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Emergence of analytical techniques in value retail

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Could you ever think the data accumulated a decade ago, while carrying day-to-day business could boil down to digits in zettabytes and help us to develop a cognitive vision towards the expansion of the businesses across the sectors?… Probably not.

Emergence of analytical techniques in value retail
Big data analytics to be explained in simpler terms is the collation of the data, basis the pattern of buying of the end consumer

In the fast-growing and manufacturing world. Words like Market Segmentation, Manual Surveys, and Written Feedback may not be considered of great use. But marketers have played smart and found work easy solutions to develop a strategy using BI tools, some have extended the interest in technology and hunt for help from the AI’s and Big Data Analytics. But the real questions arise – Are these the only dependent conventional sources to be relied on?

With the genesis of Big Data Analytics, the sister terms coined and religiously taken into consideration while planning a Marketing strategy are DeepDive Analytics, Sentiment Analysis, and Emotion Analysis.

DeepDive Analytics actually makes your task easier by helping you access the data for any time period and compare ad hoc time periods giving permission to access the data which is dated Long ago. It enables you to add data from other segment and work over it. Empowering you to do multitasking. Monitoring and Visual Analytics have been predefined dimensions. Deep Dive Analytics, however, allows you to choose dimensions and measures for analysis. The options of monitoring dashboards like Filtering, sorting, and manipulating data, adding columns, compare disconnected time periods, add columns with computed data, rank, filter, sort, change data operations, change summary operations and groups is all predefined, but in Deepdive you can do it on your own.

Coming to terms like Sentiment analysis and Emotion analysis gets very confusing as they are very tightly closed concepts and difficult to differentiate. Sentiments Analysis is limited to the Positive, Negative and Neutral feedback whereas Emotion analysis is a wider concept as emotions are not limited to a specific time and are the only constant phase humans go through. Emotion analysis overpowers Sentiment analysis as sentiment analysis can answer to a query in terms of ‘WHAT?’ But Emotion Analysis can actually answer it in depth telling ‘WHY?’.

Big data analytics to be explained in simpler terms is the collation of the data, basis the pattern of buying of the end consumer. It is taken into consideration largely because of the 4 V’s it possesses: Volume, Variety, Velocity and Veracity.

As value retail has grown multiple times. Customer buying pattern is cultured when the shopper goes to the billing desk to pay the bills for the items purchased. That’s the time, data is captured on an individual level and stored in systems to pile huge information. Hence using this data, we understand the USP of the products, which helps us to carry out the marketing strategy and restore products in the stocks.

Big data analysis can be used in many ways, it can be used by the retailers to understand the customer behaviour around by 360 degrees turn and build up publicizing strategy on a bigger scale. Learning brand sentiments can cost a lot of capital through surveys and consumer polls, hence gathering data through Online forums like Twitter and Facebook, basis their interest or liking for a particular brand, brand loyalty, Range of products preferred according to the pricing. We will be able to understand the brand sentiments easily. Creating Customised promotions for the people based on the preference of buying or data collection through browsing history of the person, we can actually map a demographic segmentation and create the marketing plan and boost the purchasing power of the consumer. We can change the flow of customer traffic by setting up gadgets to scan the Coupon codes or scan the QR codes when they are physically present in the store. Optimising online shopping portals can be one of the other ways of enhancing the data.

Another use of big data analytics is managing the inventories and tracking order. Hence deliverables can reach in time. Therefore, using the big data, we can analyze the predictive conversion rate and set and achieve quarterly or yearly target set. Deepdive Analytics, Sentiment Analysis, Emotion analysis are business driven proactive strategy plan of action used by many companies these days to keep up in the rat race and lead from the front. A report says that 62 percent of the retailers use information and data to create competitive edge across the Industry. In this deep driven Industry, the only challenge is to keep up on number one positioning in the retail chartbuster.