What is self-service automation? A guide from Dialpad's VP of CX

A few weeks ago, I needed to double-check the charges on my monthly phone bill. I tried the chatbot first, but it couldn't pull up my billing details, so I ended up calling in anyway and braced myself for an inevitable long wait.

You don't need to have worked in CX for a long time to know that making someone sit on the phone for 40 minutes just to ask a few simple questions is not a good customer experience.

So how do you improve it? Well, one way to do it is with self-service automation.

In this guide, I'm going to walk you through some best practices and common mistakes most businesses and organizations make when trying to implement self-service automation.

Let's get started.

Automated self-service: A quick primer

Automated self-service takes customer interactions that normally would have needed a human agent and automates them to not need that human intervention.

Example: People used to call companies, talk to a receptionist, and get manually routed by that person to the right department or person. Today, cloud phone systems have virtual receptionist or call routing features that let callers press a corresponding number on their keypad to route themselves to the right person or get answers to simple FAQs (frequently asked questions).

This is probably one of the simplest types of self-service automations. There's a wide range of business processes that you can automate.

Another slightly more advanced example of a self-service automation is the use of chatbots. Bots used to be pretty basic and could answer preset questions with preset answers.

Now, chatbots can not only search your company's knowledge base or FAQ pages to find answers to a greater variety of questions, they can even analyze customer sentiment on calls in real time and search unstructured sources of information too, like PDF documents and even past customer conversations.

Self-service automation has moved well beyond that kind of scripted, answer-only bot, though. Dialpad AI Agents are built to understand a goal, make a decision, and take action, not just retrieve an answer. Instead of routing a customer through a rigid decision tree, an AI agent can look up an order status, reschedule an appointment, update a record in your CRM, or confirm an identity, then hand off to a human agent when needed with full context carried forward: what the customer asked for, what's already been tried, and why they're escalating. That combination (autonomous action plus a seamless human handoff) is what separates today's self-service automation from the FAQ bots many people picture when they hear the term.

One of the biggest benefits of self-service automation, of course, is that it can help businesses save money, reduce the burden on human agents, and by extension, encourage customer experience teams to better plan out customer journeys and workflows as they're planning and setting up automations.

Common mistakes businesses make when implementing self-service automation

When you're trying to set up workload automations for your customer support team, it's important to look outside that immediate team as you're planning. (Not talking to your IT department? Might want to reconsider that.)

Here are a few pitfalls to watch out for:

Not creating an easy path to human interaction

As great as chatbots, AI agents, and other self-service automations are, they can't solve every question that your customers will come to you with.

Whether the question is for your support, sales, or IT team, you need to make sure the path to a human interaction is effortless.

There are few things as frustrating as trying to talk to a human when you've already gone through all the IVR menu options on a phone call, chatbot, or AI agent flow, and not being able to reach someone.

Is your chatbot's user interface easy to navigate, even for non-business users or non-tech-savvy customers? Do you have your most popular or frequently asked questions listed up front, or did you bury them at the end? What's the process for your AI agent to escalate to your human agents?

Make sure your AI platform for customer experience makes self-service management easy, with a clear path for customers to escalate to a human when needed. For example, Dialpad lets you build a chatbot or AI agent conversational flow with just a few clicks, including the option to escalate to a human.

Think of the times you've tried to contact a business and struggled to connect with a human. Do the opposite of that.

Over-complicating the point of entry

In the same vein, once customers know they want to reach out, don't overcomplicate the point of entry.

Think about the companies that make it almost impossible to connect with a human when you have a problem with an order. You'll answer questions and go through multiple decision trees, and if you're lucky, they might give you a number to call.

Compare that to companies that make it simple: you reach out with a problem, validate your account with a code sent to your phone, and you're talking to an agent in less than two minutes.

An AI agent can be just as strong a point of entry as a human, provided it's built to actually resolve things. A well-designed AI agent can verify who you are and handle routine requests immediately, so customers aren't left hunting for the one button that gets them to a person.

As a customer, which experience would you want to have? Although it takes a bit of work, that's what you have to keep in mind when planning your customer self-service automation.

Requiring too many steps to validate, without the ability to solve or deliver outcomes

Another barrier that pops up for customers that can complicate their experience is the problem of having too many validation steps.

I usually notice this most often in newer, less mature organizations that use many different applications, creating a wall of validation requirements between the customer and their resolution.

Specifically, I'd say four industries often tend to struggle the most with validation: healthcare, education, real estate, and financial services. And there's a good reason for this: these sectors are dealing with highly sensitive information, and to protect data, they've created multiple levels of validations.

But as you've probably guessed by now, this is a double-edged sword, and organizations in these industries are beginning to look at ways to improve the user experience.

For example, some companies are implementing geotags to validate you as a customer when you contact them for help. By comparing your location (with geotags) to the validation information you're giving them, they can confirm you're the person you say you are, which can get you past that validation process more quickly.

Not specifying to customers what can and can't be addressed with self-service

Have you ever started talking to a chatbot on a company's website only to find that the bot can't actually help you provision a new user for your company (for example) through chat, and that you have to call in for help instead?

To mitigate this potential problem, clearly state what self-service solutions your customers can use to solve different types of problems.

