Last updated on October 13th, 2022 at 10:48 am
Data is being generated at every moment of the day and has grown from retailers using their own data to databases available across industrial verticals. It is so huge that cloud storage is now the buzz word. Data analytics with the Big tag deals with data primarily and the predictions or forecasts from analyzing databases that help with informed decision making in all processes related to business. This could run into volumes of several petabytes of data.
But, why would one need a Big Data Analytics Course? Because smaller databases that are less than a terabyte size-wise can be tackled with traditional tools. However, modern data tends to be unstructured and comes in the form of videos, audio clips, blog posts, reviews, and more which are challenging to clean, organize and include huge volumes of data.
The tools and techniques involved in the capture, storage, and cleaning of data need necessarily to be updated. One also would need faster software that can compare databases across platforms, operating systems, programming languages and such complexities of technology.
The speed and agility of analytics offer big advantages and savings in making informed business decisions. That’s why investing in data analytics and Data Analytics Training is such a popular choice across industrial verticals and sectors.
Let us look at the data analytics improvements of some real-life examples.
Offering marketing insights:
Foresight from analytics has the potential to change marketing strategy, operations and more in all firms. Whether it be effective marketing strategy or promotional campaigns, decision making, purchasing, cost-saving measures, targeting the customers, promoting products or improving efficiency through the predictions, insights, forecasts, etc help make those decisions. Just look at the campaign of Netflix covering over 100 million customers for inspiration.
Boosting retention and Customer-Acquisition:
Coca Cola used their data foresight to draw up their retention and loyalty reward programs and to improve their services, products, and customer stories. Besides boosting sales such improvements trigger loyalty too.
Regulatory compliance and Risk Management insights:
Singapore based UOB did their risk assessment and management for the financial sector and budgeting. Foresight and predictions can also be effectively used as a critical investment in regulatory compliance.
Product innovations:
Take the example of Amazon’s diversification into groceries, food, and fresh-foods segment. Their analytics program was based on the acceptance of customers trends and successfully helped innovate product lines, design models of innovation in saleable products, etc.
Management of logistics and supply-chains:
This essential field can be transformed very effectively as Pepsico did with improved processes, scheduling deliveries, warehouse management, reconciling logistics and shipment needs and more.
Budget and spending predictions:
The loyalty of customers is reflected in spending patterns and data is collected from use of credit cards, effects of promotional programs and customer retention data, web users log-in data, IP addresses, etc to gauge predictions for spending and effective budgeting. Did you know that Amazon analyses accounts that run into astounding figures like 150 Mil customers and their analytics programs increased sales by 29 percent and new customers by 40 percent? That’s huge profits from data analytics!
Bettering customer service:
Improvement in customer experience yields big dividends as in the case of Costco where specific customers who were at risk with listeria contamination in fruits and were warned instead of creating a scare with emails to all customers.
Demand forecasting:
Just look at the Pantene and Walgreens hair-care products sales figures. They promoted the products based on a demand prediction of weather and anticipated higher humidity affecting sales of anti-frizz hair products. Pantene recorded a 10 % increase and Walgreens a 4% sales increase. Smart use of data analytical predictions by retailers!
Research on journeys of customers:
This graph is never a straight line and when in retail marketing analytics with many thousands of customers, one can help understand data like where an individual customer will seek product info, how and where to reach such customers, why the customer loyalty changed, etc. Looking for the needle in the haystack is now easy with data analytics.
Concluding note:
All enterprises, especially in the retail sector, need big data analytics to have reduced operational expenses, a competitive edge, enhanced customer loyalty, better productivity, and retention. The demand for data analysts keeps growing alongside the growth of data and is an ideal choice of careers with scope, payouts, and growth. If you wish for a Data Analytics career, then do a big data analytics course at the reputed Imarticus Learning. Their data analytics training with assured placement, certification, soft skill modules,industry-suited curriculum, and real-time project work offers the best career choices. Enroll today!
