Using AI as the primary worker in a production environment is hard. This series shares our journey in solving specific challenges.

The Task

Determine the perceived customer sentiment based on their messages.

🔔In customer service, sentiment can impact how a conversation is serviced. In most businesses, a “Neutral” sentiment should characterize the vast majority of conversations. Other sentiments, such as “Happy” or “Upset,” are used to flag conversations for unique service flows. Generally, sentiment ratings should lean towards neutrality.

Requirements

1. Agent must determine a customer’s sentiment from a list of available options, based on their messages.

2. Agent must respond in a pre-defined format.

Round 1

We are using ChatGPT 4 as the AI to power our agent. We will start by generating a small set of test emails. Emojis will indicate the sentiment I believe to be accurate.

Message 1: Frustrated subscriber - 😠

Subject: Order status
I put my monthly subscription on pause 2 months ago. I have NOT TAKEN IT OFF PAUSE, as I still have alot of product. PLEASE cancel this. When I'm low on stock will do the automatic monthly schedule again. PLEASE confirm this correction by email. 

Message 2: Exchange please - 😐

Subject: Order issue
Hello! I ordered 4 boxes of candles and I don’t care for the blue or pink. Can I exchange these for a different color? The order number is 568792. Thank you!

Message 3: Stuck order - 😐

Subject: Request for help
My order has been stuck in transit.

Message 4: Where is my order - 😐

Subject: Your order Shipped!
Placed my order 8 days ago and still don't have it. Any updates?

Message 5: Great order - 😀

Subject: Order received
Just got my order. Everything in it is so GREAT! Thank you!

Message 6: Answer me - 😠

Subject: Order received
This is my third message with no response. Why can I not get help?

Prompt

For our first prompt, we will start with a very simple and direct approach.

What is the customers sentiment based on their message?
options:
"Happy"
"Neutral"
"Upset"

  ---Start of Message---
Subject: Order status

I put my monthly subscription on pause 2 months ago. I have NOT TAKEN IT OFF PAUSE, as I still have alot of product. PLEASE cancel this. When I'm low on stock will do the automatic monthly schedule again. PLEASE confirm this correction by email. 
  ---End of Message---

Response

I executed the above prompt for each test message. Here are the responses.

Messages

1. Frustrated subscriber - "Upset" 👍

2. Exchange please - "Neutral" 👍

3. Stuck order - "Upset" 👎

4. Where is my order - "Upset" 👎

5. Great order - "Happy" 👍

6. Answer me - "Upset" 👍

Results

1. 👎Agent must determine a customer’s sentiment from a list of available options, based on their messages.

2. 👍Agent must respond in a pre-defined format.

The agent responded using the correct format each time. Its sentiment alignment matched our expectations in four out of six instances, with discrepancies occurring in messages 3 and 4, where we anticipated a “Neutral” sentiment instead of an “Upset” one.

Below are its justifications for the differing sentiments.

Message 3

The customer's message indicates a problem with their order not being delivered as expected, which suggests that they are not fully satisfied or happy. This typically characterizes an upset sentiment.

Message 4

The customer's message indicates dissatisfaction with the fact that they placed an order 8 days ago and still haven't received it, hence their sentiment is upset.

🔔In a typical context, these justifications are sensible. However, in customer service, nearly every message relates to a problem. Therefore, to be effective, we need to tune sentiment analysis to identify customers who have explicitly expressed their upset feelings, rather than inferring that their circumstances justify an upset state.

Round 2

Let's take an incremental step and define the sentiment options. The prompt will attempt to categorize messages as “Happy” or “Upset”, defaulting to “Neutral” for all others.

What is the customers sentiment based on their message?
options:
"Happy" - Message expresses exuberant joy or immense appreciation.
"Upset" - Message directly expresses anger or extreme frustration.
"Neutral" - Message does not qualify as Happy or Upset.

