High-quality customer care is a core tenet of your contact center’s success. In fact, customer engagement focused on emotional, complex, and loyalty-driven interactions is the future of the customer experience, according to a new study by McKinsey. But the question is, what does that look like in action? For that, you need in-depth and accurate data.
Data is how you determine if you’re meeting goals and providing exceptional customer experiences call after call and day after day. With data, you can determine how to meet needs successfully and make improvements to your processes when and where you need them. There’s only one problem: many contact centers—particularly in the eCommerce space—struggle with bad data.
How is Bad Data Defined in the Contact Center and Why Does it Matter?
Bad data in the contact center is defined as any data that doesn’t tell you the whole story or allow for simple and accurate analysis. It costs companies an average of $9.7 million per year due to poor decision-making, business inefficiencies, mistrust, missed opportunities, and lost revenue.
Examples of bad data in the contact center include:
- Too Much Data: Data overload is a problem for many contact centers. Being paralyzed by too much information without understanding the information means you can’t leverage the data to improve your performance.
- Too Little Data: At the same time, there are call centers that gather too little data. You need tools such as text analytics or QA scorecards in place to gather, analyze, and interpret data daily.
- Inaccurate Data: If you rely solely on your agents to tag customer issues based on their opinion, there’s a high likelihood that issues will be tagged incorrectly or missed entirely. Data can also be inaccurate if not enough of it is collected to paint the entire picture.
- Poor Data Quality: Lastly, only 16% of companies characterize their data as “very good.” This poor-quality data can be due to a lack of tools for monitoring and analysis, not measuring the correct KPIs, or misinterpreting what information means.
Whatever the case, all of this bad data results in customers feeling unsatisfied and money being left on the table. After all, 58% of American consumers will switch companies because of poor customer service.
So, how do you overcome the problem of bad data to make better decisions? Recognize where bad data comes from in the contact center and learn to focus on good data instead.
5 KPIs that Avoid Bad Data in Your eCommerce Call Center
While metrics on account information, order quantities, shipping addresses, payments, and marketing performance are essential, they don’t give you the full picture. There is a mountain of hidden data in your contact center when it comes to providing high-quality customer service. Understanding this data, where you can find it, and what it means will help you recognize performance issues immediately and make changes for the better.
The following five KPIs should be monitored and analyzed consistently and constantly, so you can start making the right decisions and avoid bad data.
Customer Call Volume
Customer call volume might sound like a simple metric for your contact center, but it is crucial. Increases and decreases in call volume can indicate many different issues. And understanding the reason behind the change is essential for your performance. It will help you make both short- and long-term adjustments to your day-to-day procedures, training, and/or workflow to decrease the reasons why your customers call you.
For example, for an eCommerce business, an increase in customer service call volume could stem from product issues, website payment gateways, or account issues—the list is endless. To find out what’s driving your call volume, a solution like text analytics can uncover the underlying issue through keyword analysis and topic trends. You can then track your call volume against specific issues to find out exactly what issues are costing you and how to fix them.
Issues with products (or services) have a very clear and direct impact on your entire company. Not only can these issues negatively affect your brand, but ultimately impact revenue. After all, every product that’s returned as defective or unsatisfactory costs your company in fulfillment and negative reviews.
That’s why it’s essential to track product defects as a key data point. You should cross-reference the number of defects reported with the volume of your product/item calls to make more informed business decisions regarding customer satisfaction with a particular product. From there, your contact center can disseminate this information to your product team or adjust agent training to tackle the problem head-on during every call.
Customers dislike having to wait for their product to arrive, and undelivered shipments are a significant negative mark against your brand. But shipping issues can have many different causes, from mistyped addresses to changes in account details or issues with a shipping partner. Knowing where the problem stems from can help you better handle shipping complaints when they come up.
In a global marketplace, it’s essential to track customer shipping complaints by a specific carrier/shipping partner. Even if this is a short list, it will give you a better idea of where the shipping issue stems from and whether changes need to be made to your processes or procedures to fix the problem.
Average Call Length
Similar to call volume, average call length is another metric in the contact center that helps you uncover complex issues. By tracking this KPI, you can keep an eye on issues as they grow more complicated. This understanding will help you train your agents to more quickly handle these longer support calls. It will also alert you to larger business issues that may need fixing to avoid these complex issues in the first place.
Issues with Your Website
While websites are extremely helpful for communicating product information, they can also cause a lot of problems. For example, if you get a lot of calls or tickets about item pricing, color availability, or payment gateways, your website could be the source of these problems. By tracking issues with your website as a data point and looking at trends, you can determine where website improvements are needed—including your customer knowledge base page—and make adjustments in order to cut back on these types of calls.
How to Use AI Text Analytics to Gather Good Data and Improve the Customer Experience
Developing a comprehensive view of the issues in your contact center is essential to success. The last thing you want is to miss issues floating just below the surface. That’s why, to be increasingly competitive in such a difficult market, you have to get a handle on your contact center data.
Text analytics can help you analyze 100% of your text interactions automatically. In this way, you can gain the necessary insight into every customer query and conversation for better and faster decisions. It will help you uncover agent knowledge gaps, identify learning needs, improve agent training, and enhance the customer experience.
After all, the true value of customer support comes when all of your data works together to give you a complete view of your customers’ wants and needs.