Data-Driven Contact Center Optimization: Using Analytics to Drive Performance

min read
Data-Driven Contact Center Optimization: Using Analytics to Drive Performance

Rapidly advancing technologies, both in the cloud and here on the ground, open the door to new and efficient ways of handling and analyzing large amounts of data. This, in turn, is fueling a new era of business optimization among companies looking to drive better performance. 

Nowhere is this more evident than in contact centers, also known as call centers, where teams handle huge amounts of customer interaction data every day. Data-driven contact center optimization is crucial not only to create more efficient operations but also to drive standout customer and employee experiences. 

In this post, we will look at some of the ways data can be handled in a contact center environment and discuss relevant key performance indicators (KPIs) and best practices. We’ll also examine some of the challenges teams face when attempting call center optimization. 

What Is Data-Driven Contact Center Optimization?

So, what do we mean by contact center optimization that’s driven by data? 

First, contact center optimization is an ongoing strategy — or, a series of strategies — focused on improving the operational efficiency of a call or contact center. These efforts help to improve the overall experience for customers and employees. A good optimization strategy can also result in reduced operational costs.

Leading contact center operations use data and data analytics tools to the fullest. They can:

  • Analyze customer data to understand customer preferences, behaviors and pain points to provide more personalized interactions and anticipate needs.

  • Use predictive analytics solutions to forecast call volumes, peak times, and customer issues.

  • Tap into conversation intelligence to analyze speech and text so they can surface trends, identify keywords, and more in customer interactions, and truly listen to the voice of the customer

These are just some examples of how contact center operators can use data strategically to optimize their operations, elevate team performance, and ensure customers can receive an experience that’s memorable for all the right reasons.

Does my contact center really need data analytics?

Of course! Data is gold that has value in all areas of a business. It is so important to modern marketing, in fact, that data-driven marketing trends are transforming the discipline.  

The average call center, whether it’s focused on sales or customer service, generates a tremendous amount of valuable customer and agent data from thousands of digital and verbal interactions every day. If you aren’t using the intelligence from data to improve call center performance, create efficiency, train agents, solve problems, inform your overall marketing strategy, and smooth the customer journey, you are missing several beats. 

Collecting and Preparing Data

Capturing and then analyzing all the data flowing into the call center and delivering actionable insights from that data can be challenging, especially if you don’t have the right tools. 

Let’s take a closer look at what type of data you might want to collect, and methods you can use to gather data in the call center.

5 Key Types of data to collect

Data from incoming calls and digital interactions, customer data, agent data, and operational data are among the types of data found in a typical contact center. You can use contact center optimization software to track all of this information.

1. Call data

Data collected from phone calls such as incoming call volume, average handle time of calls, hold time, call abandonment rate, and first call resolution, provides crucial insight into how to improve call center operations. 

2. Conversation data

Even in today’s digital world, 61% of consumers still prefer to speak to someone by phone when they contact a business. Phone conversations supply companies with a rich vein of data … if that data can be captured and analyzed.

With artificial intelligence-powered conversation intelligence tools like Invoca, you can access a wealth of insights, at scale, from all the calls that you capture coming into your contact center. You can learn about customers’ interests, needs, and concerns; identify barriers to purchase; detect trends; surface customer service issues; and much more.

3. Customer data

Companies collect massive amounts of information on customers from demographics (age, location, income, marital status, etc.) to customer preferences. Data from customer feedback, including reviews and customer satisfaction scores from surveys, are included in the customer data category. 

Contact centers can also collect data about the customer’s digital journey and use it to optimize their experience. They can gather details about which ad a caller clicked or what webpage they visited prior to reaching out to the contact center. And with the intelligent tools, they can use that information to route calls most effectively or to help agents enter a call prepared to customize the conversation.

4. Agent data

Call centers gather data from agents, too. Metrics like agent performance, attendance, adherence to schedule, and productivity are useful for the purposes of call center optimization.

5. Operational data

Finally, there is data to gather from the contact center operation itself. These metrics include service level, average speed of answer, financials, and occupancy rate (i.e., the number of agents staffing the call center at a given time).

