How to Use & Implement Predictive Analytics in Your Contact Center: Transforming Support Performance

Operating a contact center has many challenges. Long wait times, low First Call Resolution (FCR) rates, and high agent turnover can all negatively impact your contact center's performance. With all that stacked against you, it can be hard to see a path forward. But what if we told you that you can use the data from the challenges you are experiencing now to predict how to avoid them in the future?

Well, with predictive analytics, you can. Predictive analytics is the process of harnessing data to forecast future trends or events. The end goal is to make data-driven decisions that enhance overall performance.

In this article, we'll briefly review predictive analytics, what it is and where you can see it being used. Then, we'll look at contact center predictive analytics specifically and highlight how call center data can be utilized to avoid future challenges, boosting performance.

Contact Center Analysts Monitoring Predictive Analytics

What Is Predictive Analytics?

Predictive analytics is a type of data analytics that enables you to make predictions using historical data along with AI and statistics processing. The predictions can be about the near future or the distant future

Predictive analytics differs from other types of analytics because it uses data to answer questions about the future, whereas other forms of data analytics are concerned about why or what happened in the past. It's one of many different types of data analytics.

Examples of datasets that predictive analytics software utilizes to answer that question:

• CRM systems
• Browsers
• Sales
• Financial
• Other Key Performance Indicators, like peak call times and First Call Resolution rate

How Can Predictive Analytics Be Used in Contact Centers?

The customer care industry can also benefit from modern contact center analytics. For example, you can predict customer behaviors, automate follow-ups, and optimize employee staffing for peak times.

Let's take a closer look at the various ways contact center data can be leveraged for predictive analytics.

1. Boosting Your Contact Center Self-Service Options 

   Based on customer behaviors, predictive analytics can help deliver the most effective assets and information to customers as they navigate the contact center's self-service options.

   How To

   For example, engagement data, such as a customer's clicks or search history on your website, can be used to direct them to the correct page.

   Similarly, data from the tendencies of contact center personnel themselves can be used to trigger predictive analytics software to deliver assets that help them perform better. 

   Benefits

   • More customer satisfaction 
   • Fewer customers are waiting to speak with personnel

2. Mitigating Contact Center Employee Turnover

   Data from contact center employee burnout and turnover can help build predictive models for the future.

   How To

   For example, predictive analytics can help contact center managers intervene earlier with employees who are unhappy, thereby improving retention.

   On the other hand, predictive analytics can help you make decisions about hiring new employees in the future.

   Benefits

   • Decreased employee turnover
   • More employee satisfaction 
   • Boosted employee efficiency 

3. Predicting Customer Satisfaction Over Time

   Contact centers collect data points, such as customer satisfaction surveys and Net Promoter Scores (NPS).

   How To

   You can use that data to make predictions about your customers' overall satisfaction with your customer service.

   And, you can go one step further to predict which channels of your contact center will be most effective to invest your resources strategically.

   Benefits

   • Increased customer loyalty
   • Higher Net Promoter Scores
   • Less customer churn

4. Streamlining Interactions Between Customers and Contact Center Agents

   Contact center predictive analytics can help you connect customers with the agents who are the best match for their needs.

   How To

   For example, if in the self-service chatroom with an AI chatbot, a customer informs the system they want to make account changes, they can be connected with an agent who can facilitate those changes the first time.

   This can help you increase First Call Resolution rates and lower your contact center's hold time. Over time, customer satisfaction will also increase as customers have their needs met faster.

   Benefits

   • Decreased average wait times
   • Better First Call Resolution rates
   • Shorter Average Handle Time

5. Escalating a Customer from a Chatbot to a Human Agent

   Intelligent Virtual Agents (IVAs), also known as AI chatbots, are becoming increasingly important tools for modern contact centers. IVAs can be super effective at satisfying customers' needs. However, sometimes, a customer interaction needs to be elevated to a human agent.

   How To

   Predictive analytics can use text analytics from the chatroom to predict when that needs to happen. 

   Benefits

   • More customer satisfaction
   • Higher First Call Resolution rates

6. Providing Proactive Assistance 

   Predictive analytics can be a powerful tool for understanding a customer's intent. Knowing their intent, you can provide proactive assistance.

   How To

   For example, speech data from the IVR system can direct customers to the correct personnel for further assistance.

   Similarly, your contact center can utilize predictive analytics to provide agents with real-time guidance in a proactive manner by analyzing communication logs to identify gaps in their knowledge.

