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May 5, 2015

IoT Analytics: A Game Changer for Customer Support

Puneet Pandit

Customer service costs continue to increase, driven by the greater complexity of today’s products, growing compensation costs for personnel, and higher expectations of quality service among customers. Companies may be tempted to cut back on the quality of support to save money, but there’s a better option: IoT analytics.

Many new products offered today are more complex than the ones they replace. Many customers, VARs, and distributors today don’t have the time or expertise to solve problems with products they purchase, so they simply call customer service.

While the demands placed on customer support are greater than ever, the personnel charged with delivering support struggle. According to Aberdeen, the typical contact center agent spends 26 percent of her time just looking for data, while ICMI tells us that the typical agent uses five screens for each customer interaction.

Maintaining a high level of customer service in this environment is difficult, but vitally important. History tells us that the average person who has a negative experience tells 16 people about it, while the typical person with a positive experience only tells nine. Trying to win back disgruntled customers is harder and more expensive than keeping existing customers happy.

To keep customer service costs low, most organizations create levels of customer service people. The most junior people, call them Level 1, field initial calls in the hope they can address the customer’s question or problem. If this person cannot address the issue, he forwards the call to a more senior person. This process may proceed through several more levels until the customer’s problem is addressed.

Clearly, as the customer’s issue escalates from a Level 1 customer support person to Level 2, Level 3 and beyond, the cost to address the problem increases. More senior customer service people, often engineers, are more expensive than Level 1 customer service people (who are typically not engineers).

But what if the company could dramatically streamline customer service by ensuring L1 and L2 customer service teams use automated tools that allows them to handle the vast majority of calls leaving more senior teams to focus on more productive activities? That’s the promise posed by IoT analytics.

Enter the IoT

The Internet of Things (IoT) is one of those phrases, like big data, that if you put 10 people in a room and ask them to define what it means, you get 10 definitions. Simply put, the IoT is the concept that many of today’s machines, systems, and devices have the ability to communicate with other machines, systems, and devices and produce massive amounts of data in the process.customer_service_rep

New, powerful information analytics platforms exist that collect, distill, analyze, and present massive amounts of operational data in easy-to-digest formats. These automated solutions can help customer support operations in three ways:

  1. Proactive: Applying rules to incoming data and taking proactive action, such as opening up a customer case, dispatching a part, and alerting a support team to take preventive action
  1. Predictive: Uncovering failure rates before failures happen or identifying up-sell opportunity before customers ask for upgrades. Essentially, before an incident happens, customer service or field ops teams become aware that an incident is “about to happen” and can address a problem
  1. Prescriptive: Before an incident happens, the platform provides information that provides a remedy engineering teams can implement to prevent the incident through an interface with knowledge base and providing tips to avoid a potential problem

These insights can help customer service teams reduce mean time to resolution (MTTR), thereby enhancing customer satisfaction, elevating product reputation, and increasing customer service efficiencies.

Focusing on Service

For many product companies, competition based on product performance and price is fierce, and managers are continuously looking for competitive differentiators. Today’s IoT analytics solutions can enhance the performance and efficiency of customer service (as well as many other disciplines). Think about the ability to guarantee customer service response times, maximize the number of customer service calls handled by more junior support teams, and reduce customer service calls overall by predicting incidents before they happen and handling them–without customers even knowing about them.

The benefits of superior customer service go beyond simply increasingly loyalty among existing customers.  Superior service stabilizes the existing customer base while management attracts new customers through its sales and marketing efforts.  It provides an invaluable learning experience to identify product user and related trends that product marketers can incorporate into new products.  Superior services increases pride among the company’s workforce, improving morale and productivity, while reducing turnover.   Among public companies, the improvements outlined above lead to a healthier business that increases shareholder value.

 

About the author: Puneet Pandit is the founder and CEO of Glassbeam, a provider of machine data analytics software. Prior to Glassbeam, he was founder and CEO of Orchesys, a professional services firm focused on enterprise storage solutions. Glassbeam was incubated inside Orchesys and launched in 2009. Prior to Orchesys, Puneet led the Database and Business Applications Solutions Group at NetApp. He also worked at Ernst & Young strategic advisory services and Tata Unisys as a management consultant. Puneet holds an MBA from University of Chicago and graduated with Electrical Engineering degree from Punjab University.

 

Related Items:

The Secret to Generating Value from IoT Data

Hacking Your Way Onto the IoT

It’s Sink or Swim in the IoT’s Ocean of Bigger Data

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