Who Is Calling, and Why? The Untapped Data in Heavy Building Materials – a Ready-Mix Concrete (RMC) Use Case
Tripp Arnold
Heavy building materials (HBM) operations span ready-mix concrete, aggregates, asphalt, and cement. This blog focuses on ready-mix concrete (RMC), where customer calls are especially central to daily dispatch operations and provide a clear example of how AI can turn conversations into operational insights.
Walk into a ready-mix concrete (RMC) dispatch office late in the afternoon and ask a simple question: Who is calling, and why? Tomorrow’s orders are taking shape, the phones are ringing, and dispatchers are working through confirmations. The people in that room know their customers by voice. Ask them for the numbers, though, and the honest answer is a guess because no system in that office has ever counted those calls.
That gap matters more than it might seem. RMC producers take customer calls all season long, and very few analyze them. The phone is the largest untapped data source in RMC, and it is also where the customer relationship actually lives.
AI Has Reached the Industry, but Not the Phone
Artificial intelligence is reshaping RMC. Dispatch software providers are building AI into ordering, telematics, and production. But the customer conversation, the moment a contractor calls and talks to your people, has received far less attention.
Producers do not all want the same thing, and that is fair. Some want extensive automation. Others, often family-owned businesses, want their people to stay on the phone. I understand why: those relationships were built one conversation at a time. Both groups face the same first question. Answer it by guesswork, and every automation decision that follows inherits the guess.
See, Steer, Automate
The sequence I recommend for bringing AI to the dispatch phone has three steps, and the first two build the evidence for the third. Each step consists of use cases: specific tasks the system performs for your team.
- See your calls. Learn who is calling and why, and assess customer and dispatcher sentiment across conversations.
- Steer before you automate. Route each call to a person with the context already attached.
- Automate one use case at a time. Start where the data shows the work is routine, and keep a human in the loop at every point.
See: What Your Calls Already Tell You
Ask a dispatch team who is calling and why, and they will look at the weather, traffic, the dispatch screen, or the demand graph. Those are the right instincts. But the answer is simply not on any of those screens.
Speech analytics provides it. It transcribes every recorded call and voicemail, groups similar conversations into topics, and shows how those topics change over time. A dispatch manager can see how many of yesterday’s calls were about “Where is my truck?” and then drill down from the chart to the actual conversation. Pulling up a transcript of one call when a question or dispute comes up is a use case in itself, and a very practical one.
Ozinga, for example, uses Spitch’s Customer Contact AI Hub to turn thousands of calls and voicemails into insights. Construction Equipment Guide reported that Ozinga uses speech analytics to centralize calls and voicemails and gain visibility into customer sentiment, performance, and opportunities for improvement.
“Spitch Speech Analytics empowered us with key insights, elevating both customer and employee experience.”
See: Two Sides of Every Call
Every call has two sides, the customer’s and your team’s. The tone of the conversation says as much about the business as its topic does. Speech analytics assesses that tone for the call as a whole, and the same reading serves two purposes depending on how you group it:
- Customer sentiment is analyzed by customer or plant, making it an early-warning signal. A run of frustrated calls about late trucks from one plant, or a long-standing contractor whose calls have grown terse over several weeks, becomes visible while there’s still time to respond.
- Dispatcher sentiment is analyzed across the dispatch team and can indicate workload. Anyone who has spent a busy afternoon in a dispatch office knows how it sounds: shorter answers, less patience, and nobody necessarily at fault. When the whole team’s calls turn short at once, that may signal high call volume, not a problem with the people on the phones. Treat it as a signal to send help, not as a score.
Steer: A People-First Starting Point
Not every producer wants an AI agent taking orders, and plenty of good ones never will. Call steering offers a use case for those producers, and a straightforward way to keep a human in the loop. Your people stay on the phone, and the dispatcher has the context before the conversation begins.
The AI Agent greets the caller, provides the recording notice, and asks two short questions: who is calling, and which order or job the call is about. It then routes the call to a dispatcher with that context attached. The dispatcher knows who is on the line, and which order it concerns, so the customer does not have to repeat themselves.
Steering also logs every call it handles, linking the caller to the order. That adds precision to the picture speech analytics already draws: who calls, about which orders, and when. With that picture, the next step becomes a decision rather than a guess.
Automate: One Use Case at a Time
Order confirmations are a good example of the third step. Every afternoon, dispatchers confirm tomorrow’s orders by phone: calling, talking, calling, talking. Producers are asking about an AI agent that can make those calls, allowing customers to confirm, cancel, or ask to speak with a dispatcher. A person remains available at any point.
The same pattern applies to every use case you consider. Automate the tasks, which the data shows are routine, keep a human in the loop, measure the result, and expand only when the evidence supports it.
Key Takeaways
- The phone is the largest untapped data source in RMC, even as much of the industry’s AI attention focuses on dispatch software.
- See first. Speech analytics shows who is calling and why, uses customer sentiment analysis to flag frustrated accounts early, indicates when the whole team is under load through dispatcher sentiment analysis, and keeps a searchable record of every conversation.
- Then steer, then automate one use case at a time. Call steering is a people-first starting point, and order confirmations are one example producers are asking about. Keep human in the loop at every step.
Make the Phone a Source of Insight
Every call holds clues about customer needs, order activity, and how your dispatch team is handling demand. Speech analytics and call steering help RMC producers turn those conversations into useful insight, then decide where automation can help, one use case at a time, with people still in the loop. See what your calls can reveal. Book 15 minutes with our team. Learn more about the Customer Contact AI Hub.
FAQ
The phone. Dispatch software tracks orders, trucks, and production, but the conversations between customers and dispatchers are rarely analyzed, even when they are recorded. Those calls hold the clearest record of what customers need and where service breaks down.
It transcribes calls and voicemails, groups them into topics, and shows how those topics change over time. It also reads the tone of each conversation and keeps a searchable record, so a producer can find the actual call when a question or dispute arises.
It should not, and I would say that to a producer who wants extensive automation as readily as to one who does not. Keep a human in the loop: automate a routine, high-volume use case such as next-day order confirmations, and make sure a customer can always reach a person.
Ozinga, for example, uses Spitch’s Customer Contact AI Hub to turn thousands of calls and voicemails into insight on customer sentiment, performance, and opportunities for improvement.
An AI agent greets the caller, asks who is calling and which order the call concerns, and passes the call to a dispatcher with that context attached. A person still handles every call; the conversation simply starts with the context already in hand.
Customer sentiment, grouped by customer, flags at-risk relationships early. Dispatcher sentiment, grouped across the whole team, shows the pressure on the people answering the phones. When the whole team’s calls turn short on a heavy day, that is a signal to send help, not a score for any individual.
A use case is one specific job the system does for your team. Looking up a transcribed call, call steering, and next-day order confirmations are three examples. Start with the one or two your call data points to, and add more as the results come in.
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