For example, before customers click on your AI customer service live-chat option, you might want to have a quick one-liner in the opening chat message that gives a few examples of the types of problems that someone can expect to solve through chat. When customers know the best channel to select for their issue, they'll spend less time waiting, and your agents won't have to transfer as many service requests.

This matters even more with AI agents, since they can take real actions instead of just answering questions. When Dialpad AI Agents reach the edge of what they can resolve, they hand off to a human agent along with full context: what the customer asked for, what's already been tried, and why it's escalating. Customers don't have to repeat themselves, and agents aren't starting from scratch.

5 best practices in self-service automation

To help you avoid the most common mistakes, below are some best practices I've found helpful, and maybe you will too as you develop your self-service automation solutions.

1. Identify the most common issues for customers

Before you can come up with a good self-service strategy, you need to understand the most common issues your customers have.

There are a few ways to determine this. You could do it the old-fashioned way and survey your customers over the phone manually or even make that your chatbot or AI agent's opening question.

If customers frequently call in for help with basic account information or connectivity issues, for example, you could set up a bot automation that offers answers for those topics. Your service desk or support team will likely appreciate these questions being taken out of the queue.

Alternatively, your contact center platform can also help you see these common questions. Dialpad Support for contact centers, for instance, lets you track how frequently certain keywords or topics come up on calls using Custom Moments.

Say you're getting a lot of questions about your refund policy lately, and you want to see just how often these questions come up. In Dialpad, you can create a Custom Moment to track every time "refund" and "money back" is said on a call, and see the trends, along with other metrics like hold times and call volume patterns, in your built-in analytics.

If you do notice any anomalies, you can click into the transcriptions for those calls to get more context.

Make sure you have someone owning knowledge management for support. Depending on how complex your organization is, it can be as simple as maintaining and updating your FAQs or online help center, regularly reviewing and expanding your agents' training materials, and keeping your AI agents' knowledge sources current so they're pulling from accurate, up-to-date information.

2. Have easily accessible escalation options

As I mentioned earlier, don't make it difficult for your customers to escalate their call or chat if they feel it's necessary. At the same time, you should be monitoring calls and chats for language that indicates an escalation is needed.

Our support team uses Dialpad and the nice thing is it helps with this too. The live sentiment analysis feature shows managers at a glance if any calls are at risk or in danger of going south.

This way, even if a supervisor is overseeing multiple agents and simultaneous calls, they can quickly see if they need to jump in.

There's also a service level alert feature, which automatically pings a contact center's managers if the service levels dip below a certain threshold.

3. Regularly review customer feedback

In a perfect world, self-service management would be easy and you'd be able to see customer feedback whenever you need to.

But that's probably not the case for most businesses (unless you have a dedicated self-service or IT automation-specific team).

With Dialpad Support, you can create CSAT (customer satisfaction) surveys that automatically trigger after a customer call.

One of the biggest challenges with collecting and leveraging CSAT scores is simply that not a lot of people actually fill out those surveys. Traditional post-call surveys often see response rates as low as 5%.

Often only your angriest (and happiest) customers actually bother to fill out these surveys, which means your CSAT responses are likely to be extremely skewed and not representative of what your overall audience actually thinks of your business.

Dialpad's AI CSAT feature is designed to solve exactly that. Our AI can transcribe calls and analyze sentiment in real time, and can also infer CSAT scores for 100% of your customer calls, with roughly 87% accuracy in predicting satisfaction from the call transcript. The result is a more representative sample size for CSAT scores, and a more accurate understanding of how satisfied your customers really are.

It opens up a whole new world of possibilities for gathering customer intelligence, by gleaning more insights from a source of data you already have: your everyday customer conversations.

4. Make it accessible

Think about the end users who will most likely be using your self-service options. How technologically savvy are they? What stages of the customer lifecycle will they likely be in?

Your automations' experiences should be designed around those needs.

Make your self-service options easy to find and use, whether that's a chatbot window, an IVR menu, or an AI agent handling a support line. Customers shouldn't need an onboarding tutorial just to figure out how to interact with them.

That also means matching the channel to the audience. A tech-savvy customer might be perfectly comfortable typing a request to an AI agent in a chat window, but someone calling about a time-sensitive issue may still expect to just say what they need out loud and have an AI agent, or a human, understand it. Design for how your customers actually want to communicate, not just the channel that's easiest to build.

5. Nail down the logistics of your ideal self-service automation flow

Once you've found what automated processes you want or reviewed the ones you already have, plan out the logistics of your ideal self-service automation flow.

For example, if a customer calls in about multiple password resets, what does that path look like for them? Is it numerous calls with the help desk and then a transfer to someone on your IT services team?

As you map everything out, try to plan the "happy path" and the "not-so-happy path" for a customer. This way, you'll be able to be proactive and try to anticipate where you can make improvements in your customer experience.

How will your business use self-service automation?

The use cases span the full customer journey, from a quick FAQ lookup to an AI agent handling an entire order change end to end.

What actually determines success isn't how many channels you turn on, it's whether those tools sit on a platform that gives them shared context: the same customer history, the same escalation paths, and the same data your human agents already rely on.

Approached that way, self-service automation isn't just a way to deflect volume. It becomes a way to resolve more requests correctly the first time, and to give your team better visibility into where customers are still getting stuck.