  ---Start of Message---
Subject: Order status
I put my monthly subscription on pause 2 months ago. I have NOT TAKEN IT OFF PAUSE, as I still have alot of product. PLEASE cancel this. When I'm low on stock will do the automatic monthly schedule again. PLEASE confirm this correction by email. 
  ---End of Message---

Response

Messages

1. Frustrated subscriber - "Upset" 👍

2. Exchange please - "Neutral" 👍

3. Stuck order - "Neutral" 👍

4. Where is my order - "Upset" 👍/👎

5. Great order - Happy 👍/👎

6. Answer me - "Upset" 👍

Results

1. 👍Agent must determine a customer’s sentiment from a list of available options, based on their messages.

2. 👍Agent must respond in a pre-defined format.

The results are not exactly what I was looking for, but they could work. In the case of criteria 2, the quotations around a specific word were inconsistently applied in message 5. I will overlook this issue, as our platform is capable of correcting it.

This time, the sentiment aligned with expectations in five out of six instances. The exception was message 4, which was still categorized as “Upset” instead of “Neutral”. However, considering that the wording of message 4 does express frustration, this categorization might actually be correct. Score one for the AI sticking to its point.🫡

Validation

I tested this prompt across a series of actual customer messages. Below are a few examples, with personally identifiable information removed for privacy.

1. Re: Thank you for your order!

Hi!
I edited the order earlier this week to blue, red, green yellow, and purple. The website showed it as "saved" at the time, and I'm surprised that it has now reverted to the old set. Can you please change that for the current delivery?

Result 👍

"Neutral"

2. Request from Bob

My order has been stuck in transit

Result 👎

"Upset"

3. Re: Your Order Has Shipped!

Hello Team

Order: 154687

The tracking mentions that the product has been delivered. Unfortunately I did not receive any package. I have waited for a few days as well however no luck. I would like to request a refund. I shall order again soon :)

Thank you

Result 👎

"Upset"

Analysis

The prompt is still biased towards classifying messages as “Upset” when “Neutral” is desired. The AI continues to interpret the underlying justification for being upset, rather than the actual expression of sentiment.

Round 3

I will try to refine the criteria for “Upset” by adjusting its definition and redefining “Neutral” to include expressions of a problem.

What is the customers sentiment based on their message?
options:
"Happy" - Message expresses exuberant joy or immense appreciation.
"Upset" - Message directly and strongly expresses anger or extreme frustration.
"Neutral" - Message relays a problem but does not directly express negativity or does not qualify as Happy or Upset. 

  ---Start of Message---
Subject: Order status
I put my monthly subscription on pause 2 months ago. I have NOT TAKEN IT OFF PAUSE, as I still have alot of product. PLEASE cancel this. When I'm low on stock will do the automatic monthly schedule again. PLEASE confirm this correction by email. 
  ---End of Message---

Response

Messages

1. Frustrated subscriber - "Upset" 👍

2. Exchange please - "Neutral" 👍

3. Stuck order - "Neutral" 👍

4. Where is my order - "Neutral" 👍

5. Great order - “Happy” 👍

6. Answer me - "Upset" 👍

Results

1. 👍Agent must determine a customer’s sentiment, from a list of available options, based on their messages.

2. 👍Agent must respond in a pre-defined format.

The results meet the originally expected criteria. The AI also now aligns with my original expectation for message 4. So much for sticking to it’s point… 😞

Validation

I again tested the prompt with a series of actual customer messages. Below are the results, compared against the previous validation examples.

1. Re: Thank you for your order!

Hi!
I edited the order earlier this week to blue, red, green yellow, and purple. The website showed it as "saved" at the time, and I'm surprised that it has now reverted to the old set. Can you please change that for the current delivery?

Result 👍

"Neutral"

2. Request from Bob

My order has been stuck in transit

Result 👍

"Neutral"

3. Re: Your Order Has Shipped!

Hello Team

Order: 154687

The tracking mentions that the product has been delivered. Unfortunately I did not receive any package. I have waited for a few days as well however no luck. I would like to request a refund. I shall order again soon :)

Thank you

Result 👍

"Neutral"

Analysis

The prompt is performing as expected, differentiating between expressions of problems and inferred anger.

Conclusion

Our AI agent is now ready to attempt sentiment inference, a challenging task. A key insight was refining the AI's empathy to distinguish between direct expressions of being upset vs assuming an upset state when circumstances justify such feelings.

