11 Tips For Improving First Contact Resolution With Data


Imagine this scenario: You buy a new printer but can’t connect it with your router. You call the manufacturer, make it through the phone tree, and a representative walks you through the process. The next morning, you try to print something. You get an error message: Disconnected from router. You sigh and call the help desk again as your workday grinds to a halt. If only your issue had been fully resolved the first time you called. If you’re an entrepreneur, CEO, or other business leader, first contact resolution (FCR) should be just as important to your company as it is to you as a consumer.

This metric, as shown in the example above, affects your customer satisfaction rates and thus your bottom line. If you can make sure your customers are getting their questions answered thoroughly as part of one positive brand interaction, you’ll cultivate dedicated brand loyalists

In an era where customer service is increasingly frustrating all around, creating a positive and efficient experience is a smart investment. One of the most surefire ways to improve FCR is by using data to understand where your team can improve. Here are some tips for how to do so.

1. Examine the data you already have.

The first step is to do a self-audit to determine what data you already have. Too often, businesses treat data like a “closet full of clothes but nothing to wear” situation; many businesses have reams of data but aren’t actually using it. Does your business already collect quality assurance (QA) data, creating scorecards for each of your agents? Do you collect average handle time (AHT) numbers and occupancy (the amount of time each agent spends interacting with customers)? Understanding your already-collected data and related benchmarks gives you the lay of the land as you work to improve your first contact resolution. 

2. Consider the tools you may need. 

Using technology such as AI can improve first call resolution (FCR), as well as average handle time (AHT) (statistics which often have an inverted relationship). Assess your need for other tools, too. You might need to invest in specialized training for your agents, a rich knowledge base, an efficient triage system, and maybe some data collection and analysis tools. You may even be able to purchase tools you can use for different departments at your company, saving everyone time and money. 

3. Prioritize the right metrics. 

Once you’re ready to set (or reset) your data priorities, make sure that the metrics you’re using are driving toward your desired end. If your top goal is improving first contact resolution, then collect keywords from multiple contacts and CSAT scores from long vs. short calls. Look at customer behavior on your website before and after they contact your chatbot. Get input from your team on what could directly help them, too. 

4. Use a tool that can collect metrics — and use them in context. 

Collecting data from your team’s interactions can be a headache, but a tool such as MaestroQA can automate QA data collection, saving time for everyone. QA data serves as a check to ensure that your agents are following your FCR best practices. It also is key for surfacing new insights to increase first contact resolution further. Once you have your QA data, use it and other metrics, to get the full story behind agent performance.

5. Personalize your service to your customers. 

Not all the data you use needs to originate from your service center. Consider your customer demographics. Borrow insights from your marketing team. Who are you selling to? What pain points is your company solving? How does your product or service address your customer’s needs? Once you put yourself in your customers’ shoes, you’ll better understand what they want from their service interactions. You’ll also be able to better personalize each interaction. 

6. Listen to what your customers tell you. 

If you want to know what your customers think about their experiences with customer service, ask them. You can include short surveys at the end of chat interactions, such as email follow-up, phone or text follow-up, and more. But simply asking isn’t enough. You have to ask the right questions depending on your goals. Is a one-question CSAT-style survey what you need? Or do you want to know about a specific aspect of their contact? Then, once you have some data, consider what changes you can make based on the pain points and frustrations (or wins and joys) that your customers share. 

7. Pay special attention to multiple-contact data. 

When customers contact your team multiple times to solve the same or similar issues, ask: Why? Look at the QA scorecards from those contacts; listen to or read transcripts and chats; talk with agents who dealt with particularly sticky situations. Do agents need quicker access to answers in a knowledge base? Do customers need follow-up documentation? Learn from what went wrong so you can implement smart changes for the future. 

8. Use QA scorecards to assess agent understanding. 

A review of your QA data using a platform such as MaestroQA can expose cracks in your training and best practices and give you a starting point for coaching. Perhaps your agents don’t understand a particular SOP. Maybe your knowledge base has a slow load time, leading to agents guessing at best answers. A deep dive into your QA scorecards may uncover some operational quirks, leading to solutions that will improve your first contact resolution.

9. Introduce coaching for your agents. 

If you don’t already have a coaching program in place, consider implementing one. Coaching allows individual agents to improve based on their own metrics and the overall company metrics. A well-run coaching program allows agents to have ownership of their roles and feel equipped to grow their careers at your company. It allows data dashboards to be used on a very practical, on-the-ground level. It also helps your team understand why you’re asking them to prioritize certain aspects of customer service, inspiring greater buy-in. 

10. Use website data to help solve repetitive issues.

Get ahold of your web team, and ask them about bounce rates, conversion rates, and frequently visited pages. If people are spending six minutes with a specific product’s user manual online, make sure your team is well-trained on that particular product. (Maybe feed that information back to research and development, too!) If you have website visitors who search for a certain topic after using your chatbot, provide information about that topic as part of your general follow-up plan. Your customers don’t live in silos, so neither should your data. 

11. Use data to guide better question-asking. 

As you look through your data, you may find themes regarding why people make consecutive contacts. Use this information to help your agents (or your AI) know when to ask follow-up questions. A simple, “Can I help you with anything else?” at the end of every contact is a good start. But you might also notice that there’s a root cause associated with multiple contacts. If you can guide your agents to dig for that root cause, they’ll be able to help more customers understand what they need. Then, they can get their root issue solved on the first contact.

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