Methods for collecting data

There are several ways to collect data in the contact center, and we already hinted at a few. They include:

  • Call recordings and transcripts: Thanks to cloud-based technologies and AI like Invoca, contact centers can automatically generate recordings and transcripts for every call received in the contact center. In addition, teams can search the call transcripts quickly, and at a scale, for valuable insights. 
  • Conversation Intelligence AI: Using an AI-powered conversational intelligence platform like Invoca, managers can automatically QA 100% of calls to evaluate agent performance. You can also use the AI to identify common trends, customer pain points, agent performance issues, and processes that need improvement.
  • Customer surveys: Contact center agents and customer service professionals conduct surveys through various channels such as email, phone, or web chat to gather additional feedback on the customer experience.
  • Performance metrics: Last but not least, metrics gleaned from actual agent performance can be used to identify top-performing agents and spotlight areas where staff may need additional training or coaching.

4 Steps to Prepare data for analysis

Data takes many forms, and not all data is high quality or ready (or easy) to work with. So, it is necessary to prepare it before attempting to analyze it. There are four basic steps to this process: 

Step #1: Remove duplicate or irrelevant data

Duplicate data can skew analysis and irrelevant data can waste time and resources. Scrubbing data, also known as de-duping, to remove duplicates can also be time-consuming. Luckily, artificial intelligence and machine learning can train algorithms to rapidly and efficiently scour data at scale, removing duplicate and irrelevant data in a fraction of the time it takes manually.

Step #2: Standardize data

Data from different sources can have variations in formatting, abbreviations, and units of measurement. It’s important to standardize data so that it can be analyzed easily in an apples-to-apples comparison.

Step #3: Validate data quality

Again, don’t assume the data you have collected is quality data. It’s essential to validate data quality to ensure accuracy, consistency, and completeness. A quick internet search can connect you with information on tools and processes related to this step.

Step #4: Transform data

Data can be aggregated to different levels of granularity, combined with other data sources, or transformed into different formats before being analyzed. It all depends on your data analytics goals.

Analyzing Data

Once data is cleaned, standardized, validated, and transformed it can finally be analyzed. There are several forms of analysis available when looking to optimize operational performance for your contact center. Some commonly used approaches are described in this section.

Techniques for analyzing contact center data

  • Descriptive analytics, often viewed as the simplest form of data analytics, uses historical and current performance to tell us what has already occurred. This can help managers plan optimization schedules based on validated past results.
  • Predictive analytics uses AI-driven machine learning algorithms to take current and past data and use it to predict future performance so that managers can set optimization goals for the contact center.  
  • Prescriptive analytics is the “best of both worlds,” a hybrid of descriptive and predictive analytics that managers can parse to make data-driven decisions.
  • Speech and text analytics software automatically tracks, transcribes, and analyzes calls and chats. Platforms driven by artificial intelligence, such as Invoca, use machine learning and natural language processing (NLP) to identify trends at scale. Instead of a human listening to hundreds of calls, AI can quickly and seamlessly listen to, and transcribe, thousands of calls delivering robust data insight quickly and efficiently.

4 Important KPIs for analyzing contact center performance

When looking to boost call center performance, you’ll want to start using key performance indicators (KPIs). The four KPIs typically used to track performance in contact and call centers are:

1. Average Handle Time (AHT)

AHT measures the average duration of a customer call, from start to finish, including hold and talk time.

2. First Contact Resolution (FCR)

The percentage of customer inquiries or issues that are resolved during the first contact with a call center agent is the FCR. The aim is to have the FCR be as high as possible.

3. Customer Satisfaction Score (CSAT)

The CSAT is derived from short, post-call customer surveys. It measures the overall satisfaction of customers with their contact center experience.

4. Net Promoter Score (NPS)

This measurement is also derived from a simple post-call 1 to 10 ranking question, typically “How likely would you be to recommend XYZ product or company to a friend or colleague?” Responses are tallied and the resulting ranking indicates the NPS.

There are other relevant KPIs you might want to consider, too, depending on your objectives. Contact quality and after-call tasks are two examples.