AI Powered Operations with MelodyArc CX

MelodyArc CX is the world's first all-in-one customer operations platform, reducing operating costs by >50%. It's a single end-to-end solution that handles daily customer operations by providing both agents and AI together in one powerful platform.

MelodyArc CX layers on top of your existing CRM and other customer service channels to manage all the moving parts. From responding to and resolving customer requests, to implementing and cascading policies, to forecasting and analytics, MelodyArc CX takes on daily operations. It's more cost effective than agents alone and ensures higher resolution rates than automation. 

Pay-as-you-go pricing with no minimum commitment means MelodyArc flexes with your business.

Book a demo today to learn more.

Using AI as the primary worker in a production environment is hard. This series shares our journey in solving specific challenges.

The Task

For retail e-commerce customer support, when a customer is asking for order support, the Agent needs to identify which items a customer is referencing.

Requirements

1. Agent must correctly identify all items referenced by the customer.

2. Agent must respond in a pre-defined format.

🔔 In live operations, an AI agent can ask a human agent for help if it is not confident which items to select.

Round 1

To begin, we have chosen ChatGPT 4 as the AI to power our agent. We will start with a straightforward task: developing a response mechanism using a simple customer message and order set. Our focus will be on defining how data is presented to the AI and how it interprets and responds to this data

🔔MelodyArc AI agents run using APIs. Results can differ when using ChatGPT APIs vs the UX.

Prompt

  Your Role: As an expert customer service agent, your primary task is to interpret customer emails and deduce the referenced items from the details given, creating a response in the form of JSON objects.
  
  Instructions:
  
  1. Analyze the Message: Carefully read the provided customer message. Read the entire message before proceeding.

  2. Analyze available Item options: Items that the customer ordered will be presented in this format.

  "Order Number \${order_id} placed on \${placed_timestamp} for item: \${item_name}(id \${item_id}) x \${quantity}, total \${price}. The shipment is \${order_status}"
      
  3. Selection: Based on the message, identify the item(s) that the customer is referencing. Please bear in mind that the customer might be referring to multiple items.

  4. Formulate JSON Object: Provide your results in a JSON object structured as:
  
  {
     "item_id": "<item_id for referenced item>",
     "item_name": "<item_name for referenced item",
     "order_id": "<order_id for referenced item>",
     "quantity": "<quantity for referenced item>"
  }
  
  5. Respond with the final JSON object, do not include any other text or comments.
  
  Example response:
    
  [
    {
      "item_id": "ITEM12345",
      "item_name": "Example Product A",
      "order_id": "ORD012345",
      "quantity": 1
    },
    {
      "item_id": "ITEM52365",
      "item_name": "Example Product B",
      "order_id": "ORD013378",
      "quantity": 2
    }
  ]

  ---Start of Message---
Subject: Need help

I hope this email finds you well. My name is Jake, and I am writing to request an update on the shipment status of my recent order.
  ---End of Message---
  ---Start of Items---
Order Number 20231115-001 placed on 2023-11-15T10:45:00 for item: Stainless Steel Water Bottle (id 987) x 1, total $15.99. The shipment is not fulfilled.

Order Number #20231114-045 placed on 2023-11-14T14:30:00 for items: Bluetooth Headphones (id 456) x 1, total $89.99, Portable Charger (id 321) x 2, total $39.98. The shipment is in-transit.

Order Number #20231113-123 placed on 2023-11-13T08:15:00 for items: Wireless Mouse (id 789) x 1, total $24.99, USB-C Cable (id 654) x 3, total $29.97, Laptop Sleeve (id 532) x 1, total $19.99. The shipment is delivered.
  ---End of Items---

Response

[
  {
    "item_id": "987",
    "item_name": "Stainless Steel Water Bottle",
    "order_id": "20231115-001",
    "quantity": 1
  },
  {
    "item_id": "456",
    "item_name": "Bluetooth Headphones",
    "order_id": "20231114-045",
    "quantity": 1
  },
  {
    "item_id": "321",
    "item_name": "Portable Charger",
    "order_id": "20231114-045",
    "quantity": 2
  },
  {
    "item_id": "789",
    "item_name": "Wireless Mouse",
    "order_id": "20231113-123",
    "quantity": 1
  },
  {
    "item_id": "654",
    "item_name": "USB-C Cable",
    "order_id": "20231113-123",
    "quantity": 3
  },
  {
    "item_id": "532",
    "item_name": "Laptop Sleeve",
    "order_id": "20231113-123",
    "quantity": 1
  }
]

Results

1. 👎Agent must correctly identify all items referenced by the customer.

