way to refine and make sense of this enormous volume of information in order to enable optimised decision making. This is where the Big Data and Data Analytics tools come into the picture. Relying on advanced pattern identification algorithms, heuristic machine learning and real-time information processing, Big Data analytics has been empowering businesses across market segments with the tools they require to survive and succeed.
The retail market, in particular, has benefitted a lot from the rise of Big Data. Consider Macy’s, for example. The retail chain registered a 49 percent increase in their sales during the last holiday season, a fact that it attributes to using Big Data as a differentiator. Even small and medium-sized retailers have benefitted from the use of Big Data technologies, as revealed by a recent Wipro report – 54 percent of the survey respondents indicated financial gains through multichannel sales when employing Big Data, while 52 percentsaw greater sales by offering the next logical item to the consumer. No wonder, then, that Big Data in Global Retail market is pegged to grow at a CAGR of 35 percent and become the fourth largest industry in terms of market share by 2020.
Given the disruptive impact that data analytics can have on retail businesses, both offline and online, here are a few Big Data trends that are even now actively revolutionising the way retail industry operates:
a. Extreme segmentation and consumer profiling: Segmentation is an age-old practice in retail; consumers are often grouped into different clusters based on their age, income groups, professional/personal backgrounds, likes/dislikes, regions etc. Big Data technologies have taken this approach a step further. With the end-goal of eventually serving a market of one, Big Data tools segment the market to the maximum possible degree. By analysing all aspects of the user’s online and offline interactions and factoring in all the details, Big Data tools can accurately predict the choices and behavior for each and every retail consumer by profiling them.
For gauging how impactful extreme segmentation can be, consider the example of Netflix. With its users segmented in over 70,000 profiles, Netflix can target millions of customers with exceptional accuracy and update those profiles on a daily basis.
b. Advanced machine learning for more tailored reports: The ultimate goal of any data analytic tool is to enable the best business decisions by compiling and presenting results from the most relevant metrics to any business owner. However, different retail businesses will have different requirements; the requirements of a retail perfume business, which has high acquisition and low retention and operates chiefly on seasonal demands, will greatly vary from that of a low acquisition, high retention business such as a grocery store which caters to day-to-day necessities. Even businesses operating in the same space may have different business requirements depending upon their target market and approach.
As such, data analytics tools at present seek to deliver more personalised
business insights to retail industry players. Boosted by the recent state-of-the-art tech innovations in machine learning, automated pattern recognition and artificial intelligence (AI), data analytics can drive better business decisions.
c. Completing an ‘omnichannel’ user profile through enhanced personalisation: Many retailers have adopted an omni-channel marketing strategy that combines traditional, digital and mobile channels. As such, omni-channel profiling becomes vital to enable an end-to-end view of business functions through the user’s perspective. While retailers often collect information such as in-store footfalls and web visits, many choose to augment their knowledge stores with data from third parties in order to identify users and predict their purchasing decisions. Did the same user who is now in store previously visit the website as well, and why was the decision made to purchase an item in a store instead of buying it online? Big Data has been answering these questions for retail businesses to develop more comprehensive user profiles, predict choice patterns and tailor their offerings to better suit their prospective consumer base.
d. Consumer security and privacy: From a security perspective, more data and more channels equate to more risk. For evidence, look at what happened recently at the Target and TJX stores. Such lapses in security can often be detrimental to a brand’s image, and have led to stringent, more security-centric measures being rolled out by regulatory bodies across the globe. Loyalty card details can no longer be accessed without a user’s explicit permission, while measures are being implemented to only allow data collation for legitimate usage. Also, as there is a push to make the handlers of sensitive information more accountable for the users’ personal information, data analytics services have also started anonymising data to circumvent the privacy breaches whilst at the same time also allowing comprehensive business insights into the retail industry.
e. Optimised real-time pricing: The retail industry, owing to its massive size, can be slow to adapt to market changes. Through technological enablement, Big Data services have been actively changing this to allow retail businesses instant access to key insights such as current competitor pricing, product demand and inventory stock levels. This allows the businesses to respond to the changing market dynamics in real-time and leads to an optimisation of business operations. A common example that large grocery chains employ is the practice of dropping prices as the day gets to a close in order to move fresh produce, or start offering bulk deals as the product gets close to expiration date. The interesting thing here is to use big data to figure out the right discount level that will move the product, but not cost tremendous margin hit.
Moreover, data analytics also lets businesses identify a change in consumer demand patternsand to gauge if the changes in consumer behavior are long-term or short-term. This predictive approach leads to better price adjustments to counter the flagging demands,leading to a substantial increase in revenue.
Author's Bio: The article has been authored by Mr. Aashu Virmani, Chief Marketing Officer, Fuzzy Logix.