CSAT

Why should you separate product and agent CSAT?

Why should you separate product and agent CSAT?
Content Manager & Customer Service Expert
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Why should you separate product and agent CSAT?

CSAT is one of the most popular satisfaction surveys. It's short, simple, and fairly intuitive.

This is why so many companies use it to gauge how satisfied their customers are with different aspects of their business.

Companies send out CSAT surveys to customers after notable customer lifecycle moments such as post-onboarding, after 6 months, prior to renewals, and following customer service interactions.

The last one - post-service conversations is a major application area of CSAT surveys.

Companies use it to understand how a customer service agent is performing by understanding how satisfactory customers are finding their interactions with an agent.

But is CSAT ratings really a true indication of agent performance?

We’re here to tell you that It’s not.

In this article, we will explore this idea in detail.

All CSAT ratings are not the same

Picture this.

Jack is a customer service agent at an online grocery delivery company.

He is assigned a live chat ticket where the customer is complaining about the quality of the product delivered.

He promptly attends to the customer, gathers all necessary information, and tries his best to help the customer.

The customer seeks a refund because he’s not happy with the quality of the cheese he’s received.

According to the company policy, perishable items are not eligible for return or refund.

He informs the company policy to the customer.

Jack was polite, and friendly, and answered all customer queries promptly.

Example of poor CSAT rating
Example of poor CSAT rating


The customer, however, was not pleased with the situation.

And although Jack did his best in the capacity of a service agent, the customer leaves a terrible CSAT rating.

This rating, directly attributable to Jack, makes it seem that Jack didn’t do a good job.

While the reality is quite different.

This is a common issue plaguing customer service.

Therefore, ratings received as a result of poor products and poor customer service can not be treated in the same way.

Why do you need to separate product and agent CSAT?

As seen in the example above, when companies receive CSAT ratings post customer service interactions, they all can not be treated as the same.

This is especially true when using CSAT ratings as agent performance indication.

Why?

There are several reasons for it, let’s look at some.

1. Wrong representation of service experience

Because of how CSAT surveys are framed, the negative responses are skewed towards the immediate experiences of customers.

These experiences could be from a bad customer service interaction, product problem, or operational failure.

It’s therefore important to not take the CSAT rating as an insight into a specific service interaction.

Positive and negative drivers of CSAT
Positive and negative drivers of CSAT

As you can see in the image above, all positive drivers are customer-service related, whereas, the negative ones are related to the product and operations.

2. Undue pressure on agents

Poor CSAT ratings could be due to a number of reasons.

So a CSAT rating post a customer service conversation should not be always attributed to the agent who handled the interaction.

In a scenario where customers leave bad CSAT ratings due to a product or operation issue, agents shouldn’t be held accountable.

Additionally, some topics always lead to bad CSAT no matter how an agent handles a customer.

This will differ depending on the type of business you're in.

For instance, for an e-Commerce company, there could be repeated product quality issues, payment-related issues, and so on.

Topics that always lead to low CSAT
Topics that always lead to low CSAT

In the image above, we should the probability of a bad CSAT rating for certain topics - no matter the quality of service.

3. Product teams can utilise their feedback better

It is important to note here that CSAT driver analysis often has a number of insights that the product teams would find useful.

However, hardly any product teams today look at CSAT or customer service conversations for insights.

Product related insights from CSAT driver analysis
Product related insights from CSAT driver analysis

Take for example a topic such as the app crashing repeatedly due to a specific reason.

If a number of users leave negative CSAT ratings because of it and if the company could dig into that specific reason, the product team could fix it leading to an overall good CX, improved CSAT rating, etc.

4. Customer service can improve with specific feedback

It is needless to say that if customer service teams knew what exactly is leading to bad CSAT ratings, they could work on those specific issues.

Customer service related insights from CSAT driver analysis
Service related insights from CSAT driver analysis


For example, if you could figure out the top service-related issue leading to bad CSAT, such as first response time, resolution time, etc, companies can tackle it objectively.

Rather than focusing solely on agent performance based on CSAT ratings they get.

Closing thoughts

By separating agent and product-related issues, you can easily point to the exact issues in each team.

By communicating these insights to the respective teams, you will be able to attack the true reasons for low CSAT ratings rather than blaming service agents.

This will not only lead to improved CSAT scores but also better agent productivity and ultimately elevated customer experience.

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CSAT

Why should you separate product and agent CSAT?

Piusha Debnath
Content Manager & Customer Service Expert
In this article
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Is your AI accurate, or am I getting sold snake oil?

The accuracy of every NLP software depends on the context. Some industries and organisations have very complex issues, some are easier to understand.

Our technology surfaces more granular insights and is very accurate compared to (1) customer service agents, (2) built-in keyword tagging tools, (3) other providers who use more generic AI models or ask you to build a taxonomy yourself.

We build you a customised taxonomy and maintain it continuously with the help of our dedicated data scientists. That means the accuracy of your tags are not dependent on the work you put in.

