4 Common Use Cases for Machine Learning in Business Text Messaging

Robot standing next to wall of text implying machine learning in business text messaging

Machine learning sounds like a super high-tech term—but if you’ve got a modern business text messaging platform, you might already be using it. 

The idea behind machine learning is actually simple: machine learning programs use large sets of data to detect patterns and make decisions. In business text messaging, machine learning helps teams streamline repetitive tasks, which automates workflows. For most businesses, machine learning is a critical tool that can help teams focus on the most important tasks, like managing large projects and personally connecting with customers. 

Read on to learn about 4 common use cases for machine learning in business text messaging.

Auto-Respond with Tailored Messages

Nearly every professional knows about the power of a good auto-response. These messages, which we usually use with email, let our coworkers know we’re on vacation and tell customers when we’ll be able to answer their queries. Business SMS uses intelligence similar to machine learning (i.e. automations) to take those auto-responses one step further.

With the help of automations, businesses can set up their SMS solution to analyze the timing and content of messages and determine whether to send a templated reply. The system can even send a variety of replies based on keywords. 

For example, if a customer replies after hours with a message containing the keyword “reschedule” or “ingredients” the system can automatically recommend or send a tailored reply. 


Incoming Message: Can you help me reschedule an appointment?

Auto-reply: Hi! You can reschedule your appointment online at http://www.acme.com/appointments. Text STOP to unsubscribe. 


Incoming Message: What ingredients does next week’s lunch delivery have in it? 

Auto-reply: Hello! The ingredients next week include chicken sausage, rice, red beans, and a spicy, creole sauce. Text STOP to unsubscribe. 


This kind of tailored response makes customers feel as though they’re receiving personalized care, even if they haven’t spoken to one of your team members yet. 

Make Suggestions for Responses

As the work day progresses, it takes longer and longer for employees to review incoming messages and decide how to respond. Machine learning helps employees quickly and accurately respond to customers. 

For example, a business text messaging platform that uses machine learning can suggest how an agent should respond to a customer text with a recommended template. The more responses an agent sends to customers, the better the system can predict what the agent should send.

Right off the bat, for example, a machine learning system should be able to detect whether an incoming message is about requesting a meeting. It might identify the words “schedule” or “meet,” then suggest that the agent schedule a meeting.

Automated suggestions help agents make decisions quickly so they can accurately respond to customers faster than ever before. 

Route Messages to Relevant Agents

Shared inboxes are incredibly useful for most business SMS teams, but some teams who receive a mass of texts still need help streamlining message assignment. Machine learning software in business SMS platforms can also help with this. 

As we explored in the auto-reply section above, machine learning can detect certain keywords in a message. Because of this capability, a business SMS platform with machine learning capabilities can identify message intent and choose whether to send an auto reply or route the message to a specific agent. 

For example, the word “return” can be used multiple ways. 


Incoming Message: How do I start a return? 

Machine Learning Decision: Send appropriate auto reply and route to customer service agent.

Incoming Message: When does your team return to the office? 

Machine Learning Decision: Send after-hours reply listing office hours. 


Automated message routing can both reduce the number of messages your agents have to manually reply to and streamline your team’s shared inbox, ensuring that the right agents are focusing on the right incoming queries. 

Flag Inappropriate Messages

While most use cases for machine learning in business text messaging are simple, machine learning can also help teams identify and block inappropriate messages. 

Throughout the business world, customer service departments receive spam and inappropriate communications. These messages clutter up inboxes, reducing agent productivity and slowing the response time for legitimate customers. While in the past, team members would have to manually triage, analyze, tag, and close inappropriate texts, machine learning has eliminated that need. 

Now, business text messaging platforms can be set up with a machine learning training model that identifies keywords and determines whether they are inappropriate according to their context. For example, “cash” may or may not be a word included in an inappropriate message. Machine learning software can automatically archive and tag texts containing inappropriate use of the word “cash,” while allowing appropriate messages with the word “cash” to remain in the queue. Examples of both cases might look like this:


Inappropriate: We’ve made your cash withdrawal. Call us at 555-713-1234 to let us know if you have any questions. 

Appropriate: Does your store accept credit cards or only cash?


A business text messaging platform will flag the inappropriate message and surface a small “Block” link beneath, allowing agents to quickly decide whether to block or approve the sender. Machine learning is far more accurate than the traditional method of monitoring messages for specific keywords, which does not take context into consideration. 

With the spam accurately filtered out from team queues, it’s far easier for teams to get real work done.


Want to learn more about machine learning and business text messaging? Give us a shout.

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