2. 👍Agent must respond in a pre-defined format.

It did great in adhering to the format but was not able to associate the customer’s request for “my recent order”. The AI is taking a broader view of what recent means, and is biasing to error on the side of selecting too much vs too little. However, this highlights a gap in understanding compared to a human agent.

Round 2

I tightened interpretation of “recent” with explicit instructions on how to handle “singular terms” in Step 3.

In cases where the customer uses singular terms to refer to their order, such as 'my order', 'the order', or 'my recent order', prioritize identifying only one order that most closely matches the description. If multiple orders could fit but the customer's reference is singular, select the most recent or the closest to completion order.

Prompt

  Your Role: As an expert customer service agent, your primary task is to interpret customer emails and deduce the referenced items from the details given, creating a response in the form of JSON objects.
  
  Instructions:
  
  1. Analyze the Message: Carefully read the provided customer message. Read the entire message before proceeding.

  2. Analyze available Item options: Items that the customer ordered will be presented in this format.

  "Order Number \${order_id} placed on \${placed_timestamp} for item: \${item_name}(id \${item_id}) x \${quantity}, total \${price}. The shipment is \${order_status}"
      
  3. Selection: Based on the message, identify the item(s) that the customer is referencing. Please bear in mind that the customer might be referring to multiple items.

In cases where the customer uses singular terms to refer to their order, such as 'my order', 'the order', or 'my recent order', prioritize identifying only one order that most closely matches the description. If multiple orders could fit but the customer's reference is singular, select the most recent or the closest to completion order.

  4. Formulate JSON Object: Provide your results in a JSON object structured as:
  
  {
     "item_id": "<item_id for referenced item>",
     "item_name": "<item_name for referenced item",
     "order_id": "<order_id for referenced item>",
     "quantity": "<quantity for referenced item>"
  }
  
  5. Respond with the final JSON object, do not include any other text or comments.
  
  Example response:
    
  [
    {
      "item_id": "ITEM12345",
      "item_name": "Example Product A",
      "order_id": "ORD012345",
      "quantity": 1
    },
    {
      "item_id": "ITEM52365",
      "item_name": "Example Product B",
      "order_id": "ORD013378",
      "quantity": 2
    }
  ]

  ---Start of Message---
Subject: Need help

I hope this email finds you well. My name is Jake, and I am writing to request an update on the shipment status of my recent order.
  ---End of Message---
  ---Start of Items---
Order Number 20231115-001 placed on 2023-11-15T10:45:00 for item: Stainless Steel Water Bottle (id 987) x 1, total $15.99. The shipment is not fulfilled.

Order Number #20231114-045 placed on 2023-11-14T14:30:00 for items: Bluetooth Headphones (id 456) x 1, total $89.99, Portable Charger (id 321) x 2, total $39.98. The shipment is in-transit.

Order Number #20231113-123 placed on 2023-11-13T08:15:00 for items: Wireless Mouse (id 789) x 1, total $24.99, USB-C Cable (id 654) x 3, total $29.97, Laptop Sleeve (id 532) x 1, total $19.99. The shipment is delivered.
  ---End of Items---

Response

[
    {
      "item_id": "987",
      "item_name": "Stainless Steel Water Bottle",
      "order_id": "20231115-001",
      "quantity": 1
    }
]

Results

1. 👍Agent must correctly identify all items referenced by the customer.

2. 👍Agent must respond in a pre-defined format.

Got both parts right. I am concerned that the “singular terms” instruction might bias the prompt to this exact message. We will check for that in our validation tests.

