Businesses are overwhelmed with information, which means it often goes ignored.
Granted, it can be hard to sort through huge amounts of data, especially if you don’t have the time and resources. However, with the right tools and processes, you can start putting this data to use in next to no time and with minimum manual input.
To improve how you analyze your data, follow these 5 steps in the data analysis process:
Before jumping into your data analysis, make sure to define a clear set of 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’ll need to collect (and what type of analysis you’ll need to perform).
Design your questions around a specific problem and possible solutions. For example, if you see a spike in low CSAT scores in customer satisfaction surveys that asked customers to rate customer support, you might ask the following questions to solve the problem.
Goal: improve customer support
Once you’ve defined your goals, you’ll need to decide how to measure them.
For example, if you want to measure how customer service agents are performing, maybe you’ll calculate how long it takes each agent to complete a task and measure each agent’s performance against the average.
If you want to know what is making customers unhappy about your customer service, you might want to measure how often customer support is mentioned or even how often an agent’s name is mentioned.
Now that you know what your goals are and how you want to measure them, you can start collecting the correct type of data. While it’s best practice to collect both quantitative and qualitative data, you’ll also need to collect data that’s relevant to the questions you're trying to answer.
Quantitative data: structured data that can be quantified and measured. For example, tags and numerical data,
Qualitative data: unstructured data that needs to be structured before mining it for insights. For example, text, speech, images, videos.
Quantitative data is often stored in databases or Excel sheets, ready for you to analyze. But, where can you find qualitative data?
Here are a few examples of qualitative data:
Most of this data will be stored in the tools you use on a daily basis, like Zendesk, Gmail, and social media management platforms. Some of it you may need to gather using web scraping tools (if the data lives on websites, blogs, or forums).
Keep in mind that the success of your data analysis depends heavily on the quality of your data, so 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.
Data analysis tools, 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.
These tools are great for getting started with data analysis, but there are more complex data analysis methods that you can use to go even deeper with your analysis.
Choose your data analysis method:
Some of the these data analysis methods use AI and machine learning to help you automatically analyze large sets of unstructured data. But don’t let that put you off. There are many out-of-the-box tools that come with pre-trained machine learning models so you can get powerful insights in just minutes, without writing a single line of code.
A pre-trained keyword extractor, for example, can quickly summarize your data by pulling out the most frequent words in your data. Sentiment analysis, on the other hand, automatically classifies your data into positive, negative, and neutral.
Try out this ready-to-use sentiment analysis model to see how easy it is to extract insights from qualitative 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.
Now for the fun part. Transform your data analysis into striking data visualizations using data visualization tools, which help summarize your data so you can easily spot trends, patterns, and relationships in your data. They’re also a great way to back up business decisions and present your findings to the rest of the team.
Data Visualization Tools
Once you’ve visualized your data, start making decisions that help you reach your business goals.
Analyzing data allows companies to learn what’s working well for them and what to improve in order to grow.
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 AI machine learning tools, like MonkeyLearn, to understand why numbers and curves are fluctuating.
Sign up for a free demo to see how easy it is to perform a deep analysis of your data.
September 17th, 2020