ERP Insights >> Magazine >> December - 2015 issue
IoT Analytics: A Game Changer for Improved Product Development and Customer Service
Author : Puneet Pandit
Friday, January 8, 2016
Two business disciplines that are significantly challenging for many companies today are new product development and customer support. New product failure rates hover in the 40 percent range, much too high given the amount of information on customer preferences available today. At the same time, customer service costs continue to increase, driven by the greater complexity of today's products, higher salary costs for customer service personal and higher expectations for quality service among customers.
IoT analytics provide new, deeper insights that enable product development and customer service teams to perform better, enhance customer satisfaction and reduce costs.
Machine data has traditionally been difficult to collect and analyze, due to both the enormous amounts and different types of data involved. Data comes in a variety of forms, such as text logs, XML, JSON, CSV or SNMP. There are also different data class categories, such as event messages, configuration blobs and statistical dumps. Data is also likely to be in different protocols, such as email, FTP, SFTP, as a stream or as a batch log file.
Advanced analytics companies have developed new solutions that are able to handle the volumes and disparate types of data involved, making machine data analytics practical and affordable for a much wider range of companies. To solve the challenge of rapidly digesting and analyzing large volumes of disparate data, advanced analytics, companies have developed hyper scale platforms and analytics languages to parse, tag, model and store data. These languages provide a single step solution for parsing data and storing it in a structured data store. Once in the data store, analytics team can combine this data with other company data sources, such as bug databases, knowledge databases, et al.
Critical to developing and marketing new products or enhancing customer service practices is creating a "single point of truth," a body of data and insights that is comprehensive, accurate and timely. These data/insights will provide all disciplines within the company the information they need to make critical decisions. Essential elements of an information platform to provide this single point of truth include:
- A centralized data repository that can capture terabytes of structured and unstructured operational data.
- Analytical tools that can describe and create meaning and relationships between elements in the data.
- Reporting capability on how customers are using existing products, performance information on components within existing products, et al.
- Dashboards and similar elements that enable new product development and other teams to create customer analyses and drill downs.
IoT Analytics Bring New Levels of Innovation to New Product Development
What if those responsible for designing new products could understand which features are most and least popular, known as 'feature propensity,' which components tend to fail sooner than others, how customers actually use products versus how product designers think they use them, and similar insights. And, what if product developers could then utilize these insights to develop new products that perform better, potentially cost less and most importantly are aligned with actual customer needs.
Trane, which designs and produces HVAC equipment, began to insert sensors into its equipment and the data analysis enabled facility managers to think of and operate various HVAC and other devices as a single large system versus a series of individual machines. For example, rather than simply turning on at the beginning of the day and off at the end, a "smart" HVAC system, based on IoT analytics, now operates based on the building's occupancy. In the future, Trane plans to sell "outcomes" versus equipment; for example., ensuring the air within a facility remains at 72 degrees.
IoT Analytics Facilitate Customer Service Efficiency
Many new products offered today are more complex than the ones they replace. At the same time, with management teams still leaner than before the recession, many customers, VARs and distributors don't have the time or expertise to solve problems with products they purchase and simply call customer service.
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 can not address the issue, he forwards the call to a more senior person. This process may proceed through 3-4-5 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, 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).
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?
Meru Networks, a market leader in the development of mobile access and virtualized WiFi solutions, was eager to reduce the time required to diagnose customer support issues. By applying an IoT analytics solution, customer service leaders were able to quickly convert mounts of log files into a graphical dashboard and reports. This information enabled Meru to identify key trends, exceptions and events requiring immediate attention. The application enables Meru's team to both quickly resolve escalated customer service issues and provide proactive support as part of network health check that customers receive if they subscribe to a higher level of support.
For many companies, competition today is based on product performance and customer service. The ability to develop new products faster that more completely meet and exceed customer needs, supported by high quality customer service that can predict and address incidents before they happen- is a true game changer.
IoT Analytics: A Game Changer for Improved Product Development and Customer Service
Author : Puneet Pandit
Friday, January 8, 2016
Two business disciplines that are significantly challenging for many companies today are new product development and customer support. New product failure rates hover in the 40 percent range, much too high given the amount of information on customer preferences available today. At the same time, customer service costs continue to increase, driven by the greater complexity of today's products, higher salary costs for customer service personal and higher expectations for quality service among customers.
IoT analytics provide new, deeper insights that enable product development and customer service teams to perform better, enhance customer satisfaction and reduce costs.
Machine data has traditionally been difficult to collect and analyze, due to both the enormous amounts and different types of data involved. Data comes in a variety of forms, such as text logs, XML, JSON, CSV or SNMP. There are also different data class categories, such as event messages, configuration blobs and statistical dumps. Data is also likely to be in different protocols, such as email, FTP, SFTP, as a stream or as a batch log file.
Advanced analytics companies have developed new solutions that are able to handle the volumes and disparate types of data involved, making machine data analytics practical and affordable for a much wider range of companies. To solve the challenge of rapidly digesting and analyzing large volumes of disparate data, advanced analytics, companies have developed hyper scale platforms and analytics languages to parse, tag, model and store data. These languages provide a single step solution for parsing data and storing it in a structured data store. Once in the data store, analytics team can combine this data with other company data sources, such as bug databases, knowledge databases, et al.
Critical to developing and marketing new products or enhancing customer service practices is creating a "single point of truth," a body of data and insights that is comprehensive, accurate and timely. These data/insights will provide all disciplines within the company the information they need to make critical decisions. Essential elements of an information platform to provide this single point of truth include:
- A centralized data repository that can capture terabytes of structured and unstructured operational data.
