As technology has developed around the world, it has given rise to a wide range of solutions and tools that can be used in many different fields, workplaces, and projects. Businesses that make numerous calls in their day-to-day activities to clients, leads, etc. have adopted the use of a technological invention named autodialers.
Automatic dialers automate the processes involved in call centers or customer service departments. The use of automatic dialers has significantly increased, causing a spike in the value of the autodialer industry. In the year 2022, the autodialer software market across the globe was valued at $417 million.
Autodialer software offers numerous benefits to organizations that implement it. The benefits include an increase in efficiency at work, reduced idle time for call center agents, offering personalized calls to clients, etc.
While it is common to hear people talk about the benefits of autodialers, it is important to keep in mind that there are different kinds of automated dialers. Each type of autodialer has its specific use cases.
Types of Autodialers
The types of autodialers are:
1. Progressive Autodialers
Progressive autodialers are used in areas that have a high call volume. They are popularly used when companies and brands are running sales and marketing campaigns. The progressive auto-dialing software automatically phones the next person once an agent is done with a call. The progressive dialers take the stipulated list of numbers and all automatically dial the numbers progressively.
This type of autodialer software is best suited for call centers or customer service centers that are largely focused on making high-quality lead calls.
2.Predictive Autodialers
Predictive auto-dialing systems help ensure that call center agents, customer service agents, etc., work efficiently. The predictive phone dialer software has algorithms that help them analyze call times and ensure that once an agent gets off a call, they are linked to another immediately.
A predictive autodialer’s only job is to connect agents to the next number in the call queue. Predictive autodialers greatly improve work efficiency, and they increase the number of leads a business can connect with in a sales campaign.
3. Preview Autodialers
Preview autodialers are used when call center agents require information about the client before making the call. Preview automatic dialing systems are often linked with CRM systems to offer more information about the lead that will help to convert them easily.
When using preview autodialers, the call agents have the option to get directly to the call or take time to go through the client’s information. This type of autodialer is mostly used in situations where getting to know the lead or client personally is important.
Autodialers have a huge reliance on machine learning technologies for many use cases. One of the use cases where autodialers use machine learning is to optimize sales campaigns. This article goes through some of the ways automatic dialing software uses machine learning to optimize sales campaigns.
1. Voicemail Delivery
During sales campaigns, it is quite common for sales agents to call clients and get their voicemails. In such situations, the call agents can opt to leave a voicemail message or hang up and then call the lead or client at a later time. When call agents spend time making numerous calls and find the person is not available, a lot of time is wasted that could have been spent making lead conversions.
When autodialers call leads in a sales campaign, they use machine learning technology to identify the sounds being played on the call. With this, the automatic dialer can know that the call has gone to voicemail, and it will come up with an appropriate message and leave it as a voicemail. This use of sound recognition to send automated voicemail messages is important, as it helps ensure no calls are made without any messages being delivered to the potential leads.
2. Predictive Dialing
Autodialers are popular for their role in increasing the efficiency of call agents. Automated dialing software increases efficiency through predictive dialing. Call agents are only linked to calls that are picked up when using predictive dialing. However, there is more to the functioning of predictive dialing than meets the eye.
Predictive dialing uses machine learning algorithms to analyze various caller and client data, such as the call abandonment rate. Such data is analyzed by the phone dialing system so it can figure out when an agent on call will be free once the call ends. By figuring this out, the automatic dialer phones a new number, which the recipient will pick up by the time the call center agent is done with the previous call, thus giving them time to tend to the new call.
3. Natural Language Processing
Autodialers interact with humans a lot. Due to this, the developers had to use machine learning technology to integrate natural language processing in the autodialers. Natural language processing capabilities come in handy when understanding and responding to clients on the phone.
With natural language processing capabilities, autodialers can understand what leads say. When the autodialers understand client responses, they can respond accordingly and offer the needed help or services. Natural language processing offers the autodialer the capability to engage in conversations with the client naturally and engagingly.
Machine learning algorithms are useful for training the dialer to recognize all of the accents and nuances that will be encountered during use.
4. Sentiment Analysis
When making phone calls as part of a sales campaign, it’s important to understand and take into account how clients feel. This is where the sentiment analysis feature of automatic dialing systems comes in. Autodialers use machine learning technologies to analyze the tone of a conversation, particularly the emotional tone.
Before connecting a lead to a call agent, the autodialer looks at how the conversation makes the person feel. Once it analyzes the emotional tone, it links the lead with an appropriate call center agent who is deemed fit to handle such clients.
Sending clients to different call center agents based on how they feel helps make sure all clients are happy. This increases the chances of the lead becoming a client.
The use of sentiment analysis in autodialers is particularly vital when leads or clients phone in with complaints. This is because certain call center agents are more proficient at handling customer complaints than sales campaigns. This increases customer satisfaction rates.
Conclusion
Autodialers offer numerous benefits to brands running sales campaigns. As you can see, machine learning is the main driving force behind autodialers’ advantages. This, therefore, means that as machine learning technology advances, the benefits offered by autodialers using machine learning will increase.