Either way, we recommend you start a free trial. Included in the trial is historical analysis of your data—more than enough for you to prove it works.

CSAT is one of the most popular satisfaction surveys. It's short, simple, and fairly intuitive.

This is why so many companies use it to gauge how satisfied their customers are with different aspects of their business.

Companies send out CSAT surveys to customers after notable customer lifecycle moments such as post-onboarding, after 6 months, prior to renewals, and following customer service interactions.

The last one - post-service conversations is a major application area of CSAT surveys.

Companies use it to understand how a customer service agent is performing by understanding how satisfactory customers are finding their interactions with an agent.

But is CSAT ratings really a true indication of agent performance?

We’re here to tell you that It’s not.

In this article, we will explore this idea in detail.

All CSAT ratings are not the same

Picture this.

Jack is a customer service agent at an online grocery delivery company.

He is assigned a live chat ticket where the customer is complaining about the quality of the product delivered.

He promptly attends to the customer, gathers all necessary information, and tries his best to help the customer.

The customer seeks a refund because he’s not happy with the quality of the cheese he’s received.

According to the company policy, perishable items are not eligible for return or refund.

He informs the company policy to the customer.

Jack was polite, and friendly, and answered all customer queries promptly.

Example of poor CSAT rating
Example of poor CSAT rating


The customer, however, was not pleased with the situation.

And although Jack did his best in the capacity of a service agent, the customer leaves a terrible CSAT rating.

This rating, directly attributable to Jack, makes it seem that Jack didn’t do a good job.

While the reality is quite different.

This is a common issue plaguing customer service.

Therefore, ratings received as a result of poor products and poor customer service can not be treated in the same way.

Why do you need to separate product and agent CSAT?

As seen in the example above, when companies receive CSAT ratings post customer service interactions, they all can not be treated as the same.

This is especially true when using CSAT ratings as agent performance indication.

Why?

There are several reasons for it, let’s look at some.

1. Wrong representation of service experience

Because of how CSAT surveys are framed, the negative responses are skewed towards the immediate experiences of customers.

These experiences could be from a bad customer service interaction, product problem, or operational failure.

It’s therefore important to not take the CSAT rating as an insight into a specific service interaction.

Positive and negative drivers of CSAT
Positive and negative drivers of CSAT

As you can see in the image above, all positive drivers are customer-service related, whereas, the negative ones are related to the product and operations.

2. Undue pressure on agents

Poor CSAT ratings could be due to a number of reasons.

So a CSAT rating post a customer service conversation should not be always attributed to the agent who handled the interaction.

In a scenario where customers leave bad CSAT ratings due to a product or operation issue, agents shouldn’t be held accountable.

Additionally, some topics always lead to bad CSAT no matter how an agent handles a customer.

This will differ depending on the type of business you're in.

For instance, for an e-Commerce company, there could be repeated product quality issues, payment-related issues, and so on.

Topics that always lead to low CSAT
Topics that always lead to low CSAT

In the image above, we should the probability of a bad CSAT rating for certain topics - no matter the quality of service.

3. Product teams can utilise their feedback better

It is important to note here that CSAT driver analysis often has a number of insights that the product teams would find useful.

However, hardly any product teams today look at CSAT or customer service conversations for insights.

Product related insights from CSAT driver analysis
Product related insights from CSAT driver analysis

Take for example a topic such as the app crashing repeatedly due to a specific reason.

If a number of users leave negative CSAT ratings because of it and if the company could dig into that specific reason, the product team could fix it leading to an overall good CX, improved CSAT rating, etc.

4. Customer service can improve with specific feedback

It is needless to say that if customer service teams knew what exactly is leading to bad CSAT ratings, they could work on those specific issues.

Customer service related insights from CSAT driver analysis
Service related insights from CSAT driver analysis


For example, if you could figure out the top service-related issue leading to bad CSAT, such as first response time, resolution time, etc, companies can tackle it objectively.

Rather than focusing solely on agent performance based on CSAT ratings they get.

Closing thoughts

By separating agent and product-related issues, you can easily point to the exact issues in each team.

By communicating these insights to the respective teams, you will be able to attack the true reasons for low CSAT ratings rather than blaming service agents.

This will not only lead to improved CSAT scores but also better agent productivity and ultimately elevated customer experience.

Frequently asked questions

Is your AI accurate, or am I getting sold snake oil?

The accuracy of every NLP software depends on the context. Some industries and organisations have very complex issues, some are easier to understand.

Our technology surfaces more granular insights and is very accurate compared to (1) customer service agents, (2) built-in keyword tagging tools, (3) other providers who use more generic AI models or ask you to build a taxonomy yourself.

We build you a customised taxonomy and maintain it continuously with the help of our dedicated data scientists. That means the accuracy of your tags are not dependent on the work you put in.

Either way, we recommend you start a free trial. Included in the trial is historical analysis of your data—more than enough for you to prove it works.

Do you integrate with my systems? How long is that going to take?

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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

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