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Business Level Strategy Add in library

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Question:

Decisions about which business metrics to track and how to calculate those metrics influence the behavior of operations teams. One criteria we’re interested in tracking is how many emailed replies (touches) it takes for support agents to resolve an inbound support ticket.

We’ve explored a few ways of addressing this question and have narrowed the suite of possible metrics down to a list of two:

● % Single Touch Resolution

● Calculated as: Count of tickets resolved with 1 touch / Count of resolved tickets

● Average Touches Per Ticket

● Calculated as: Average count of touches for resolved tickets

1. Explain the differences between these two metrics

2. How might choosing one metric over the other influence behavior?
 
 

Answer:

% Single Touch Resolution

In response to the number of tickets solved in a single touch, the Percentage Single Touch Resolution is referred to the ratio of the number of count of tickets solved with a single touch to the total number of resolved tickets in the help desk[1].

% Single Touch Resolution = (Number of tickets resolved with 1 touch)/ (Total number of resolved tickets)

This estimation metric helps in determining the skills of the organization team in understanding the concerns of a particular ticket and  providing quality solution to the customer of the concerned organization through its help desk such that the resolution satisfies the customer on a single touch.

Average Touches per Ticket

In response to the number of tickets solved in a single touch, the Average Touches per Ticket is referred to the average count of touches that are required in order to resolve the tickets that come in a healthy help desk of a particular organization[2].

Average Touches per Ticket = Total number of touches for resolving the tickets/ Total number of tickets resolved

Thus, this estimation metric helps in determining the efficiency of the performance of the organization team in providing effective solution to the customers of the concerned organization through its help desk.

2. Influence of choosing one metric over the other

As mentioned earlier, the Percentage Single Touch Resolution estimation metric helps in determining the skills of the organization team in understanding the concerns of a particular ticket and  providing quality solution to the customer of the concerned organization through its help desk such that the resolution satisfies the customer on a single touch[3]. Thus, by implicating this metric in the business strategy, an organization could evaluate the knowledge capabilities of its employees and identify how efficient their resolutions are, which help in full satisfaction of the customers on a single touch.  By estimating the Percentage Single Touch Resolution, the company could identify its quality of the resolutions being provided by its members and hence, act in encouraging the knowledge share in its business environment in order to enhance the knowledge and performance of the employees for providing quality resolutions in the future.

Average Touches per Ticket, on the other hand, helps in measuring the touches (e-mail) required in order to satisfy a particular customer in respective of a particular ticket[4]. By implicating this metric within the business strategy, an organization could evaluate its resolving capabilities in respective of solving a particular ticket through its help desk.

To conclude, choosing Percentage Single Touch Resolution over the Average Touches per Ticket would influence the organization to focus more on the quality of the resolution being provided to its customers. On the other hand, choosing Average Touches per Ticket would enable the organization to focus more on its resolving capabilities. Thus, Percentage Single Touch Resolution metrics display more of a qualitative approach whereas; the Average Touches per Ticket illustrates the quantitative approach of an organization in particular.

 

References

Jammalamadaka R, Mehrotra S and Venkatasubramanian N, "Protecting Personal Data From Untrusted Web-Based Data Services" (2011) 2011 Network Security

Kuss O and Dickel H, "A Confidence Interval For The Difference Between Two Reaction Indices" (2011) 64 Contact Dermatitis

Qiang Wang and Yingchao Shen, "Web-Based: A Data Warehouse On Osteoporosis Data Warehouse In The Osteoporosis Community Health Information Management System" (2013) 06 Journal of Biomedical Science and Engineering.

Wang Q and Shen Y, "Web-Based: A Data Warehouse On Osteoporosis Data Warehouse In The Osteoporosis Community Health Information Management System" (2013) 06 Journal of Biomedical Science and Engineering

[1] Bettina Forster and Helge Gillmeister, "ERP Investigation Of Transient Attentional Selection Of Single And Multiple Locations Within Touch" (2010) 48 Psychophysiology.

[2] Ravi Chandra Jammalamadaka, Sharad Mehrotra and Nalini Venkatasubramanian, "Protecting Personal Data From Untrusted Web-Based Data Services" (2011) 2011 Network Security.

[3] Qiang Wang and Yingchao Shen, "Web-Based: A Data Warehouse On Osteoporosis Data Warehouse In The Osteoporosis Community Health Information Management System" (2013) 06 Journal of Biomedical Science and Engineering.

[4] Oliver Kuss and Heinrich Dickel, "A Confidence Interval For The Difference Between Two Reaction Indices" (2011) 64 Contact Dermatitis.

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