Data and technology have changed the way we do business. Companies have more tools than ever to handle different tasks and available data to fuel their decision-making. That is why making business decisions based on intuition is a thing of the past.
Modern businesses make well-informed decisions and use their resources for maximum efficiency. That is why data analysis has become a mandatory process in any industry. It’s essential, like accounting or product development.
Let’s see how data analysis reflects on customer service and how you can use it to your advantage.
Data analysis refers to many methodologies and technologies for solving various business problems within business analytics. Data analysis has four different methods, each focusing on specific tasks:
- Predictive analysis: includes statistics and modeling techniques on datasets to create likely future outcomes and predictions.
- Statistical analysis: includes learning about trends, relationships, and patterns within quantitative data. This method allows researchers to uncover information that wasn’t evident at first.
- Competitive analysis: includes learning about competitors and assessing different aspects of their customers, services, products, marketing, sales, etc.
- Consumer data analysis: entails gathering customer information and analyzing it to learn more about consumers.
Data analysis is a critical process that lets you gather more quality data while allowing your business to put proper analysis in place to get the most out of your data.
Organizations that use proper data analytics processes to fuel accurate and relevant data can easily:
- Change, adapt, and improve the company mission, values, or vision to keep up with customer needs, market trends, and employees.
- Create, polish, and manage new sales and marketing strategies.
- Create better business plans or make upgrades to their current ones.
- Find the best solutions for current challenges and recognize technological shortcomings.
- Recognize their best and worst processes, departments, and workflows.
In other words, you can gather all the data you want, but if you don’t analyze it properly, it won’t give you the benefits you need. For example, many companies force customer service reps to juggle multiple platforms and perform manual data entry.
However, they can streamline data entry and make data analysis much easier with a simple CTI system, as they have consistent input.
Customer service and data analytics complement each other. That is especially true with consumer data analysis. Overall, data analytics can give more context, depth, and information necessary to provide better customer service.
Customer service allows you to gather valuable feedback and metrics you can analyze to learn what you’re doing wrong and improve different aspects of your process.
However, customers don’t always talk about what they dislike or like, and it’s often tricky to discover what they think. That is where data analytics comes into play, as it can uncover unseen trends and patterns.
More data means better insights, and better insights help create a personalized experience. Modern consumers want to feel unique and trust companies that try to give them what they need. Data lets companies learn what customers like the most about their ads, products, services, website, etc.
This information lets customer service reps approach every customer individually and establish meaningful communication.
With data analysis, companies can also learn how their customers use their services or products, the best features they like about them, why they bought them, any issues they might have, etc.
This information piles up over time and becomes the fuel customer service reps need to deal with queries more effectively.
Here are the essential steps to take to get all of the benefits above and then some.
With data analysis, you can find recurring trends and patterns that can be both positive and negative. Having the whole process at your fingertips with a clear overview can make it easier to implement these trends using CRM software.
There are so many KPIs in customer service, and you need to prioritize. Think about the core of the problem. For example, if your average response time is too long, you can analyze this process to see what’s wrong and not hire new reps immediately since the problem can be in many different areas.
Creating a visualization of customer journeys can help companies understand what motivates consumers. With this journey, you can also understand how your service fits into the whole process and improve any issues. You can solve these problems and create a customer journey map with data analytics.
With proper methods and technologies, data analytics can fuel your customer service process. You might need some time to develop an efficient data workflow, but in the end, you will see excellent returns on your investment.