When companies analyze data, it can create a new kind of value. This can be likened to the microscope, which allowed scientists to see and measure things never before seen at the cellular level.
Using big data systems, businesses can make smarter operational decisions and improve customer experiences. For example, a power company can use real time weather and shipping information to optimize dispatching and inventory decisions.
Real-time Customer Engagement
Real time customer engagement is the ability to deliver a tailored experience at exactly the right moment for your customers. It’s a key differentiator that the likes of Amazon and Netflix excel at.
When utilising Big Data analytics tools, this process can be incredibly speedy and accurate. The key is to have a rigid structure in place that’s able to deal with the vast amount of information that’s gathered, whilst keeping it up to date.
This can be achieved through the use of AI technologies such as Chatbots and Virtual Assistants. For example, Loom uses its chatbot Cooper to identify when a user reaches their free usage limit, so the software can automatically trigger a modal asking them to upgrade. This real-time customer engagement strategy has short-term and long-term benefits, including increasing marketing efficiency and boosting brand credibility and competitiveness. It also builds a strong bond between consumers and brands that prioritizes positive communication and connection over impersonality or distance.
Big data analytics allows businesses to analyze past performance and predict future trends. This can include forecasting sales, customer churn, employee turnover and more. It is used by many industries and for a variety of business problems.
Marketing is one of the more popular use cases for predictive analytics. According to technology writer Mary Pratt, the software can be used for next best action, lead qualification, proactive churn management and “data-driven creatives,” which help companies decide what kind of marketing messaging will resonate with their customers.
Predictive analytics is also used to identify bottlenecks in business processes. For example, by analyzing historical data regarding cash flow, businesses can spot peaks and lows to ensure they have sufficient funds for operating expenses. This can also help them plan for future fiscal stability. This analysis can be performed using a variety of algorithms, including clustering, classification and regression. Each algorithm is designed to perform a specific task, such as categorizing data objects into groups or predicting continuous data such as the likelihood of a customer buying a car with a particular finance deal.
One of the biggest challenges in healthcare is integrating patient data from multiple sources. Big data analytics technology enables medical institutions to automatically trawl large sets of data, quickly delivering valuable insights that enhance service and reduce costs.
In a case study by Intel, hospitals in Paris used machine learning to trawl 10 years worth of admission data, then analyzed it for relevant patterns. They were able to forecast their daily and hourly admission rates, helping them better manage staff and shifts.
Identifying symptoms and trends also helps hospitals reduce costs. For example, a predictive analytics system can spot prescription errors before they occur, which could save $21 billion per year, according to the Network for Excellence in Health Innovation. It’s an industry that desperately needs greater insight, because prescription errors lead to unnecessary visits to the emergency room and even death. In fact, overdoses from medication now kill more people than car accidents in the United States.
Big Data is not a substitute for vision and human insight. The companies that succeed in the age of big data do so because they have leadership teams who can spot a great opportunity, understand how a market is developing, think creatively and propose truly novel offerings, communicate a compelling vision, and persuade people to embrace it.
The volume of data that organizations generate on a daily basis is staggering. It’s generated when customers open emails, interact with mobile apps, tag businesses on social media, visit websites, make purchases online, speak to customer service representatives and more.
This massive amount of data can be overwhelming, but recent technological breakthroughs have made it easier to store and analyze these large datasets. The cloud, Hadoop and other open-source frameworks have reduced the costs of storing data. These technologies make it easier for marketing teams to use tools that will help them improve their analytics. This allows them to provide more personalized customer experiences and boost brand loyalty.