Validation

I tested this prompt across a series of actual customer messages. Below are a few examples with sanitized data.

1. Re: A shipment from order #20231113-123 has been delivered

Hi,

It says my order was delivered on 11/15 but I have not received it and it has been a few business days since then. Please help!

All the best,
Jack

Result 👍

[
  {
    "item_id": "789",
    "item_name": "Wireless Mouse",
    "order_id": "20231113-123",
    "quantity": "1"
  },
  {
    "item_id": "654",
    "item_name": "USB-C Cable",
    "order_id": "20231113-123",
    "quantity": "3"
  },
  {
    "item_id": "532",
    "item_name": "Laptop Sleeve",
    "order_id": "20231113-123",
    "quantity": "1"
  }
]

2. Missing item

Hello,

I received the large box of packets that I ordered, but the cable was not included in the shipment. Is it still being sent?

Thank you,
Jack

Result 👍

[
  {
    "item_id": "654",
    "item_name": "USB-C Cable",
    "order_id": "#20231113-123",
    "quantity": 3
  }
]

3. Request exchange or refund

Hi, I did not realize the subscription was set as monthly. I am not ready for the next shipment to come in yet. Please cancel and refund order my last order

Thank you,
Jack

Result 👍

[
  {
    "item_id": "987",
    "item_name": "Stainless Steel Water Bottle",
    "order_id": "20231115-001",
    "quantity": 1
  }
]

Conclusion

Our AI agent seems ready to tackle item selection. A key learning was understanding how “recent” was interpreted by default and how we modified that understanding for e-commerce support.

AI Powered Operations with MelodyArc CX

MelodyArc CX is the world's first all-in-one customer operations platform, reducing operating costs by >50%. It's a single end-to-end solution that handles daily customer operations by providing both agents and AI together in one powerful platform.

MelodyArc CX layers on top of your existing CRM and other customer service channels to manage all the moving parts. From responding to and resolving customer requests, to implementing and cascading policies, to forecasting and analytics, MelodyArc CX takes on daily operations. It's more cost effective than agents alone and ensures higher resolution rates than automation. 

Pay-as-you-go pricing with no minimum commitment means MelodyArc flexes with your business.

Book a demo today to learn more.

On the Lex Fridman podcast in March 2023 (#367), Sam Altman pondered the potential of AI in job markets, particularly Ai in customer service. He speculated,

“I would say customer service is a category where I could see there being way fewer jobs relatively soon. I'm not entirely certain about that, but I could believe it ... like whatever call center employees are doing now.”

Since then, there's been a surge of startups harnessing ChatGPT and similar AI tools with the goal of revolutionizing customer service. Interestingly, no company has truly managed to deliver that quintessential human touch, the expertise we expect from a great human agent, using AI alone. Instead, the trend leans towards AI as a sidekick—assisting human agents. Why? Because customer service isn't just about answering questions. It involves understanding nuanced client specifics like policies, brand inclinations, and past interactions, then crafting the best solution to minimize customer effort and maximize satisfaction.

Table of Contents:

• Our Approach

• AI as the Primary Worker

• What’s Next

Our Approach

We founded MelodyArc in 2021, before the wave of generative AI took off. Looking around, we noticed the tech landscape was increasingly enamored with the idea of replacing humans with AI. But having been in the customer service field for years, we knew it was not an easy task. At MelodyArc, AI agents serve as the primary workers, offering efficiency and consistency in customer support. But there's a human element. Expert human agents stand by to augment the AI, validating, guiding, teaching, and adding that essential human touch to the AI's decisions.

AI as the Primary Worker

Our platform seamlessly integrates AI and human agents, drawing on the strengths of each. In the sections that follow, I'll delve into how our platform functions at a high level and share some key insights we've gathered along the way.

1. Internalizing external data

To have AI serve as the primary worker in customer support, it needs access to a client’s systems.

Like other platforms, we connect to client systems using APIs. Data is either pushed to us via webhooks or we retrieve it through triggered API calls. The primary entry point for this data is what we term the “ingress system”. This system channels tasks, such as customer messages, usually from popular ticketing platforms like Zendesk, Gorgias, or Kustomer directly into our platform.

