GUEST COMMENT Thinking big: how five big data trends have revolutionised the retail industry
Driven by an increase in the consumer purchasing power and rising population, the global retail industry is expected to cross $20,000 billion by 2017. The industry has, despite its massive size, recorded a Compound Annual Growth Rate (CAGR) of 3.9 percent year on year over the past few years. Needless to say, the massive scope of opportunity offered by the retail industry has led to a highly competitive market that often operates on razor-thin margins. In such an intense, cut-throat atmosphere, retail industry players are often on the lookout for any kind of competitive edge over rival businesses.
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, data analytics has been empowering businesses across market segments with the tools they require to survive and succeed. Given the disruptive impact that data analytics can have on the retail businesses, both offline and online, here are a few big data trends that are even now actively revolutionising the way the retail industry operates.
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 behaviours 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.
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 that 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 personalized
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.
Completing an ‘omnichannel’ user profile through enhanced personalisation
Many retailers have adopted an omnichannel marketing strategy that combines traditional, digital and mobile channels. As such, omnichannel 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.
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.
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.
Moreover, data analytics also lets businesses identify a change in consumer demand patterns and to gauge if the changes in consumer behaviours 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.
Partha Sen is co-founder and CEO of Fuzzy Logix