   Benefits

   • Increased First Call Resolution rate
   • Shorter Average Handle Time
   • More customer satisfaction 

7. Building Customized Predictive Models to Anticipate Customer Behaviors and Drive Sales

   Historical data points like customer loyalty and turnover can help you predict customer behavior and recognize preference patterns.

   How To

   Qualitative and quantitative survey response data from the CRM system can help you identify relevant pain points your business needs to improve. Predicting customers' behavior is also helpful for automating follow-ups.

   For example, if your contact center data indicates that customers are experiencing difficulties with a specific component of your product or service, you can follow up and check in.

   Benefits

   • Increased customer loyalty
   • Decreased customer churn
   • Higher Net Promoter Scores

How Can Predictive Analytics Improve Contact Center KPIs?

We alluded to the relationship between predictive analytics and contact center metrics and KPIs in the previous section. Now it's time to make it more straightforward: contact centers can utilize predictive analytics software to track the ongoing progress toward achieving specific KPIs with precision.

Now, let's look at three specific scenarios.

• Scenario #1: Forecasting Agent Load

  Forecasting software can predict the times when a contact center is expected to be busiest. Knowing when your contact center is busiest enables you to optimize staffing by ensuring all personnel are available.

  KPIs you can improve:

  • Customer wait times
  • First Call Resolution
  • Customer satisfaction

• Scenario #2: Predicting Customer Churn

  Predictive analytics can identify when customers are at risk of leaving your product or service for a competitor. By analyzing customer report data and identifying when customers are on the verge of leaving, you can proactively initiate efforts to retain them. Retention strategies include personalized interactions and tailored solutions.

  KPIs you can improve:

  • Customer churn
  • Net Promoter Score
  • Customer satisfaction

• Scenario #3: Helping Agents in Problem Solving

  Predictive software can identify when your contact center personnel need assistance. This is possible by analyzing interaction data such as tool usage and call patterns. Understanding when someone needs help allows you to provide contact center training opportunities proactively.

  KPIs you can improve:

  • Average Handle Time
  • Employee churn
  • First Call Resolution

Challenges and Risks of Predictive Analytics in Contact Centers

Predictive analytics can be a highly effective tool for contact centers. However, there are some associated risks and challenges to overcome for it to be the most effective. 

• Data Quality and Bias: Predictive analytics data can be incomplete, inaccurate, or biased, leading to unfair outcomes or poor predictions
• Data Privacy and Security: Contact center predictive analytics uses sensitive data (e.g., customer personal information) that could be at risk of misuse or security breach
• Model Maintenance: Predictive models require ongoing updates to remain relevant
• High Costs: Predictive analytics software can be costly upfront
• Skills Gaps: Well-trained personnel (data scientists or analysts) are required to develop, maintain, and analyze the data

4 Tips for Incorporating Predictive Analytics Into Your Call Center

1. Define S.M.A.R.T. Goals

   Defining S.M.A.R.T. goals is the first step for establishing a baseline of usable data. The flip side of this coin is highlighting your contact center's pain points that you want to improve.  For example, reducing wait times or improving first-call resolution rates.

2. Gather and Clean High-Quality, Integrated Data

   Regardless of the goals you focus on or pain points you want to improve, you have to collect loads of data. And don't just focus on datasets from one or two segments of your contact center. Instead, collect data from all customer touchpoints. Your predictive analytics can be more powerful if you build it upon a rich foundation of historical data.

3. Train Your Staff to Use the Tools and Act on Insights to Make Decisions

   Collecting data to use for predictive analytics is only half the battle. Harnessing the data to make data-driven decisions is the other half. To maximize the value of the data you are collecting, it's vital to train contact center personnel effectively. Proper training empowers them to create insights and make informed decisions.

4. Choose the Right Software with Suitable AI-Powered Capabilities

   Humans have yet to develop a reliable method for predicting the future. Fortunately, certain AI-powered software can do that for us. To reap the benefits of predictive analytics, having the correct software is a necessity.

Use Predictive Analytics to Strategize For The Future

Predictive analytics enables contact centers to move beyond reactive decision-making. Instead, businesses can become predictive in how they operate their contact center. This can help them anticipate customer behavior, optimize marketing strategies, achieve customer service goals, and improve overall operational efficiency.

Giva Can Help Streamline Your Contact Center Processes

Giva has developed AI-powered, HIPAA-compliant customer service software.

The AI Copilots speed ticket interactions and retrieving knowledge base answers.

Further, you can harness the software to create visual dashboards, reports, charts and graphs, which can help you make data-informed decisions.

Want to learn more? Get a demo to see Giva's solutions in action, or start your own free, 30-day trial today.