Feelingstream - Journey from data collection to decision-making

6 Steps of Data Analysis

Data analysis and the use of customer conversations, in our opinion, are the future of empowered decision-making. Making informed business decisions requires having access to reliable sources of information, such as customer conversations. With the technology we have today, the boundaries of what we can learn from customer interactions are being pushed further and further. In the past, using data for decision-making was limited, and gut feeling played a significant role in the process.

Nowadays customer conversations can be tapped into securely and used for knowledgeable decisions for all areas of the business. Analysis can help raise customer service quality, improve sales performance or increase efficiency. Customer conversations provide large amounts of feedback for products as well as processes. Never before has this information been used to its full potential.

In this article, we will introduce the 6 simple steps of data analysis that we believe will help you make more informed decisions. 

1. Determine your goal

While it is always a good idea to go over previous reports, listen to and read various conversations for ideas, etc., we think that in order to get the best results, data analysis should always be motivated by a business question or a goal.

These goals or questions can come from various sources or ways, such as your own quarterly goals or the overall business strategy. Ideas may also strike you as a hint from an outer source, creating a question relevant to your position and business. 

Here are a few examples of goals: 

  • understand why your customers are calling 
  • reduce incoming calls by a certain percentage 
  • reduce average handling time or wait times of calls 

These goals will help you better understand the problems your clients are experiencing and will help you identify any process or service bottlenecks. An alternative approach would be to ask questions that are relevant to your business. For example

  • why are we receiving calls about our self-service and what can we improve? 
  • how are our Agents handling questions about a certain product? 
  • which sales pitches or arguments are our Agents using for our campaign?

These are merely a few examples of possible questions. Check our article for additional examples of typical business questions “10 typical questions that AI-driven customer service quality monitoring can help you answer – Part II”.

2. Understand the context around business and data

When you know your goal or question, then the next step is to create context – you need to understand the data that you have and that you can access to investigate your business question or goal. Do you want to look at the last month or the last year? Is it a certain type of conversation you need to look at or a particular channel? Are you missing any data? Which additional information can support this investigation? 

To give you an overview of your call drivers, for example, you might want to know why your customers are calling you. Then what? Should you look at only inbound calls? Do you have access to Agent and customer audio separately? Do you have multiple languages and the capacity to analyse them?

Answering these questions will help you understand what you can work with. 

3. Acquire an overview of the data

Getting a broad overview of the data is the next step in the data analysis process. To do this, you must gather and thoroughly examine all available information. You also need to decide whether there is any incomplete or missing data that may be required before continuing the analysis. Furthermore, it’s crucial to identify any potential outliers or anomalies in the data that you might need to address.

Having a general understanding of the data is crucial if you want to do the analysis correctly and efficiently. By giving the data the attention it deserves, you can ensure that your analysis has a solid foundation.

4. Formulate specific hypothesis

Once you have created an overview of your data, it’s time to consider the hypothesis or hypotheses you wish to explore further.

Your ideas or hypotheses may come from various sources, such as thoughts that arise while creating the initial overview, reports or resources outside of the conversations, customer feedback or comments, or something you heard on the customer service floor.

Be sure to record all of your hypotheses and prioritise which ones you will investigate first if you have multiple ideas.

5. Test the hypothesis

In order to test a hypothesis, it’s necessary to gather data that either supports or disproves it. As your understanding deepens and you become more familiar with the data, make sure to record your findings and notes to avoid losing any important ideas.

Additionally, be sure to write down any additional hypotheses that you may come across during the process. While it’s important to avoid getting sidetracked, it’s equally important not to let potentially valuable ideas slip away.

6. Validate results

Validating your results by listening to or reading customer conversations is a crucial step in the process. The number of events you should review depends on the level of confidence you need in your results.

Once you have completed the validation process, you can decide what steps to take next. If you have several hypotheses, you can investigate each one separately and compare the outcomes. You should consider the scope of the problem, its impact on customers, potential benefits, and other relevant factors.

It’s a good practice to periodically re-evaluate and measure issues over time by revisiting earlier investigations. This is because issues may change over time and require ongoing monitoring.

Data analysis as a process 

Data analysis is an ongoing cycle that involves asking questions, investigating and learning. As you progress through this cycle, new ideas and areas for investigation may arise. However, it’s crucial to always keep your business goals and opportunities at the forefront of your mind. Once you’ve collected the relevant data, analysed the results, and determined whether to take action or simply observe, you can move on to the next hypothesis or question.

Don’t let valuable customer feedback go to waste – start using data analysis to make informed decisions for your business today. Follow these six simple steps outlined in the article to get started!