The top 5 benefits of using call center predictive analytics

by Andy Nguyen
Call center predictive analysis

Today, most call centers use data analysis tools to get an overview of their business processes and improve decision-making. 

But advanced technologies like call center predictive analytics go beyond that and can help you predict future events. These predictive insights will help you improve service quality, agent productivity, and operational efficiency. 

Imagine the possibilities!

Call centers realize the vast potential of this technology and are using it to transform how they engage with their customers.

In this article, we’ll share five top use cases of call center predictive analytics and five actionable tips to get the most out of it.

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What is call center predictive analytics?

Call center predictive analytics is a type of advanced analytics that uses current and historical data to give insights on what may happen in the future. 

Predictive analytics tools can help forecast future trends by using cutting-edge technologies like:

  • Data mining and data science.
  • Artificial intelligence.
  • Business intelligence and analytics.
  • Machine learning.
  • Statistical and predictive modeling.
  • Sentiment analysis.
  • Speech analytics.
  • Text analytics (for customer interaction through chat, email, or chatbots).
  • Big data analytics, etc.

Several external factors govern call center operations, like customer behavior and market changes. But predictive analytics can help bring a sense of stability and control in day-to-day operations. 

It empowers you with the ability to anticipate future outcomes and take preemptive measures. 

How does it work?

The process starts by collating huge volumes of call center data from various channels and sources like call recordings, KPI (key performance indicators) values, call center software, etc. 

The analytics software runs predictive algorithms over this data to identify trends and patterns that are likely to repeat themselves in the future. These insights can help you arrive at estimates and predictions for different elements of call center operations, such as customer engagement, workforce requirements, sales, etc.

Let’s check out five top ways in which predictive analytics can propel your call center’s performance.

5 ways call center predictive analysis can boost your business 

Here’s how using predictive analytics can help improve your call center’s performance:

1. Better customer experience

Advanced analytics tools can make the customer journey more convenient and fruitful when they reach out to your business. 

For example, consider a scenario where a call center is getting excessive repeat calls, causing an increase in the wait time. 

With the help of predictive analytics and machine learning, the company can identify specific behavioral trends, such as customers making repeat calls for minor issues like checking status updates.

After a feasibility study, the company can roll out a customer self-service solution that helps them get status updates as messages on their phones. This would not only save customers’ time but also free up the company’s telephone lines. 

Similarly, advanced analytics tools constantly record and learn about the customers’ preferences through their conversations with agents, chatbots, and feedback on social media. 

Agents can use this information to deliver a more personalized and immersive service experience to the customers.

Predictive analytics can even help you forecast specific customer needs even before they reach out. You can give them a heads up and take proactive steps to address those issues quickly. 

Learn more ways to improve customer experience with this detailed list.

2. Improved customer retention rates

Acquiring new customers costs more than retaining existing ones. Predictive analytics can undoubtedly help you improve customer retention rates.

For instance, call center analytics software can process speech, text, and other data sets to identify customers more likely to discontinue their relationship with the company. Some common trigger signs that it looks for include tonal dissatisfaction, call escalations, low agent scores, etc. 

The call center can create a targeted retention plan to engage these customers and repair the relationship proactively. 

Analytics-driven clues can also alert a company when a specific action increases the risk of customer churn. A customer denied a loan is one such example of this scenario. In this case, a bank could preemptively offer some other incentives to retain the customer.

3. Increased agent performance

Besides improving customer experience, call center analytics can help boost the performance of call center agents.

Predictive and speech analytics tools can monitor each call to pick up potential root causes behind poor customer engagement, such as:

  • Agents’ resolution time.
  • Inappropriate tone.
  • A fast or slow pace of talking.
  • Inability to engage with the customer.
  • Agent talking over the customer.
  • Pronunciation mistakes.
  • Poor accent, etc.

With older assessment systems, agent evaluation was a time-consuming affair. 

It would take months even to identify issues with the agent’s technique, let alone correct them. A manager or supervisor would need to manually listen to randomly selected customer calls or perform a spot-check during live calls. 

But modern analytics tools can passively record calls and analyze them to reveal even the most minute performance issues. 

A supervisor can then take the necessary steps to resolve these issues, shortening the overall evaluation-feedback cycle. 

Moreover, predictive intelligence can empower agents by training them with real-world scenarios on mock calls and providing guided workflows.

All this would have an immediate and positive impact on customer satisfaction and help improve customer loyalty in the long run. You can also use these performance insights to improve the call center training program to resolve such issues early on.

For more tips, check out this detailed guide on measuring employee productivity in a call center.

4. Efficient operations

A call center should anticipate the call volumes for the next hour, day and week, and assign enough agents to handle the workload for smooth functioning.

Call center analytics do a great job of predicting the number of calls the center may receive based on past trends and the potential impact of disruptive events like a power outage.

It can then run simulations to predict the probability of meeting the SLAs (service level agreement) at any given time. The software maps the expected call volumes with KPIs like agent count, ASA (average speed of answer), AHT (average handle time), etc. 

Analytics software using a predictive model can even go a step further to suggest the number of agents needed at any given time to meet the service level goals.

With these real-time insights, call centers can manage their workforce and resources to meet the demand efficiently. 

For example, suppose the analytics show a dip in the volume of inbound calls for the next day. In that case, managers could cancel overtime requests and reschedule shifts to avoid having excess agents and resources. 

Intelligent predictive technologies can also help leaders perform what-if analysis to gauge how changes in cost, staff, revenue, etc., affect the call center’s overall performance. They can use the results to make high-level policy and operational changes.

Discover ten best practices for efficient call center operations in this guide.