Additionally, to enable AI and agents to actually take actions, not just generate responses, we also integrate with other platforms. This includes ecommerce solutions like Shopify, logistics providers like ShipBob, and tech tools such as Jira. For situations where an interface lacks API access, we've found workarounds, like using manual methods including webform submissions and custom crawler bots. That, however, is a topic for another time 😉

It's important to note that these integrations aren’t merely about data acquisition; they equip our platform with the context and capabilities to function. The combination of this data, and how MelodyArc utilizes it for both AI and human agents, makes our approach functional and efficient. We'll delve into the specifics of this utilization in the next section.

2. Servicing outside of straight lines

Delivering exceptional, on-brand customer support is complex. It's marked by multiple attributes: knowledgeable support from adept agents, precise adherence to brand voice, swift and accurate application of policies, and tailored experiences based on individual customer history.

Achieving this kind of support, especially on a large scale, is directly linked to a company's operational procedures and the proficiency of agents in understanding and executing those procedures. Traditionally, organizations capture these procedures in documentation akin to wikis. These pages often contain decision trees, rule charts, and flow diagrams outlining how to navigate various scenarios.

However, this model has its limitations. A primary challenge is the difficulty of keeping these resources updated. Changes in one area can cause a domino effect, necessitating updates in multiple interconnected areas. Additionally, transforming these complex updates into a format that's both human-readable and presentable is cumbersome.

Because of this, we took a different approach. Instead of formulating fixed procedure flows, we introduced “Points”—containers that encapsulate fragments of information, whether simple or complex. These Points can contain varied data: a list of available shipping statuses, rules determining if a shipment is on schedule, standard return policies, protocols for manual password resets, or even actionable commands like processing refunds or placing orders.

A Point is triggered when its predefined conditions or inputs are met. For instance, to utilize a Point focused on tracking a shipment, certain criteria like customer verification and the presence of a tracking number are required. Once triggered, a Point generally produces one or multiple outputs—like package tracking details in this instance.

These outputs can then become inputs for subsequent Points. This cyclical interaction continues until all triggerable Points have been activated.

Some Points are designed to decipher factors demanding qualitative judgment—assessing, for instance, the emotional tone of a customer's message or discerning their preference between a refund or a replacement. Addressing such qualitative queries demands the involvement of an agent, be it AI or human.Our AI-powered customer support platform alerts an agent whenever such Points are activated, and the cycle of input-output continues.   platform alerts an agent whenever such Points are activated, and the cycle of input-output continues.

The advantages of our Point-based system include:

1. Adaptability & Personalization: Instead of being tied down to a rigid procedure, Points allow the creation of dynamic, undirected graphs. This means each customer interaction can follow a unique, personalized path rather than a one-size-fits-all solution.

2. Simplified Updates: By revising or adding new Points, information can be easily updated in the system without disrupting the entire procedural architecture. This fluidity accommodates complexities that would otherwise overwhelm human agents.

3. Agents

The solutions above enable AI to assume the primary worker role. Here’s how our AI agents operate:

1. AI First: Every incoming task, like a new customer conversation, is assigned to an AI agent.

2. Navigating the Graph: The AI agent traverses the dynamic graph, created by the Points, to resolve the task. This includes making attempts to interpret and answer qualitative questions.

3. Asking for Help: If, at any point, the AI agent is unsure about a decision, it seeks help. Help might come from another, perhaps more specialized AI, or more often, from a human agent.

4. The Human Role: When called upon, a human agent provides specific inputs, refining the AI agent's understanding and bolstering its confidence in tackling Points.

5. Maintaining Ownership: Even with human input, the AI retains ownership of the task. It determines the best route to resolution, whether there’s one solution or several. If the AI ever struggles to meet a confidence threshold for a decision, it can always call upon its human counterparts for further guidance.

In practice, especially with new clients, many tasks initially require human agent reviews to ensure optimal outcomes. Over time, as the AI gathers more data and becomes familiar with client needs and nuances, its dependency on human oversight diminishes.

