Gathering data is key to understanding your customers.
But collecting customer data is only the beginning. You then need to analyze it.
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 for you will depend on your goals and the type of data you wish to process ‒ whether it’s unstructured, structured, or semi-structured.
There’s are plenty of tools that make data analysis faster and easier for businesses once you get the hang of them.
Read on to learn more about data analysis, and how to get meaningful information from your 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 raw data into meaningful and actionable information.
First, you need to know the type of data you’re dealing with:
Below are some data methods you can use, depending on the nature of your data and the results you want to obtain:
Automated tools for text analysis use machine learning and Natural Language Processing (NLP) technologies to provide relevant insights from text, whether classifying data (by topic, sentiment, intent, and more), or extracting specific information (like keywords, entities, and features).
You can use text analysis to detect topics in customer feedback and understand which aspects of your brand are important to your customers. Combined with sentiment analysis, you can gain in-depth knowledge about how customers feel towards each aspect.
You may discover negative mentions in product reviews are often about price, _or positive mentions in surveys are mostly about _customer service.
Text analysis techniques also include text extraction, helping you quickly identify frequently occurring topics in text-heavy datasets. You can also use this technique to extract specific pieces of information, such as company names.
Try out this company extractor to automatically detect if customers are mentioning competitors in your product reviews.
Statistical analysis involves examining large sets of quantitative data with the purpose of discovering trends and patterns. The two most popular forms of statistical analysis are:
Statistical analysis techniques are very frequent in market research. By surveying a sample of customers about their purchasing behavior, marketers are able to draw inference over a wider population.
T-tests (or a hypothesis testing tool) are often used in inferential statistic analysis. They’re used to compare two groups of data to detect significant variations between them. For example, a company might want to analyze the success of a marketing campaign by comparing their average sales on a regular week with their sales during the campaign.
Diagnostic analysis is an exploratory type of analysis, which tries to find the root cause of an issue, in order to explain _why _something happened.
Whether it is a fall in revenue, an increase in your shopping cart abandonment rates, or a sudden drop in website traffic, businesses need to find reasons to explain events related to customer behavior all the time. To do that, analysts need to sift through multiple sources of quantitative and qualitative data, search for correlation (the relationship between two variables) and use regression methods to understand how one variable may affect the other.
Predictive analysis tries to answer the question: _what will happen in the future? _By collecting historical data, and using statistical and machine learning algorithms, you can detect trends in past and present data and makes inferences about future outcomes.
This type of data analysis is allowing companies across all industries to forecast future performance, sales, demand, and customer behavior. In finance, companies are using predictive models to measure credit risk and detect fraud, while telcos are using them to identify customers at risk of churn and act proactively, offering them an improved customer experience.
Prescriptive analysis provides the best course of action for a company in a given scenario. It’s used to examine all kinds of raw data to predict possible outcomes for different decisions.
Prescriptive machine learning models are able to learn from new data and automatically adapt, making it possible to navigate uncertainty and rapidly changing conditions.
Although there are different data analysis methods depending on your goals, the data analysis process is the same and can be summarized in a series of seven steps.
Before jumping into the analysis, make sure to 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’ll need to collect (and what type of analysis you’ll need to perform).
Maybe you want to identify why customers are turning to the competition. Or estimate the volume of support requests you’ll have in the future, so you can allocate resources accordingly.
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 and business intelligence tools are excellent for crunching numbers. But what about qualitative data?
Machine learning tools can help you make sense of those large sets of unstructured data. Get powerful insights in just minutes using pre-trained models ‒ like this keyword extractor. 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, 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.
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. 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.
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 they weren’t paying enough attention to their qualitative data. Fortunately, text analysis tools make it easy to gain true value from this data.
SaaS tools like MonkeyLearn classify text data and extract important information from large datasets using machine learning.
Sign up for a free demo and start picking apart your data!
September 17th, 2020