Data-Driven Applications and Machine Learning for Customer Satisfaction… and Privacy

Thousands of companies are using big data and analytics to gain insight into their data. And, while visualizing data can be helpful, graphs alone don’t cut it. What businesses need are data-driven applications that help employees do their daily jobs better – while wowing the customer. More importantly, these data-driven applications must be actionable and based on individual preferences. For instance, they should alert a marketing or salesperson each morning with a notification, such as: “Here are the 50 customers that might churn in the next 30 days.” It’s very likely that the customer would appreciate the effort of their bank to deliver a message or service that would give them a reason to stay. Your customer knows that you have sufficient information about them to deliver a more personalized experience. Now, it’s time to do it. This is how big data processing can create real business value - by providing finite and actionable insights for employees that allow them to better serve their existing and prospective customers immediately.


Data-driven applications create true business value because they provide users with actionable tasks in real-time, are scaled for the enterprise and remove human subjectivity via machine learning. Machine learning encompasses the algorithms, optimization and learning tools that interact with the data, thereby eliminating any human interaction/intervention between the data being generated and the offers or services being delivered to the customers - ensuring customer data remains secure and private.


The sheer mass of data on customers is not possible to process in one data scientist’s human brain. Machine learning must be used to analyze and deliver instruction on what should be done to better the business. So, instead of a data scientist looking deeply at a section of the data, the systems are looking at and devising outcomes from all the data - mainly due to the ever-growing volume of data and the need to quickly make something of it. And, as more data is fed into the system, machine learning continues to get smarter to deliver the best, most relevant content to customers. But, as mentioned earlier, it doesn’t make any sense to simply keep making graphs about data and big data.


Financial institutions need to focus on the business problem, have clear goals and introduce data-driven applications, based on machine learning to deliver more automated and actionable results for the problems of the business. There are a lot of solutions available to work with big data, and now they are not only allowing the ability to search many of the databases that hold big data, but also aggregate, analyze and visualize that data.

At the end of the day, the more content and data companies have on their customers, the better their ability to quickly drive actionable results and deliver greater revenue to the business, while ensuring privacy and convenience for the customer. But, remember, the key to delivering superior customer experience is to contextualize their data, and to get personal—understand your customer at the individual-level to deliver the right messages and offers, at the right time.