Collecting customer data is just the beginning to understanding your customers. You then need to analyze this data
Analyzing data is just one step in the data analysis process but by far the most difficult and important step.
Through data analysis, you can make sense of raw data, identify trends and patterns, and gain valuable insights to help you make smart business decisions.
The different data analysis techniques are vast. Finding the right data analysis strategy will depend on your goals and the type of data you wish to process: unstructured, structured, or semi-structured.
Read on to learn more about how to analyze data by following a robust data analysis process, and how to get meaningful insights from your business data in next to no time.
Data analysis is the process of organizing, interpreting, and visualizing data, in order to extract insights for smart and effective decision-making. Using different data analysis methods and tools, you can turn data into meaningful and actionable information.
The two types of data that businesses can analyze are:
You’ll need to use different methods, techniques, and tools to extract value from each type of data, but you can follow the same seven steps in the data analysis process to gain insights from your data.
To improve how you analyze your data, follow these 8 steps in the data analysis process:
Before jumping into data analysis, make sure you define a clear set of measurable goals. What do you want to obtain from data? What’s the problem or situation that you are trying to understand? Knowing this will help you identify what data you need to collect (and what type of analysis you need to perform).
Design your questions around a specific problem and possible solutions. For example, if your receive a bunch of NPS survey responses about your customer service and notice a spike in Detractors, you might ask the following questions to solve the problem:
Once you’ve defined your goals, you’ll need to decide what to measure and how to measure it. As mentioned, above, there are two types of data: quantitative and qualitative.
Quantitative data could potentially answer the first question in the above example: Can we improve customer service without hiring more staff? You’d dig into numerical data, for example, on average how long it takes each agent to complete a task to find out which agents take longer to respond to tickets. Maybe, those who take longer to respond need more training to bring them up to speed.
Now, to answer the second question: What is it about our customer service that Detractors are unhappy with?, it’s likely you’ll need qualitative data. to find out exactly where the problem lies in your customer service.
Do Detractors often mention response times in their open-ended survey responses, or is there a particular agent that is often mentioned? By quantifying qualitative data, you can know exactly what you need to improve.
Below are some data methods you can use to measure or quantify your data, depending on the nature of your data and the results you want to obtain:
Customer data collection is an obvious step in the data analysis process. Without data, you can’t go much further. But you also need to know which type of data to collect.
While it’s best practice to collect both quantitative and qualitative data to gain a broad perspective of the situation, you’ll also need to hone in on relevant data. Ultimately you’ll need to collect data that’s relevant to the goal you’re trying to achieve.
You probably know where to find quantitative data about your business, but perhaps you’re unsure where to find qualitative data. Here are a few examples of what to look for:
Most of this data may already be stored in the tools you use on a daily basis, like Zendesk, Excel, and Google Sheets. Some of it you may need to gather using an API, integrations, or web scraping tools (if the data lives on websites, blogs, or forums).
The success of your analysis depends heavily on the quality of your data. Take the time to prepare your data, by removing noise and unnecessary characters, HTML elements, and punctuation marks, which usually appear in unstructured text data.
A few tools that may help you:
When performing text analysis, you also need to remove stop words (like “a”, “in”, “out”, “there”). Finally, when analyzing customer opinions, use an opinion unit extractor to slice and dice each comment into opinion units. Customer feedback often contains more than one opinion and topic, so splitting this data will give you more accurate results.
Spreadsheet software, like Excel, Google Sheets, and Airtable, and business intelligence tools, like Tableau and Google Data Studio, are excellent for crunching numbers. They allow you to plug in your quantitative data and create comprehensive visualizations, charts, and graphs. Using these data analysis tools, you can easily spot patterns and trends at a glance, as well as compare data to previous periods.
But what about qualitative data?
Machine learning tools can help you make sense of large sets of unstructured data. You can use out-of-the-box tools that come with pre-trained machine learning models so you can get powerful insights in just minutes. This keyword extractor, for example, can quickly summarize your data by pulling out the most frequent words in your data. If your data requires a more customized approach, you can build your own no-code text analysis tools, using an intuitive tool like MonkeyLearn.
Once you’ve trained your model using your business data and criteria, you can connect it to the tools you love through the MonkeyLearn API or integrations(like Excel, Google Sheets, and Zendesk), and start gaining value from data in real-time.
Through data analysis, you can transform raw data into meaningful insights.
Analyze quantitative data in Excel using formulas, graphs, cross-tabulations, and more, to gain quick insights in numbers. Then use text analysis tools to automatically classify or extract text data from product reviews, social media posts, and other types of qualitative data.
If you’ve recently launched a new feature, for example, you may be interested in listening to what your customers have to say. Look for relevant keywords in customer feedback, or perform sentiment analysis on customer opinions mentioning specific features. Try out this pre-trained sentiment analysis model to see how easy it is to extract insights from qualitative data:
Use data visualization tools to summarize your data and turn your data analysis results into attractive and easy-to-understand dashboards, graphs, and reports.
By creating interactive visualizations that draw from multiple data sources, you can spot trends, patterns, and relationships in your data, and easily share those insights with your partners.
Look at the insights in your dashboard and use them to respond to your initial goals. Here's an example of what your results might look like:
Now that you have a deep understanding of the situation, you’ll be able to suggest improvements or identify which areas of your business to prioritize.
And don’t forget to close the customer feedback loop by letting your customers know that an issue has been solved, and processes improved.
Businesses have been taking advantage of quantitative data for a long time now, but there are so many more insights they can gain from qualitative data.
Fortunately, text analysis tools make it easy to gain true value from this raw data. By combining both quantitative and qualitative data analysis, businesses can spot trends and truly begin to understand their customers.
Use everyday tools, like Excel, to analyze your data and understand what’s happening. Then power up your data analysis with machine learning tools, like MonkeyLearn, to understand the ‘why’ behind the numbers, trends, and patterns.
Sign up for a free demo and start diving deep into your data!
September 17th, 2020