We are officially doing business in an era of big data. Companies in all industries are collecting and analyzing massive amounts of information about their customer base. Organizations collect information at every customer touchpoint, from their business phone numbers to their website visits, and use it to make data-driven decisions regarding marketing, sales, and product development.


The results of big data are often realized through technology. Manual processes become automated. Supply chains become more efficient. The movements of inventory, shipments, and even people can be tracked to help streamline operations.  It would seem that big data is removing the human element from business interactions. Customers don’t have to call a 1800 number and ask sales people for suggestions, they can simply log in to an app that will direct them to products that they might need or like, based on their customer profile. International corporations can manage overseas work forces without ever setting foot in another country.


While big data is helping to streamline and automate service delivery, the truth is, the data itself is only half the story. Without humans to analyze the information, and without customer contact agents to help deliver service and maintain relationships, big data can only take an organization so far.


Data and customer service


Typically, big data is used to improve marketing and product development issues. Companies who take it one step further and use it to improve customer service are truly harnessing the power of this information. Here are some ways in which companies are using big data to strengthen customer relationships:


How to Improve Customer Service with Data

  • Predict What Customers Want Before They Know They Want it


Have you ever walked into a diner and your usual waitress appears with your regular order, without even stopping to ask what you want? That level of personalized service is what retailers try to do when they make recommendations for customers based on past purchases.  They make those recommendations based upon big data collection. They know what websites each customer visits, where they live, how many times they’ve contacted customer service, the nature of those contacts and much, much more. Through predictive analysis, they can present new and relevant products to their customers on a regular basis.


  • Improve Customer Service Interaction


When a customer reaches out to a service agent, that agent has access to a complete data profile that paints a detailed picture. Having immediate access to customer profiles is critical in an era when customers have access to multiple channels to connect with brands, including the very public social media space.  With each interaction, the relationship can improve because agents are always gathering new data, and improving the customer profile.


  • Identify and Solve Pain Points


When organizations dig deep into pain points, they can solve problems that affect a significant number of their customers. Delta Airlines, for example, knew that lost luggage is one of the biggest headaches for travelers. Even when luggage is not lost, customers always face a great deal of anxiety at the baggage claim area, praying their bags have made it to the destination. To help alleviate this stress, Delta instituted a program called “Track My Bag” which is available on the airline’s smartphone app. Customers can keep tabs on their luggage from check-in to final destination, and should their baggage be lost, they will know precisely where it is, and when they can expect it to be returned.


How Big Data Affects The Financial Services Industry

The key to making big data work is human interaction. Computers can collect data. Computers can arrange data in meaningful ways. But computers cannot analyze the data and provide insight that will improve customer relationships. In banking, technology has been gradually replacing human interaction over the last several decades. Since the dawn of the automated teller machine, customers have been moving away from personal banking services to self-service technology. Customers can withdraw cash from a store checkout line, deposit checks using smartphone apps, and apply for a mortgage without ever meeting with a loan officer.


The key to making big data work is human interation


Before self-service options, customers visited their local bank branch at least once a week to deposit checks, cash checks, withdraw money, and make loan payments. They got to know the tellers, and the tellers got to know the customers. Tellers were trained to listen to customers and watch for changes in behavior. As customers neared the end of their car loan, for example, tellers could begin to plant seeds for new loans, or for savings accounts to help them maximize their upcoming increase in cash flow. If a customer became engaged, or if women became pregnant, tellers were able to recommend products and services for these life events.


But as technology took over, that personal touch was lost. Fewer and fewer customers come into a branch to conduct business. The financial services industry has historically failed at balancing the human side and the technical side of banking.  Customers have become so dependent on self-service channels that the thought of calling a customer service line or visit a branch rarely crosses their mind. A phone call costs precious time, and a trip to a branch costs even more time; time that banks have conditioned customers not to waste.

Big Data: Bridging the Gap Between Humans and Tech

Big data analysis can help banks bridge the gap between the technical and the human. Banks can collect infinite amounts of data that can give them insight into individual customers – but that data is usually used for product and service development, not customer service initiatives. Banks provide a service. That means that a human touch is required in order to get results. Data can bridge the gap between self-service technologies and service agents who can close deals. Through data, banks can identify changes in a customer’s profile and behavior. Through analysis, it can be determined what that individual customer may need. The appropriate agent can then reach out to the customer to give them customized support and service.


Big data can be seen as a sales funnel that picks up on certain trends and triggers that were previously only available in highly personal environments. The key to tying it all together are the humans who analyze the data, and the service employees who follow through with the customer.



Companies of all sizes across all industries can use big data to understand their customers and provide personalized service options. This is not, however, where it all ends. Organizations that will best benefit from big data analytics are those that can parse out the relevant data from all of the white noise that is mined through data collection efforts. Only when they can successfully draw insight from their analysis, will they be able to truly put big data to work targeting customers, personalizing experiences, and solving their problems. Big data is a valuable resource, but human involvement is where the rubber meets the road. Big data can only provide real benefits when actionable insight is provided by analysts and human follow through is implemented with customer contact agents.