What’s Next

We've provided a high-level overview of MelodyArc platform’s implementation of AI for customer support. We'll delve deeper into individual parts in future entries. There is tons more to cover!

Reach out to learn how MelodyArc can be personalized to support your business.

Frequently Asked Questions

What are the benefits of using AI in customer support? 

AI in customer support helps both businesses and customers. Businesses save time, deliver more consistent results, and save money. Customers get faster answers, 24/7 support, and personalized interactions. It is worth noting, that the benefits of AI are reversed if the response does not answer the question asked by the customer, or reduce their effort to solve the problem. 

Pro tip: Like in carpentry, measure twice and cut once. If you don’t have a solid understanding of your actual customer service costs, start by calculating your Cost per Conversation (CPC) value.

Table of Contents

• Be Purposeful with Your Communication Channels

• Minimize Add-on Work

• Solve Customer Issues Fast

• Prevent Customer Conversations

Strategies to Reduce Customer Service Costs

Be Purposeful with Your Communication Channels

Customer service operates across a myriad of channels, both offline like email, social direct messaging, SMS, and online like voice, phone, and live chat. The cost implications are tied to the response time expected on these channels; quicker response expectations translate to higher costs due to the need for more customer service agents and a lower ability to queue up work. Online channels typically cost 2-3 times more than offline. Assess the ROI of live channels to ensure they are worth the additional costs compared to more affordable offline channels.

How to do it?

Generally, you should graduate to online channels after offline is performing great. "Great" offline performance looks like consistent response times of at least 24 hours or less and 24/7 coverage 365 days a year. Once you are there, then start with live chat. Run experiments with chat on and off to see how many customers choose that channel. Based on those results, determine if it is worth the cost.

Minimize Add-on Work

Inevitably, customer service agents are bogged down with additional tasks that don’t directly address customer needs. This includes things like documenting detailed conversation notes, filling out third-party forms, and the always-present logging within Excel or Google Sheets. These activities accumulate like barnacles to a boat. They are always launched with good intentions, usually to address “hot” problems, but are not free and add complexity, risking process errors. Challenge these activities to eliminate non-essential tasks.

How to do it?

Walk through the customer service process and document all steps. Identify the Add-on tasks and find the name of the person who mandated each task. Demo for them the task. There's a good chance they no longer need it. If they say it is needed, ask if they can share the benefit of that work. A good tactic is to inform them that you are going to share why this task is being done with the working team. This need to justify helps people internally question the value of the work they are prescribing.

Solve Customer Issues Fast

The number of back-and-forth messages in a conversation not only frustrates customers but also drives up costs. Strive to solve customer issues as close to the first message as possible. Common controllable drivers of high message count are misunderstanding what a customer is asking for, operating policies that add complexity, or the communication channel itself. For example, the conversational nature of live chat is better at faster resolution than offline channels like email.

How to do it?

Study customer conversations with high message counts. Seek out operating policies that force multiple messages. Like Add-on work, identify the names of those who mandate these policies and question the value. A common example is retailers often ask for photos of items reported as damaged. Question the purpose of this photo. The photo does not provide evidence of legitimacy as it can easily be faked. Discover if the photo produces other value such as for a supplier or carrier claim.

Prevent Customer Conversations

For customers, no conversation is better than a delightful conversation. Studying the reasons customers had to ask for help can point to solutions that may eliminate or reduce those reasons. Common solutions are addressing poor communication regarding updates such as shipment statuses, including messaging that points to self-service solutions, and identifying and challenging poor performing operational processes.

How to do it?

Much like previous strategies, prevention begins with studying your conversations. Label each conversation with a root cause, the primary reason the customer needed help. Identify top root causes and work with stakeholders to identify mechanisms that can, preferably, prevent the issue, or secondarily, reduce the customer’s effort to resolve. Enabling customer self-service is a common way to achieve the secondary objective.

Finally, remember that preventing conversations doesn’t reduce costs unless you actually reduce your costs. This usually means reducing spend on agent labor, or if all is going well, reducing the need to hire more labor as your business grows.

Bonus Strategy: Just use MelodyArc CX

MelodyArc CX is the world's first all-in-one customer operations platform, reducing operating costs by >50%. It's a single end-to-end solution that handles daily customer operations by providing both agents and AI together in one powerful platform.

