Roger Swannell

Distributing customer service tickets by variation of complexity of query and abilities of agents

Deciding on a method for distributing customer service queries depends on an analysis of the tickets to understand how much variation there is the complexity of the queries and analysis of the agents to understand the variation in their ability.

This analysis isn’t to understand how complex the queries are, but to understand how different the queries are. If some of the queries are simple and some very complex, this is a high variation in complexity, and if all the queries are simple or all complex then this is low variation in the complexity. If the analysis of the difference in agents ability shows that some agents have lots of knowledge and ability whilst others have very low level of knowledge and ability then that is a high variation, but if all the agents are equally knowledgeable then the variation is low.
Assigning customer service agents

High variation in the complexity of queries and high variation in the abilities of agents

If there is a high degree of variation in the complexity of the queries, meaning some can be resolved easily in a few seconds whilst others take days or even weeks to reach resolution, and if the variation in the ability of the agents is high, due to some agents having lots of experience and others having a lower level of knowledge, then distributing tickets across the team using a load balancing method is best. This means that agents with more ability can be assigned and resolve more tickets than agents with lower ability who will be slower.

High variation in the complexity of queries and low with variation in the abilities of agents

If the queries the team receive have a high variation of complexity but there is a low variation in the abilities of the agents as they all have similar levels of knowledge and experience, then manually triaging tickets can often be the most efficient method of assignment. The second most efficient method would be load balancing as complex tickets will take longer to be resolved by a one low ability agent as much as any other low ability agent.

Low variation in the complexity of queries and high variation in the abilities of agents

If the majority of the queries are of a similar complexity (high complexity or low complexity) and there is a high variation in the abilities of the agents, because some are new to the role whilst others have more experience for example, then using a Skills based approach to assign tickets is an effective option. Agents with particular knowledge will be assigned tickets that require their expertise whilst agents with less experience will be assigned the tickets that only require more general skills.

Low variation in the complexity of queries and low variation in the abilities of agents

If the type of queries the team receive are mostly of a similar complexity, e.g. all about similar topics and the abilities of the agents are all approximately the same, which means they all resolve tickets at a similar rate, then using round robin to distribute tickets equally across the team is the most efficient method.

Inspecting and adapting my work schedule

I have a dashboard that tracks my time spent on the various pieces of work.

 

It allows me to ‘inspect and adapt’,  to question whether I’m spending my time on the things that return the most value, to think about how to fit future work in, and look for areas of work to not do.

I’d really like to find a way to pull the data from my Outlook calendar rather than having to manually enter it into a spreadsheet every week, but for the time being the dashboard is useful enough to commit the time to updating it.

Using location in chatbots

Using location in a chatbot can be really useful in providing information and services, but getting the coordinates into the chatbot isn’t as smooth and easy as perhaps we’d like.

Getting the user’s location starts with gaining permission for the device to send its longitude and latitude coordinates to the chatbot.

But if location is switched off, Messenger needs to ask for location to be switched on.

Clicking the button in Messenger triggers the device to seek permission to allow Messenger to access location.

But that doesn’t always work.

Once location is switched on Messenger can get the long/lat coordinates.

And then Messenger can pass the coordinates to the chatbot.

Once all those steps have been taken the chatbot can finally use the precise location to provide whatever information or services to the user.

Given how complicated getting that location is for the user we should question whether getting the precise longitude and latitude coordinates is the best option for obtaining the user’s location. If the chatbot only needs to know the region the user is in then simply asking the user could be a better option.

Different teams move at different paces

I drive a sports car. You drive a bus. I can get myself there quickly. You can get lots of people there slowly. You can’t make a bus handle like a sports car but you can tell people to get off the bus and find other means of transportation.

Boring Instagram: Animals Looking At You

The first of my Boring Instagram Collections is ‘Animals Looking At You’

Boring Instagram: Animals Looking At You

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