Data is everywhere and it is available in chunks. Whether it comes from your employees, your customers, your competitors, if you have the right tools and talents in form of data analysts or other such profiles, you could use this abundance of data and turn in to usable insights that can help improve your business operations, improve revenues and reduce costs significantly. All these chunks of information are generally referred to as Big Data. And the process of using these data points for driving business decisions is done via Business Analytics.
How Big Data is being used for Transforming Business Analytics?
Now that we understand the concepts of Big Data and Business Analytics, let’s see how big data is transforming business analytics. We are a leading business analytics solution provider company that often uses big data to derive data-driven analytics for our esteemed clients. Here are a few key areas we have identified where doing the same could benefit you –
Identifying Different Patterns and Trends
Data Analytics is primarily used for identifying trends and patterns from structured and unstructured data that may not be as easy to comprehend using traditional data analysis techniques. This is instrumental for B2C businesses that is trying to get a better grasp on their target audience’s preferences, behavior patterns, market trends and other important factors that impacts how they interact with the industry you are in, and what do they look for in a solution provider.
From such insights you can then make data-driven decisions to improve, change or upgrade your business services and offerings to match the preferences of your audience to improve conversion rates, repeat customer rates and much more.
For instance – An ecommerce marketplace mammoth like Amazon uses big data analytics to identify what products buyers generally search for, what additional products they buy with that product and what kind of discounts they look for.
Bundling all these strings of different information together, they can provide the end user with a recommendation of ‘Frequently purchased together’ or ‘People Also Looked For’ to provide alternates or close seconds for customers who don’t get sold on the first product but still want to browse available options. This improves the overall retention rate of visitors as well as improves the conversion rates significantly.
Customer Segmentation
Another important aspect of big data is to structure your database about your customers. You can segment and categorize your customers based on their preferences, behavior, location and more. This is ideal for specific marketing efforts especially for multinational companies that is trying to enter a new market, re-purpose their brand to a specific target audience or improve overall customer engagement.
For instance – Here is a table of how some of the most popular countries segment their customers
Company | Customer Segmentation Type |
Nike | Buying Behavior determined by purchasing frequency, average order value, age groups and lifestyle choices – fashion, athletics and leisure. |
Marriot International | Travel Behavior determined by frequency of travels, type of travel booking – business, family, leisure and more. |
Apple | Tech and Lifestyle Preferences – Music and Photography Enthusiasts, Tech Savy Geeks, Creative Individuals like Designers and more. |
Coca Cola | Geographic Location and Lifestyle – through analytics of customer preferences regarding taste, purchasing power, market product offerings and more. |
Improvising Operational Efficiency
Operational expenses and processes can be very difficult to manage and maintain while ensuring optimal performance. Here is where big data analytics can help businesses improve their internal business operations significantly. It can provide better insights based on data and predictive analysis inputs that can allow professionals to take quicker data-driven decisions on data-driven insights, forecasting and predictions.
For instance – This can help a logistics company to optimize their major delivery routes to reach the end location faster while being more fuel and cost efficient. By conducting proper analysis of traffic patterns, road conditions, driver performance and other such factors, it can deduce the most optimal routes that can help such companies save up a lot of money on daily operations while reducing carbon footprint.
Pro Insight – A live example for one of the most renowned logistics companies that leverages big data analytics for route optimization is UPS -United Parcel Services.
Real-Time Analytics
One of the most important and obvious use cases of big data in business analytics is to get real-time analytics. Real-time analytics is much more beneficial than any other kind of analytics since it gives the clients an opportunity to take instant decisions based on current data. This is ideal for businesses that run a tight ship in a fast-paced industry like healthcare, finance and on-demand solution providers.
For instance – Uber uses real-time big data analytics for monitoring driver behavior and ensure quality service is provided to customers. They use data analytics to track driver ratings, satisfied customers, driving patterns and other such metrics to take corrective actions when needed. On the other side, Uber also very smartly uses real-time analytics for its pricing strategy. Based on supply and demand they optimize their cab fares during different times of a day.
Risk Management
Big Data can be pivotal in business analytic requirements for managing risks in important industries like healthcare and insurance. Since such fields generally have abundance of data in form of medical records or registration and health details, creating efficient risk management big data analytics model becomes easier. Based on the insights provided by these models, such organizations can take proactive measures to mitigate possible risks, maximize ROI and make life-saving choices.
For instance – Allstate has a dedicated program known as ADAI – Allstate Data Analytics and Insights that uses big data analytics for conducting a risk and opportunity analysis in their business operations. Their model gets trained on consumer’s claim data, behavior and other sources of relevant inputs. They are also using big data analytics to identify fraud claims by checking for certain anomalies in the claims.
Final Words
These are the top ways big data is transforming business analytics and enabling businesses to improve their business operations, sales, customer satisfaction, and other important business-related sectors significantly. If you are in search for leveraging big data analytics in business analytics, we provide tailored business analytic solutions that you can benefit from.
Very informative blog.