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In recent years, India has become one of the fastest growing markets for eCommerce. With an internet user base of around 100 million in 2016, eCommerce sales in India are expected to touch the $100 billion revenue mark by 2020. According to an Assocham survey, customer demand is seeing a industry increase of as much as 40 percent from the ongoing festival season, compared to 2015. This exponential growth can largely be credited to the enormous chunks of information now available to firms. Availability of information has created the opportunity to utilise past trends and performances for growth, allowing businesses to increase customer satisfaction via improved products and services.
As online retailers look for ways to increase market share and profitability, application of Big Data has become an increasingly powerful tool at their disposal. Big Data Analytics is assisting retailers to stay focused on a new breed of customer - the omni-channel shopper. Consumers today are searching across several channels, from brick-and-mortar stores and catalogues to internet sites and mobile devices. This transformation is also in large part driven by improvements in portable devices, electronic media and geo-based technology.
The omni-channel shopping revolution has put the consumer in control, forcing the retailers to search for a single, easy approach that lets them interact with their customers anytime and anywhere, throughout any and all channels. The ability to cultivate a revenue experience that melds the sphere of physical stores with internet shopping has led both the consumers and the retailers to embrace omni-channeling retailing. E-commerce companies have turned to Big Data Analytics for focused consumer group targeting. Assessing campaign plans and maintaining a competitive advantage, particularly during the festive shopping period.
Here are three of the exciting ways in which big data is helping drive growth in India's bustling eCommerce market;
1) Client Journey and Behaviour
Big Data plays an essential role in tracking the entire journey of a client, from entry to exit. An typical online shopper may not realise that every click has been monitored and that all purchases being created are captured from start to end. Dividing customers into different segments based on a mixture of purchase patterns and demographic details makes it much easier to target them with relevant offers. Such personalisation is even more critical during festivals and peak shopping seasons, when companies invest heavily to attract new customers and retain their current base.
Pulling information on customers in the weeks prior to a sale yields a better understanding of their current purchase preferences, and real time demand. This is very helpful as eCommerce retailers can then plan their inventory and allocate key space on their site for optimal returns. For instance, according to historic data, Electronics, Automobiles and Apparel & Clothing are a few of the classes that see the maximum purchase volumes throughout festivals; therefore, demand forecasting and inventory stocking of product before the sale period can be utilized to provide optimal discounts levels, hence maximising earnings.
Demographics also play an essential role in demand. It's been discovered that Calcutta and other significant cities see a spike during the festival of Durga Puja, while North India is expected to pick up speed during other time of the year. Big Data helps in predictive evaluation and stock management by assessing customer behaviour.
2) Personalised Offers and Content
E-retailers invest heavily to attract new customers to their sites and maximise their return on investment. This is really where campaigns, referral programmes and coupons behave as triggers to pull new visitors to websites. Amongst competing brands, it's a matter of the way the data is captured and assessed, extrapolated and converted into company decisions and strategies that determines the market leader.
As another step to customer behavioural evaluation, e-retailers use recommender systems to create recommendations autonomously for users based on previous purchases and searches, as well as based on the behaviour of other, similar users. This not only offers a personalised experience to customers, but also boosts the retailer's ability to drive sales through cross-sell and up-sell. For example, advocating mobile accessories to a individual buying a phone will raise the probability of some cross-sell. Retailers may also extract useful insights from a purchaser's search history, along with his actions on social websites (likes and clicks), thereby having the means to offer personalised advertisements which could enrich the customer's overall shopping experience.
3) Customer feedback
Customer feedback is perhaps one of the most under utilised opportunites for eCommerce firms to gain a competitive edge. It's fair to assume that the success of any business is directly proportionate to how well it fulfills its customers. Analysing customer feeback offers actionable insights not just to create better consumer experience, but also to improve existing products or services.
Among the most essential utilisations of feedback data is opinion analysis of reviews and evaluations. Sentiment analysis is a method of identifying clients' reviews and opinions about services and products. Big Data assists in processing a large number of testimonials that are not in comparable formats, and stores the mixture of data, which can be more easily utilised. With the use of text messaging and machine learning, positive and negative testimonials are categorised into various buckets and assessed to gather useful insights regarding customer satisfaction and the total business. This processed information can be a useful asset for organisations looking for immediate customer engagment.
An automated consolidation of cutsomer feedback is obviously considerably less expensive and more immediately actionable than a manual approach (if done at all). Online businesses can create click stream data in huge volumes. With the assistance of information, e-retailers analyse customer feedback from previous campaigns to improve their strategies by determining issues and solving them in another purchase.
Big Data has assisted the e-commerce business in many ways, but there continue to be an infinite number of opportunities to explore. Many companies have already begun applying Big Data for real-time analysis. Real-Time Big Data Analytics (RTBDA) may result in improved sales and greater profits by discovering and resolving issues at the time of purchase rather than post purchase. RTBDA can help satisfy customers by solving issues in situ, as well as other benefits such as detecting fraud. Ultimately, machines using RTBDA are going to have the ability to respond like people when the situation requires.
Big Data, when coupled with the Web of Things opens infinite opportunities for better decision making and hence profitability. It connects multiple devices through the internet, generating vast amounts of data. This data, if utilised properly, can be very powerful, allowing online retailers to forecast precisely what, where and when a buyer wants to buy.