Imagine the following scenario: A new printer is purchased, but it won't connect to your router. Call the manufacturer and ask for assistance. You attempt to print something the next day. An error message appears: Disconnected to the router. Your workday is halted as you sigh and call again the help desk. It would have been so much easier if your problem had been resolved immediately after you called. First contact resolution (FCR), whether you are an entrepreneur, CEO, or other business leaders, should be as important for your company as it would be for you as a customer.
As you can see, this metric affects customer satisfaction and your bottom line. You'll build loyal brand advocates if you can ensure that your customers get their questions answered in a single positive brand interaction.
It is smart to create a positive customer experience in an age where customer service is becoming increasingly frustrating all the time. Data analysis is a great way to improve FCR. Here are some suggestions.
1. Take a look at the data that you have.
To determine the data that you have, do a self-audit. Businesses often treat data like a closet full of clothes, but no clothing. Many businesses have tons of data but don't use it. Do you have quality assurance (QA), scorecards or other data that your business collects? Are you able to collect data on average handle time (AHT), and occupancy (the time each agent spends with customers)? As you try to improve your first contact resolution, it helps to understand your data and the benchmarks that are associated with it.
2. Take into account the tools that you might need.
AI technology can help improve first call resolution (FCR) and average handle time (AHT), which are often inverted relationships. You might also need to assess your needs for additional tools. It might be necessary to invest in training and knowledge for your agents. You might be able to purchase tools that you can use in different departments within your company. This could save everyone time and money.
3. Prioritize the most important metrics.
When you are ready to reset your data priorities, ensure that your metrics are driving towards your desired end. If your goal is to improve first contact resolution, collect keywords from multiple contacts. Also, consider CSAT scores for long and short calls. Take a look at the customer's behavior on your website before and during their chatbot contact. Ask your team for their input on how they can be of assistance.
4. Make use of a tool that collects metrics and puts them into context.
MaestroQA automates QA data collection and saves time. QA data is used to verify that agents are following FCR best practices. This data is also crucial for uncovering new insights that will help increase first contact resolution. To get the complete story behind agent performance, you will need to use your QA data along with other metrics.
5. Personalize your customer service.
You don't have to get all of the data from your service center. Consider your customer demographics. Get insights from your marketing department. Who are you trying to sell to? What are your company's solutions to customer problems? What is your company doing to address the needs of your customers? You'll be able to better understand your customers' needs and preferences by putting yourself in their shoes. You will also be able to personalize every interaction.
6. Listen to your customers.
Ask your customers to share their customer service experiences. Short surveys can be included at the end of chat conversations, such as phone, text, or email follow-ups, and many other things. It's not enough to simply ask questions. It is important to ask the right questions based on your goals. Are you looking for a CSAT-style, one-question survey? Do you need to learn more about their contact details? Once you have the data, you can then consider making changes based on what your customers are experiencing.
7. Particular attention should be paid to multiple-contact information.
Customers, who contact you multiple times for the same issue or similar problems should ask: Why? Review the QA scorecards of those contacts, listen to or read the transcripts, and talk to agents who have dealt with sticky situations. Are agents looking for quick access to information in a knowledge base? Are customers able to access follow-up documentation? You can learn from the mistakes and make smart improvements for the future.
8. To assess agent understanding, use QA scorecards
Reviewing your QA data with a platform like MaestroQA will reveal cracks in training and best practices, and provide a starting point to coach. Maybe your agents don’t know how to use a certain SOP. Perhaps your knowledge base is slow loading, which makes agents guess the best answers. You may find some operational flaws in your QA scorecards that can be fixed by a deep dive.
9. Coaching is a great way to get your agents started.
You should consider creating a coaching program if you don't have one. Coaching allows agents to grow based on their individual metrics as well as the companies. Agents will feel empowered to develop their careers in your company if they have a well-run coaching program. It makes it possible to use data dashboards on a practical, real-world level. It helps you and your team to understand why you ask them to prioritize particular aspects of customer service. This will encourage greater buy-in.
10. To solve repetitive problems, you can use website data.
Ask your web team about bounce rates and conversion rates. Also, ask about pages that are most frequently visited. Make sure that your web team is proficient in the product if users spend six minutes reading the user manual online. You might also want to feed this information back into research and development. Website visitors may search for information on a topic using your chatbot. Include this information in your overall follow-up plan. Your data shouldn't be kept in silos. Customers don't live in silos.
11. Use data to guide better question-asking.
You may discover common themes in your data that explain why people make repeated contacts. This information can be used to guide your agents or your AI to know when to ask follow-up questions. It's a good idea to ask each contact, "Can you help me with anything else?" You might notice that multiple contacts have the same root cause. Your agents will be better equipped to assist customers if they can help them find the root cause. They can then resolve the root cause on their first contact.