- Analytical tools that can describe and create meaning and relationships between elements in the data.
- Reporting capability on how customers are using existing products, performance information on components within existing products, et al.
- Dashboards and similar elements that enable new product development and other teams to create customer analyses and drill downs.
IoT Analytics Bring New Levels of Innovation to New Product Development
What if those responsible for designing new products could understand which features are most and least popular, known as 'feature propensity,' which components tend to fail sooner than others, how customers actually use products versus how product designers think they use them, and similar insights. And, what if product developers could then utilize these insights to develop new products that perform better, potentially cost less and most importantly are aligned with actual customer needs.
Trane, which designs and produces HVAC equipment, began to insert sensors into its equipment and the data analysis enabled facility managers to think of and operate various HVAC and other devices as a single large system versus a series of individual machines. For example, rather than simply turning on at the beginning of the day and off at the end, a "smart" HVAC system, based on IoT analytics, now operates based on the building's occupancy. In the future, Trane plans to sell "outcomes" versus equipment; for example., ensuring the air within a facility remains at 72 degrees.
IoT Analytics Facilitate Customer Service Efficiency
Many new products offered today are more complex than the ones they replace. At the same time, with management teams still leaner than before the recession, many customers, VARs and distributors don't have the time or expertise to solve problems with products they purchase and simply call customer service.
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 can not address the issue, he forwards the call to a more senior person. This process may proceed through 3-4-5 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, 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).
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?
Meru Networks, a market leader in the development of mobile access and virtualized WiFi solutions, was eager to reduce the time required to diagnose customer support issues. By applying an IoT analytics solution, customer service leaders were able to quickly convert mounts of log files into a graphical dashboard and reports. This information enabled Meru to identify key trends, exceptions and events requiring immediate attention. The application enables Meru's team to both quickly resolve escalated customer service issues and provide proactive support as part of network health check that customers receive if they subscribe to a higher level of support.
For many companies, competition today is based on product performance and customer service. The ability to develop new products faster that more completely meet and exceed customer needs, supported by high quality customer service that can predict and address incidents before they happen- is a true game changer.
IoT analytics provide new, deeper insights that enable product development and customer service teams to perform better, enhance customer satisfaction and reduce costs.
Machine data has traditionally been difficult to collect and analyze, due to both the enormous amounts and different types of data involved. Data comes in a variety of forms, such as text logs, XML, JSON, CSV or SNMP. There are also different data class categories, such as event messages, configuration blobs and statistical dumps. Data is also likely to be in different protocols, such as email, FTP, SFTP, as a stream or as a batch log file.
Advanced analytics companies have developed new solutions that are able to handle the volumes and disparate types of data involved, making machine data analytics practical and affordable for a much wider range of companies. To solve the challenge of rapidly digesting and analyzing large volumes of disparate data, advanced analytics, companies have developed hyper scale platforms and analytics languages to parse, tag, model and store data. These languages provide a single step solution for parsing data and storing it in a structured data store. Once in the data store, analytics team can combine this data with other company data sources, such as bug databases, knowledge databases, et al.
Critical to developing and marketing new products or enhancing customer service practices is creating a "single point of truth," a body of data and insights that is comprehensive, accurate and timely. These data/insights will provide all disciplines within the company the information they need to make critical decisions. Essential elements of an information platform to provide this single point of truth include:
- A centralized data repository that can capture terabytes of structured and unstructured operational data.
- Analytical tools that can describe and create meaning and relationships between elements in the data.
- Reporting capability on how customers are using existing products, performance information on components within existing products, et al.
- Dashboards and similar elements that enable new product development and other teams to create customer analyses and drill downs.
IoT Analytics Bring New Levels of Innovation to New Product Development
What if those responsible for designing new products could understand which features are most and least popular, known as 'feature propensity,' which components tend to fail sooner than others, how customers actually use products versus how product designers think they use them, and similar insights. And, what if product developers could then utilize these insights to develop new products that perform better, potentially cost less and most importantly are aligned with actual customer needs.
Trane, which designs and produces HVAC equipment, began to insert sensors into its equipment and the data analysis enabled facility managers to think of and operate various HVAC and other devices as a single large system versus a series of individual machines. For example, rather than simply turning on at the beginning of the day and off at the end, a "smart" HVAC system, based on IoT analytics, now operates based on the building's occupancy. In the future, Trane plans to sell "outcomes" versus equipment; for example., ensuring the air within a facility remains at 72 degrees.
IoT Analytics Facilitate Customer Service Efficiency
Many new products offered today are more complex than the ones they replace. At the same time, with management teams still leaner than before the recession, many customers, VARs and distributors don't have the time or expertise to solve problems with products they purchase and simply call customer service.
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 can not address the issue, he forwards the call to a more senior person. This process may proceed through 3-4-5 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, 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).
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?
Meru Networks, a market leader in the development of mobile access and virtualized WiFi solutions, was eager to reduce the time required to diagnose customer support issues. By applying an IoT analytics solution, customer service leaders were able to quickly convert mounts of log files into a graphical dashboard and reports. This information enabled Meru to identify key trends, exceptions and events requiring immediate attention. The application enables Meru's team to both quickly resolve escalated customer service issues and provide proactive support as part of network health check that customers receive if they subscribe to a higher level of support.
For many companies, competition today is based on product performance and customer service. The ability to develop new products faster that more completely meet and exceed customer needs, supported by high quality customer service that can predict and address incidents before they happen- is a true game changer.
