When was the last time you contacted Customer Service? Was it the result of a problem?
Most Call Centers are stuck in a loop. They wait for something to go wrong and then scramble to fix it.
What is the problem with that approach? By the time a customer reaches out, they are already frustrated. And even if our teams handle the issue well, the damage may already be done.
That’s why some Contact Centers are rethinking their approach to customer service. What if we could spot a problem before the customer even knows about it? What if we could offer help before being asked?
This is where predictive analytics come into the picture. The goal? To anticipate customer needs before they happen. That’s one of the ways we can include hospitality in customer support.
But what are predictive analytics? How can they help Call Centers? And what are some good first steps?
What Are Predictive Analytics and Why Do They Matter in Customer Service?
What will the weather be like tomorrow? Will it rain? Will it be sunny? Will it be warm or cold? How do you find out the answer to these questions? Most of us go to the weather forecast. But where did they get the information? Often by looking at patterns. What happened in the past, and how does it relate to what is happening now? That’s predictive analytics.
- How often does a customer contact our support team?
- What issues tend to follow certain purchases?
- When does call or chat volume spike?
- What types of problems are the most common?
Then, just like with the weather report, we can use this knowledge to prepare for the future. If it is going to rain, we bring our umbrella. If we see signs that a customer is having trouble and is likely to cancel their service, we reach out to fix the issue first.
No doubt you can think of many other examples of how such foresight could help us in a Contact Center. And it doesn’t always come down to proactively contacting the customer. Sometimes it just means we adjust staffing at certain times, provide the right training to agents, or improve routing.
We don’t have to guess everything perfectly. Even if we get a few correct it can make a significant difference. Better conversations. Less stress on our teams. More trust from customers.
Examples of Predictive Analytics in Call Centers
Seeing the pattern and acting in advance has value; we know that. But what can we do about it? Here are two practical ways that predictive analytics can be used in a Call Center.
1. Anticipate Customer Needs
This is the real secret. Anticipating what someone needs and rushing to solve it before they ask can make an enormous difference. It makes for a good customer experience. Such a customer is more likely to be loyal and to tell others about a product or service.
What are some examples?
- Consider a broad one, based on trends. Let’s say that customers who place large orders often contact support to ask for order status. Once you identify this pattern, what can you do about it? Send a proactive update to all such customers. Even a short message that says “your order is on track and here is what to expect” can be helpful.
- We can also do this at a customer-specific level. Imagine a customer who keeps contacting support about the same issue. Each time the messages get shorter, showing that this person is increasingly frustrated. A contact center system such as Bright Pattern can alert you to this situation. Then it is your chance to reach out, apologize, fix the issue properly, and show you care.
When we can anticipate needs, it builds trust. Instead of just putting out fires, we are actively working to make things better for our customers. And that’s the kind of support people remember and come back for.
2. Help Agents Do Their Best Work and Avoid Burnout
It’s hard to give great service when you’re stretched too thin. I’ve been there, and maybe you have too.
What is the situation with most Call Center agents? Many are juggling high volumes, higher customer expectations, and the emotional toll of conversations. It’s no wonder that they feel stress and anxiety.
How can predictive analytics help?
- Smarter Scheduling: I’ve seen shifts with agents sitting around waiting for something to happen. Then there are others that are out of control with activity and conversations. If we can predict volumes and spikes, then we can staff at the right levels each time. This means fewer moments of chaos and less downtime during slow periods. Systems like Bright Pattern have tools built in to help with such scheduling.
- Faster Decision Making: If we can predict the best answer to a particular question, and suggest it to agents, it can help make each conversation easier. Tools like Agent Assist in LivePerson’s Conversational Cloud use predictive analytics to do just that. Often agents may receive two or three good suggestions to choose from, instead of always having to come up with an answer on the spot.
- Better Coaching and Training: By analyzing call types, intents, and sentiments, we can find trends. What topics do customers often ask about? What products are causing repeat calls? What do our customers really want when they contact us? And how do they feel during the call? With this knowledge in mind, we can then design training for just these specific issues. And we can coach agents through them.
When our agents feel better prepared and supported, they are less likely to feel stress and pressure. And this will show in their tone, their speed, and how they interact with each other. The result? Happier agents and a better experience for the customer as well.
How to Get Started (Without a Full Overhaul)
When people hear “predictive analytics” they often think of a large-scale transformation. And what comes with that? A prohibitive cost. Months of planning. And a large team.
But it doesn’t have to be like that. Just as I recently wrote about AI, the key is to start with small experiments. Then, adjust if things don’t work. But, get started. The truth is that most Contact Centers already have the data they need to start small and see tangible results.
Practical Examples of Small Projects
- Churn risk alerts: Most Call Centers already track customer engagements. You could start a small analysis project to look for signs of frustration or repeat contact on the same issue. This could trigger an alert to give you time to act before it is too late.
- Smart routing: Chances are this is already built into the software you use. It may just be a matter of turning on some features. Send customers to the agent or AI bot most likely to solve their issue, or even to the same person who helped them before.
- FAQ and Knowledge Suggestions: This only takes a few inputs like product type and previous issues. By analyzing prior questions, you can learn the most likely solutions. You might even send a list of frequently asked questions by email or text, before the person even reaches out to support.
Tip: At CBA, we often help teams find the right size of tool for their needs. We have a wide variety of options to address different use cases. What matters most is that the solution should solve your problems, not address someone else’s ideal use case. Even downloading data into a spreadsheet and then looking for tendencies can prove an effective way to start.
Conclusion
You don’t have to predict the future perfectly to make a difference. Just being one small step ahead can set your Call Center apart.
Start small. Look at where you already have data, patterns, and pain points. Ask: What could we prepare for instead of reacting to?
Remember the goal. It’s not about a fancy new tech tool. Instead, we are looking for ways to help our agents be more confident and prepared and our customers to feel our care.
At CBA, we’ve spent over 18 years helping Contact Centers thrive. It would be our pleasure to assist you in your predictive analytics journey as well. Simply contact us today to explore ways to get started.
