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Can Data Analytics Save the World of Customer Service

We live in a world constantly bombarded by new information. From tweets and shares to online purchases, data is stored at an exponential rate. In fact, it is estimated that every two days we create as much information as was created from the beginning of humanity to 2003.

Unless this information is collected, interpreted and understood, it is literally and “virtually” useless.

However, there is one tool you can use to fully understand the wants and needs of your customers: data analytics.

Analytics became widely popular in the last decade during the rise of eCommerce and online shopping, using a customer’s recent purchases to predict other products they might be interested in.

Though successful in the eCommerce world, data analytics is seldom used in retaining customer loyalty. Customer service techniques have failed to adapt to this new methodology and have yet to fully optimize the tools and information at their disposal.

If companies harness even a fraction of the big data produced by their customers, their retention will improve astronomically.

Changing the Status Quo

Traditionally, customer service has been used as a middle-man between the customer and the company. If a customer is having an issue, it is up to the support representative to fix the problem as soon as possible. As they continue to purchase products and report problems, more and more information is compiled on each individual customer. However, this information is usually limited in scope and takes a long time to acquire.

As social media becomes an integral part in the lives of consumers, companies can see how they feel about the products they purchase. Though customer satisfaction data is stored in CRM applications, the raw and unfiltered truth lies within comments, blogs, posts and tweets. Instead of waiting for customers to come to you with their concerns, you now have the ability to identify and predict their needs with unmitigated accuracy.

Redefining the Voice of the Customer

Unfortunately, understanding and compiling this vast amount of information is not an easy venture. Much of the challenge lies within the fact that companies are having a difficult time using an analytics program that works well with their current CRM system. Though difficult in nature, here are a few ways to help optimize customer service techniques using data analytics.

Invest in Analytics

Currently, fewer than 10% of companies use big data to help analyze the needs and attitudes of their customers. This technology does not come cheap, but being able to sift through all of the noise that comes with big data is essential to understanding the true nature of your customers. Investing now will put you ahead of the curve, giving your organization a better understanding of your customer base as a whole.

React in Real Time

Being able to receive volumes of real-time data over your market is pointless if your customer service representatives don’t react as such. As our attention span dwindles, customers expect to be helped quicker and quicker. Seizing these opportunities at a moment’s notice is pivotal to retaining customer loyalty. By implementing a data platform that effectively unifies information, analytics and a recommended response, customer service agents are able to perform these necessary functions in real time.

Predict Consumer Patterns

Just as Jeff Bezos revolutionized the world of online shopping using predictive analysis, the same can be said with customer service techniques. There will be a point in time when customer’s will not only want a reaction to their needs in real time, but will also want you to predict their future needs as well. Even the simple task of transferring a call to the right department as it’s coming in can go a long way for customer loyalty.

In short, understanding your customers’ needs and attitudes enables you to respond to their problems more effectively and retain their loyalty. With a little faith and investing, data analytics can give you pave the way towards complete understanding.

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