5. Higher sales figures

For outbound call centers focusing on sales and collections, the success rate on the first call is usually low. Agents often need to make 2-3 follow-up calls to convert a sale. 

But this also means making an excessive number of follow-up calls without any reliable means of predicting the outcome. The decision on whether to call back a customer is mostly based on the agents’ judgment. 

This process isn’t efficient and could drain the company’s resources in excess. 

Predictive analytics provide a more objective and reliable way to go about making follow-up outbound calls. 

The software can dig the data sets to zero in on prospective customers who are more likely to make a purchase decision and warrant follow-up calls. Agents can prioritize callbacks using the insights.

These insights are generated using predictive, text, and speech analytics that mine customer data for:

  • Words and phrases that show buying intent.
  • Customers’ purchase history.
  • Calls that go on for longer than the average duration (a sign that the customer is showing interest).

Additionally, analytics can also look at past sales records to predict which months are more likely to achieve peak sales. It can even help you chalk out an optimal allocation of agents and resources to maximize the sales potential during these time frames.

Another compelling use case of predictive analytics is foreseeing campaign performance. 

The success of a sales and marketing campaign depends on multiple factors like:

  • Consumer intent
  • Market competition. 
  • Brand perception. 
  • Time of launch.
  • Past performances of similar campaigns, etc.

Sometimes companies invest a lot of time and resources on a campaign only to shelve it later, or worse, to see it fail after launch.

Predictive analytics could be a gamechanger in this scenario. It uses intelligent algorithms to analyze your campaign based on the above parameters and predict the chances of its success.

Call center leaders can decide what campaigns they should undertake and utilize company resources better. 

But despite such lucrative benefits, a call center may fail to use predictive analytics to its full potential and get the value they expect.

Let’s look at ways you can maximize the benefits of predictive analytics.

5 actionable tips to maximize the benefits of analytics

Here are five practical tips on laying a solid foundation to make your analytics program more effective:

1. Develop a strategy

One of the biggest mistakes companies make is spending excessive amounts on analytics systems without a proper strategy for using them. 

For example, companies starting with analytics often go overboard and get an advanced solution when they need a basic data analytics tool to clean and organize their data.

Before investing in a predictive analytics solution, you should first know why and how you will use it. 

You should analyze the use cases and identify the ones most relevant to your business. Next, you should prioritize these use cases based on the payoffs relative to the cost and efforts required.

For example, you can focus on using the technology to achieve a goal like lowering the Average Handle Time (AHT) at your center by 20% or improving First Call Resolution (FCR) by 10%.

2. Integrate data across channels

Predictive analytics, or any other branch of analytics for that matter, is a data-hungry technology. The quantity and quality of the data you feed will determine how accurate and reliable results it’ll generate. 

Fortunately, call centers generate a ton of data, such as:

  • Customer data from CRM (Customer relationship management) tools.
  • Operational data from ERP (Enterprise resource planning) software. 
  • Call data from QMS (Quality management system) tools.
  • Staffing data from workforce optimization tools, etc.

But if a call center or contact center operates with information silos, they’ll fail to leverage predictive analytics to its fullest potential. 

For example, the quality assurance and workforce management teams generate valuable data, which they can exchange to optimize their respective operations further.

But that will require a solid system in place for effective exchange of data between these departments. 

So what’s the solution?

Call centers should implement data systems that can aggregate company-wide data into one unified repository. This single data source will set the groundwork for the analytics solution to do its magic.

3. Convert insights into actions

Gathering actionable insights from raw data marks the first step towards running a more efficient call center.

However, you need to back these insights with concrete actions to benefit from them. 

For example, running predictive analytics on customer data can highlight poor customer experience due to long wait or hold times. 

But if you fail to take the corrective steps, like using intelligent routing systems, deploying IVR (Interactive voice response), or assigning adequate agents, there will be no visible improvement.

You can trace this lack of action to the operations team not knowing what to do with these insights. So you must support your analytics initiatives with an action plan that includes:

  • Setting up the right in-house talent to make sense of the analytics-driven clues and drive changes based on your business goals.
  • Using technologies like prescriptive analytics – that goes beyond forecasting and recommends actions based on the insights by predictive analytics. 
  • Agile-based mechanisms to make incremental improvements.
  • Building the required infrastructure to implement changes suggested by the analytics team.

4. Promote a data-driven culture

The long-term success of a call center or contact center analytics program depends on whether the company is aligned with a data-driven approach. 

A data-driven culture is when an organization makes strategic decisions by analyzing and interpreting the data — not through guesswork and instincts. 

For a call center to be data-driven, the entire team – from the CEO to a frontline agent – should be on the same page regarding how they make decisions.

Hera a few tips that will help you build a data-driven culture in the company:

  • Establish strong communication between your analytics team and the rest of the organization.
  • Set up an effective reporting system to comprehend what the data is pointing at.
  • Integrate company-wide data into a central repository with shared access.
  • Provide specialized training to your employees on better data skills.
  • Track the right set of call center metrics and KPI.

Discover the best call center metrics to track for success.

5. Outsource your analytics needs

It’s difficult for call center companies to meet all their data and analytics needs in-house due to the huge costs involved, especially if you’re a small firm. 

They can instead delegate their analytics functions to third-party experts. These external services have the talent and technology to provide top-notch analytics at affordable rates.

For more information, check out this comprehensive guide on call center outsourcing.

Final thoughts

Predictive analytics, combined with other intelligent technologies, is useful for call centers. 

They have the potential to give you an edge over your competitors by boosting all-around call center performance — better customer support, higher sales, productive agents, and profitable operations.

However, every call center operates differently and has different needs.

Use the information shared in this article to find how predictive analytics can fit your needs.

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