Big data analytics solutions, which use specialized software tools and applications for predictive analytics, data mining, forecasting, and optimization, have become increasingly important for most businesses, no matter what industry they’re in. With these tools, organizations can gather vast amounts of organized and unstructured data, sort and analyze it, and extract patterns and essential business insights. This data has emerged as a significant differentiator in assisting businesses in forecasting and making strategic decisions to stay competitive, boost revenue, mitigate risk, and achieve growth.
Keep reading to learn more about how big data analytics can help your business.
E-commerce & Retail
Many retailers have adopted a business-driven and pragmatic approach to big data. In fact, sixty-two percent of retailers report that the use of information (including big data in retail) and analytics is creating a competitive advantage for their organizations. Some of the most effective strategies for leveraging big data include identifying specific business requirements first and then customizing the infrastructure, such as types of analytics tools, to support those requirements.
In practically every e-commerce and retail selling/buying process stage, big data analytics plays a critical role. It can help predict trends, identify new consumers, optimize pricing models, segment customers based on purchasing behavior, and present tailored, real-time offers based on client preferences. For example, let’s say a retail outlet’s most valued customers “liked” the Food Network on social media and have shopped frequently at Whole Foods. From there, the retailer can then use these insights to target their ads on social media channels for cooking-related shows and organic grocery stores. This will likely result in higher conversion rates and lower costs for customer acquisition strategies.
The lifeline of the manufacturing industry relies on hundreds – if not thousands – of daily interactions with mechanical equipment, electrical relays, sensors, and more, all of which are coordinated and controlled by complicated systems. To keep things running smoothly, thousands of metrics and signals must be monitored at all times. Many businesses in the manufacturing industry have shifted their attention from traditional monitoring procedures to a more flexible and real-time process using big data analytics tools. This allows them to create tactical insights to significantly boost corporate performance while addressing emergent issues.
There are many use cases for big data analytics in the manufacturing industry, including:
- Predictive Quality: Similar to predictive maintenance, this allows manufacturers to track the many variables that can affect product quality and help determine root causes and factors that contributed to lower quality products.
- Supply Chain Management: In manufacturing, timing is everything. Big data helps better predict if and when a supplier will deliver and makes it possible to reduce risk by optimizing supply chains.
- Improving Throughput and Yield: There are various factors that can negatively (or positively) impact product yield, and by utilizing the insights from big data, manufacturers can identify patterns in various processes to predict product yield and proactively make improvements.
Because the healthcare sector regularly generates large amounts of data (think about the sheer volume of data that flows through insurance companies), big data analytics plays a critical role in keeping this industry moving. In fact, healthcare is one of the most promising sectors for big data utilization. Moreover, it also helps address some of the industry’s most pressing issues, such as patient profiles, genomic analysis, public health monitoring, fraud analysis, and more.
One timely example where big data can help healthcare companies is the prediction of mass outbreaks. Using big data, scientists and doctors can build models of population health and create predictive models of how an outbreak might progress in a certain population. This can help in multiple ways including the development of vaccines, preventing hospital overcrowding, and issuing quarantine recommendations.
The travel industry generates a large amount of data in the form of bookings, queries, itineraries, fare charts, and consumer feedback, which leads to extensive data trails. The travel sector can provide a significantly better client experience and boost business efficiency by harnessing all this information using big data analytics. Strategic marketing, improved customer experiences, and reputation management can all benefit from the power of big data analytics.
An example of big data analytics in the travel industry is United Airlines’ use of customer data. They analyze over 150 variables in each customer profile to measure everything from past purchases to customer preferences. As a result, they’re able to create compelling and tailor-made offers for their customers. Big data techniques have increased United’s travel industry year-to-year revenue by over 15%.
Another example is improving the customer experience. One of the biggest travel headaches is lost or misplaced luggage. Through Delta Airlines’ app, customers can track their bags using the same technology as the Delta ground staff. Approximately 11 million Delta customers have downloaded the app globally.
The benefits of big data analytics across multiple industries is countless. From healthcare to travel to manufacturing, big data allows businesses to better target their marketing strategies, streamline business processes, and improve the customer experience, just to name a few. With the continued innovation of data management and analysis tools, businesses will be able to continuously improve upon their use of big data analytics to make intelligent choices that help them improve revenue, save expenses, and boost growth.
If you’re wondering what your big data is telling you, reach out to Gemini Data today. Our full stack system simplifies data management and analysis, allowing your business to connect the dots between data from disparate sources and effectively transform data into stories.