Applying Data Analytics for Contact Center Optimization

A key reason to apply data analytics to contact center optimization is the opportunity to drive positive results by using that insight. Those outcomes can include:

Improved Agent Performance

Data analytics helps contact center agents and managers drill down and gain deeper insights into customer behavior and preferences. The more they know about the customer, the better they can serve them. Interactions can be personalized to provide a better all-around customer experience.

Increased Efficiency

Data analytics enables contact center managers to better gauge and predict agent staffing levels, making workforce optimization and management more efficient and cost-effective. Having the right number of agents in place also helps reduce wait times and improves first-contact resolution rates.

Cost Savings

Data analytics can highlight areas in the contact center where cost-saving opportunities can be achieved, such as optimizing technology. The contact center may be using too many technology vendors or may benefit from contact center optimization software or an expanded tech stack. 

Cost savings can also be realized by reducing stress among call center agents, thereby reducing agent churn, creating better workforce optimization, and improving call resolution rates. 

Real-Time Monitoring

Part of the beauty of AI is that it can help users achieve results at scale very quickly. AI-powered data analytics can monitor performance metrics in real time, allowing call center agents to quickly identify issues and take immediate corrective action.

Challenges and Considerations

While there are many benefits from contact center optimization, there are also inherent challenges. Luckily, many of these challenges can be addressed by applying the right technology tools.  

Data Quality

As explained earlier, data quality matters — a lot. If you fail to scrub the data properly, you can skew your data analysis. Missing or incorrect data, duplicate records, and inconsistent formatting can all impact the accuracy and reliability of data analytics. Ultimately, this makes data analysis a fruitless exercise and leads to a waste of time and money.

Data Privacy and Security

There are very strict privacy and security regulations attached to the gathering, storing, and analysis of personal data. Businesses need to document adherence to these data regulations very carefully, which can also be resource-intensive. 

However, since the key goal of any contact center is to improve the customer experience, it’s vital that all data privacy and security regulations are followed to the letter, or customer relationships may sour (and your business could end up in hot water).

Data Integration

Data analytics often involves combining and resolving data from multiple sources and systems. This can be a complex and time-consuming process, which results in increased costs for the call center. So, it’s important to choose tools that work well with your existing technology stack and workflows. (Invoca integrates with many leading solutions, from Google Analytics and Five9 to Optimizely and Slack.)

Data Interpretation

Data analytics generates large amounts of data. This in itself can be overwhelming and difficult to interpret. Automation and contact center optimization software can ease this challenge.

Using Analytics to Drive Performance

Despite these challenges, data analytics is essential for driving call center performance. The positives definitely outweigh the negatives where contact center optimization is concerned. 

Data-driven analytics can help with identifying areas for agent and process improvement, monitoring progress toward defined goals, and making more accurate, data-driven decisions that will set the business on a more solid footing as it looks to grow.

Start Optimizing Your Contact Center with Invoca

Invoca’s conversation intelligence platform is the Swiss Army knife of call center optimization. It’s an analytics tool that provides multiple benefits.

It starts with an automated recording of every call that comes into the contact center. Invoca’s AI-driven platform not only automatically tracks and records every call, it delivers a real-time transcription and analysis of each call. Data analysis at scale is fast and seamless.

Invoca’s conversation intelligence delivers a wealth of data that companies can use to optimize their contact center operations. For example, it tracks agent performance and automatically delivers quality assurance scores after each call. Managers can review the scores and full transcripts of calls to identify coachable moments in each agent’s talk track, then lift data to use directly in training. More effective agents lead to greater customer satisfaction, and a satisfied customer is often a repeat customer. 

In addition to measuring agent performance, Invoca can provide insights from conversations that can help businesses identify trends that could be adversely impacting customer service or sales. For instance, perhaps people are mentioning that a glitch on a webpage prompted them to call because they couldn’t complete a transaction online. Or maybe a new customer service issue has cropped up and no troubleshooting talk track exists to address it, so agents are struggling to respond effectively. 

Invoca can surface these and many other trends, allowing contact center agents to pinpoint problems and address them swiftly. Insights from Invoca can also help businesses discover new opportunities to delight their customers and grow their bottom lines.

Additional Reading

Want to learn more about how Invoca can help you improve contact center performance? Check out these resources:

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