U.S. companies lose more than $62 billion annually due to poor customer service. Getting customer service right should be a priority for every business, regardless of size. Businesses that are providing this exceptional customer service are data-driven, and making use of data from a variety of different sources.
Data isn't as complicated as you might think. Most modern customer service systems provide the necessary support to build a data-driven customer service team.
This article looks at the ways that you can focus on data in your customer service strategy. From executives to the point of customer-facing support, data can provide huge success for companies.
How to set up a data-driven team
Your customer service team should be ready to use customer data. This doesn't necessarily mean that they should be a data expert from day one - it's more about them having the right mindset.
Instill data centricity into your company culture by hiring employees that value data. Look for data-driven employees and make this an essential element of your companies hiring policy.
Ask questions that provide the opportunity to provide data-driven solutions. For practical tests, provide the opportunity for the candidate to demonstrate how they would solve the tasks using data.
It's essential to create a culture where data is embraced, and teams understand the value and insights that data can bring to their everyday work. This can be a complicated process as it sometimes means leaving opinions at the doors and understanding that data can provide insights that might be counter-intuitive. Often it can mean embracing new data types, such as location data or payment data.
Training your customer service team is crucial and can be one of the most critical factors in the success of your data-driven strategy. That's why the set up is pivotal in any company. Creating the right culture, hiring the right people, and providing the right tools and training are the initial steps for any company to take.
Building a data-driven customer service team
Creating a customer service team that is ready to embrace data is only the first step. Without proper access to data and the tools that can empower data-driven employees, frustration can flourish.
Make the data available in one place
If you want to become a data-centric customer service team, then you need to be able to access data quickly. On top of this, it all needs to be in a single place.
Connecting your data in a single place is imperative - typically, this is some kind of centralized data storage system. But if that sounds overly complicated, it can be just as effective using out of the box tools such as your CRM or help desk solution.
The crucial part is ensuring that your team updates data in this central location. It has to be easy for your customer service team to access, modify, and understand this data.
Standardize the data
For your customer service team to benefit from this centralized data, they will need to be using the same standardized metrics. The definitions of these metrics should be the same for every employee, regardless of where they sit in the team or wider company.
Different methodologies of measuring key customer metrics can cause confusion. How does your company measure churn rate, for example? Creating a standardized table of metrics and calculations is a powerful way to make sure your team is all on the same page.
Ensure the correct mechanisms are there to collect feedback
Without proper data collection in place, you can't be a data-driven customer support team. Ensuring that customers have ample opportunity to provide feedback (and are encouraged to do so) is a crucial first step.
But it's also important to collect behavioral customer data for support purposes. Such as landing pages, which support resources customers have viewed, and which product or services they have previously owned. Plus, as we have already mentioned, this all needs to be in a single place and easily accessible to your customer service team.
How to interpret the data
Get instant context and respond appropriately
Data helps you to understand customers quickly and to choose the best way to help them. A robust data-driven system will provide relevant behavioral information about your customers. It can also be used to suggest other common workflows based on previous tickets or other support processes.
These insights can help with the immediate issue - your customer service team can see that this customer has been reaching this support page and has tried to run four campaigns with the same bug. Therefore you can quickly identify the potential problem.
But the same data can then help you to address potential issues that the customer may run into at a later date. For example, previous customers with issue X also have problems with issue Y. Your customer service team can then share resources that will help customers to avoid future hiccups.
Focus on customer data when looking at feedback
Being data-driven allows your customer service team to report compelling insights based on accurate information. These insights are better at understanding the next steps to fix customer's issues and provide better support in the future.
For example, before focusing on the data, customer service teams might be saying things like, "I feel that some of our customers are churning because of this broken feature." But with a data-driven approach, you can move towards "34 of our customers are churning each quarter because feature x is broken. This broken feature accounts for $7,635 in lost revenue".
You can apply the same approach to any metric. Focusing on the data when looking at feedback is a powerful way of having valuable internal discussions, devising customer service strategy, and learning where to prioritize.
Internal data is also useful for customer service teams
Data-driven customer service teams can measure performance on an individual, team, and company level. Headline KPIs can be easily tracked with data, but adopting a data-driven approach also opens up more detailed reporting.
Customer service teams can often focus on KPIs, such as satisfaction scores and the number of tickets completed. But there are more important metrics beyond these that data can help to monitor and report on.
This can account for variation and can help to create a clearer picture of how teams are delivering customer service. Combining these results with insights and feedback from customers in a single team view can help to develop better ways of managing support requests in the future.
Remember that data can break, and be ready to mitigate when it does
Not all tools work correctly, and sometimes data-based systems can break. That's why customer service teams need to understand what these errors look like.
Companies must create a robust system in which employees can report bugs and maintain data sources.
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About the Author
James is a marketer who writes extensively about many emerging tech topics across big data, marketing, IoT and mobile. His interests include researching how data is transforming the world around us (for better and worse). He currently leads the marketing effort at Tamoco. James can be reached on Twitter at @tamocotech or @jamesewentech