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How to Use Big Data Analytics to Increase Customer Loyalty

Once you gather your big data, what is your next step? Today customer loyalty is paramount because the customer is in the driver’s seat when it comes to making a choice about how to interact with a service provider. This is true across many industries. The buyer has many more channel options and is increasingly researching purchase decisions and making buying decisions from a mobile device.

You need to manage your customer interactions armed with in-depth and customized knowledge about each individual customer to compete in a fast-paced, mobile-driven market. What does it take to provide the right offer to a buyer while he is making a purchasing decision? How do you ensure that your customer service representatives are armed with customized knowledge about your customer’s value to the company and her specific requirements?

How can you integrate and analyze multiple sources of structured and unstructured information so that you can offer customers the most appropriate action at the time of engagement? How do you quickly assess the value of a customer and determine what sort of offer that customer needs so that you can keep the customer satisfied and make a sale?

Company executives are increasingly viewing big data analytics as the secret weapon they need to take the next best action in highly competitive environments.

Companies are expanding their use of social media and mobile computing environments and want to reach their customers at the right time. To deliver successful customer outcomes in a mobile world, offers need to be as targeted and personal as possible. Companies are using their analytics platform combined with big data analysis with fast processing of real-time data to achieve competitive advantage. Some key goals they wish to achieve include

  • Increase their understanding of each customer’s unique needs. Provide these in-depth customer insights at the right time to make them actionable.

  • Improve responsiveness to customers at the point of interaction.

  • Integrate real-time purchase data with large volumes of historical purchase data and other sources of data to make a targeted recommendation at the point of sale.

  • Provide customer service representatives with the knowledge to recommend the next best action for the customer.

  • Improve customer satisfaction and customer retention.

  • Deliver the right offer so that it is most likely to be accepted by the customer.

What does a next best action solution look like? Companies are integrating and analyzing large volumes of unstructured and streaming data from e-mails, text messages, call center notes, online surveys, voice recordings, GPS units, and social media.

In some situations, companies are able to find new uses for data that was too large, too fast, or of the wrong structure to be incorporated into analytics and predictive models before. The models that companies are able to build are more advanced and can incorporate real-time data from a variety of sources.

Company analysts are looking for patterns in the data that will provide additional insight into customer opinions and behavior. Speed is a top priority. Your model needs to predict the next best action very quickly if you want to be successful in this fast-paced mobile world.

Advanced technology is helping companies to generate actionable information in minutes instead of days or weeks. Predicting the next best action often requires the use of sophisticated machine-learning algorithms from a cognitive computing environment.

We look at a real-world examples of companies in the financial services industry that are investing heavily in new ways to understand and respond to customers.

A global bank is concerned about the length of time it takes to access customer information. It wants to provide call center representatives with more information about customers and to have a better understanding of the network of customer relationships.

The bank implemented a big data analytics solution that improves the way its representatives support customers by providing them with an early indication of each customer’s needs before they got on the phone. The platform uses social media data to understand relationships and can determine whom the customer connected to.

The solution combines multiple sources of data, both internal and external. Some indication may exist of major life events that are taking place for this customer. As a result, agents are able to take the next best action. For example, a customer may have a child ready to graduate from high school, and this might be a good time to discuss a college loan.

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