MelodyArc CX layers on top of your existing CRM and other customer service channels to manage all the moving parts. From responding to and resolving customer requests, to implementing and cascading policies, to forecasting and analytics, MelodyArc CX takes on daily operations. It's more cost effective than agents alone and ensures higher resolution rates than automation. 

Pay-as-you-go pricing with no minimum commitment means MelodyArc flexes with your business.

Book a demo today to learn more.

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Frequently Asked Questions

Can employee training and development play a role in reducing customer service expenses? 

Yes, great training will ensure that your agent team is efficient and has high policy adherence, especially important when it comes to monetary compensation like concessions or refunds.  

Are there specific communication channels that are more cost-effective for customer service? 

Certain communication channels may be better suited for certain kinds of customer inquiries. For instance, for a complex issue, a phone call may actually be a more efficient way to resolve the customers issue if it takes a shorter time to resolve. However, communication channels start with the customer’s preference within the set of communication channels you offer. For instance, self-service may be the most cost-effective way to solve a return request, but the customer may prefer to request it via email.  

Table of Contents

• Simple Method: Labor Cost Analysis

• How to Calculate CPC

• Total Cost Method

• How to Improve CPC

Simple Method: Labor Cost Analysis

Step 1: Identify Labor Costs

Identify all labor costs associated with customer service during a given time period. This includes salaries, taxes, benefits, overtime, and fees for Business Process Outsourcing (BPO) services.

Step 2: Determine the Number of Conversations

Count the total number of customer conversations handled during the same time period. When choosing the time frame for the calculation, it's best to use a baseline that represents your operations. For example, if you often hire new agents, include a time period where new agents are being trained.

Step 3: Execute the Calculation

The formula is:

Example:

If the total labor cost for a quarter (3 months) is $60,000 and the number of conversations handled is 6,000, the CPC is $10:

How to Calculate CPC

CPC can vary significantly based on the size of the business, industry standards, and the efficiency of the customer service operation. However, a general benchmark is:

- For smaller businesses, a CPC range of $7 to $12 is common.

- For larger enterprises, a CPC range of $3 to $6 is typical due to economies of scale.

Comparing the calculated CPC against these benchmarks and industry averages provides a preliminary understanding of the cost efficiency of a customer service operation. Additionally, analyzing trends in CPC over time and benchmarking against industry standards or similar-sized businesses can provide a more nuanced understanding of the operational efficiency and where there might be room for improvement.

Total Cost Method

Step 1: Incorporate Additional Direct Costs

Include direct costs such as reverse logistics for return shipping, product costs for non-resellable items, and credit card transaction fees incurred during the time period.

Step 2: Account for Indirect Costs

Factor in indirect costs such as time spent on managing and hiring agents or overseeing BPO relationships.

Step 3: Execute the Calculation

The formula is:

How to Improve CPC

Improving CPC can be complex, especially when striving to maintain or enhance customer experience. Some straightforward tactics to reduce CPC include lowering labor costs through outsourcing, reducing service hours to consolidate work and minimize downtime, or implementing automations like chatbots. While these tactics may yield cost reductions, their impact on customer satisfaction must be carefully evaluated. We will delve deeper into these strategies in subsequent articles.

Simplify CPC Management with MelodyArc

You can eliminate the complexity of managing CPC with MelodyArc. MelodyArc offers a flat CPC rate with no minimums and no long-term commitments, simplifying the cost management aspect of customer service. This approach allows businesses to have a clear understanding of their customer service costs without the multifaceted calculations and management typically required.

Frequently Asked Questions

What are some common mistakes people make when calculating customer service costs? 

Watch out for hidden costs like office rent, team management, and benefits. Track your costs regularly to avoid surprises, as well as understand the root cause of deviations. By doing this, you'll see the real cost of customer service and make smarter choices for both your customers and your company. 

How often should I calculate my customer service costs? 

At least quarterly, but ideally monthly as part of a monthly business review. Track regularly for clear data, but don't overdo it, doing what’s best for you and your team.