Data analysis methods to create actionable insights
Understanding and making use of data analytics has become foundational to organizational leaders’ decision-making processes. Businesses across virtually every vertical rely on data analytics to identify potential growth hurdles, shed light on new opportunities, mitigate risks, and push forward toward long-term organizational goals.
However, to maximize the efficacy of their analytics, it’s critical that business leaders choose data analysis methods that align with the quality and scope of their data. This can be challenging due to the sheer number of data analysis techniques available.
To help business leaders navigate the process, we’ve put together a list of the most common and versatile methods to analyze data. We’ll be covering:
What is data analysis?
Data analysis is the process of collecting, organizing, and interpreting data with the aim of unlocking actionable business insights and guiding decision-making. To facilitate various use cases for organizational data, analysts will often condense, extrapolate, and visualize it into easy-to-digest charts and graphs.
Businesses use a variety of strategies to collect data for analysis. A few common data collection channels include:
- Surveys
- Online tracking
- Social media
- Newsletter signups
All of the data gathered from these and other sources will be grouped into one of two broad categories — quantitative or qualitative.
Once data is collected, classified, and organized, it can be explored using a broad array of advanced data analysis techniques, such as regression analysis, factor analysis, and dispersion analysis, just to name a few.
Quantitative vs. qualitative data
Quantitative data, often referred to as structured data, is measurable information. All quantitative data deals with statistical, mathematical, and numerical variables and analysis.
Conversely, qualitative data deals with nonnumerical variables, including images, videos, interviews, and comments.
Qualitative data, which is also sometimes referred to as “unstructured data,” can’t be measured objectively. It’s often organized around common themes.
12 common data analysis methods
There are many different methods to analyze data. To help you find the right technique for your dataset, we’ll be exploring some of the most useful data analysis methods and providing examples of each in use.
Our list of 12 common data analysis methods includes the following approaches:
1. Regression analysis
Regression analysis is a quantitative data analysis technique that allows businesses to model the relationship between dependent and independent variables. Multiple independent variables can be examined with the regression analysis technique.
When setting up a regression analysis, the dependent variable is the variable or factor you want to measure. The independent variables, by contrast, are factors that may impact the dependent variable.
For example, a restaurant owner may examine how weather can impact the number of delivery orders they receive. In this scenario the weather is the independent variable, and the number of deliveries is the dependent variable.
Regression analysis is one of the most commonly used types of data analysis methods, as it allows businesses to gain insights into how altering one variable may impact another.
The technique is popular for financial forecasting, data-driven marketing, and a number of other use cases. When businesses identify what independent variables are influencing their performance, they can take action to mitigate the impacts of these variations to limit circumstantial influences and achieve more desirable outcomes.
2. Factor analysis
Factor analysis is a specific type of regression-based data analysis that can be used with quantitative or qualitative data.
This technique serves to uncover new independent factors. Once analysts uncover these variables, they can use them to describe patterns and model relationships between the original dependent variables.
Factor analysis is useful for researching the relationships between variables within complex fields, including socioeconomic or psychological trends. For instance, marketers could use factor analysis to identify independent variables causing lower sales among specific socioeconomic groups within their target market.
Factor analysis isn’t just a great standalone data analysis method. It can also be a precursor for other techniques, such as classification and clustering.
Before datasets can be clustered or classified, analysts must have a clear view of any variables that might be influencing the raw data. Then and only then can they account for and monitor the impacts of these variables.
3. Cohort analysis
A cohort analysis is a quantitative technique that involves grouping subjects into cohorts. This method is primarily used as a behavioral analytics tool.
When classifying individuals into their cohorts, the model looks for shared characteristics or experiences. It allows analysts to look for common behavioral trends across audience segments by grouping individuals by similarities.
For instance, a brick-and-mortar and retail brand can conduct a cohort analysis to determine which sales channels are most popular across different age groups.
As part of their research, analysts might gather data on all purchases made over the past six months. They could then group customers based on where they made their purchase (i.e., online, via social media, or in person) and consider the average age of shoppers in each cohort.
In the example described above, researchers would use a cohort analysis to determine which age groups prefer which purchasing methods. They could then apply these insights to guide future marketing efforts and deliver curated advertising content to each segment.
4. Cluster analysis
Cluster analysis is another popular quantitative method. Like the factor method, the cluster analysis approach seeks to unveil various structures within a set of data. It sorts different data points into groups that are externally heterogeneous and internally homogeneous.
In other words, all of the data points within a cluster are similar, but they’re dissimilar to the data points in other clusters.
Cluster analysis is commonly used to investigate trends in specific geographic locations. Marketers also frequently employ cluster analysis to group large customer bases into specific segments.
For example, a marketing team might use cluster analysis to investigate why they’re achieving a strong return on ad spend on Facebook but not Instagram. However, applying the cluster analysis technique in this way won’t provide a holistic view of the problem.
The cluster analysis technique can uncover certain patterns within data, but it won’t explain what they mean or why they’re present.
5. Time series analysis
When businesses want to look at a large dataset in the context of time, they’ll often use a quantitative method known as a time series analysis. With this method, analytics have the chance to model time-dependent series of data. The purpose is to obtain meaningful data points like rules, patterns, or statistics.
Analysts can make predictions about future events by measuring the same variable at different points.
For example, retailers can analyze winter sales volume over a five-year period. If sales spiked during the second week of November over the last five years, they could safely predict that sales will increase at about the same time during the upcoming holiday shopping season.
Determining what will likely happen in the future can inform marketing and inventory management decisions.
Keeping with the aforementioned example, from a marketing perspective, retailers can ramp up advertising in a few weeks preceding the seasonal spike in sales to generate more traffic. Likewise, inventory managers can increase stock reserves to prepare for the surge in purchases and avoid stock-outs.
6. Discourse analysis
Discourse analysis is a qualitative method that seeks to better understand people by examining how they use language.
There are many different ways to structure a discourse analysis model. There are also various approaches for gathering discourse.
The rise of social media has made it easier for analysts to gather data for discourse analysis. These days, researchers can review how people use language in the comments section of a brand’s social media posts and compare their vocabulary, attitudes, and judgments to the ones they use to talk about the company or brand on a third-party review platform.
Analyzing online discourse across these two very different channels could reveal the general sentiments of social media followers versus product users who leave feedback on independent platforms.
7. Monte Carlo simulation
As a quantitative model and predictive analytics tool, the Monte Carlo simulation is often used to forecast the potential range of outcomes for a variable.
Under this model, analysts assign the variable in question a randomly chosen value that lies within certain possible outcomes. They then run a simulation to determine what the outcome would be if the independent variable were set at that value.
Analysts will repeat the simulation over and over, altering the variable each time to achieve a different outcome. Once they’ve plugged in all potential values from the possible distribution of outcomes, they’ll compile their results and draw conclusions.
A simple example of the Monte Carlo simulation involves calculating the probability of outcomes when rolling a pair of standard dice. Since you know there are 36 possible combinations, you can compute the probability of each pair of numbers.
The more times you run the simulation, the more accurate your predictive capabilities become. As such, you should simulate rolling the dice thousands of times for the most reliable insights possible.
8. Dispersion analysis
A dispersion analysis can be applied to either qualitative or quantitative data. Dispersion analysis isn’t nearly as common as some of the other techniques listed here, but it still plays an important role in understanding large datasets. The technique measures the spread or “stretch” of a given dataset, which helps analysts assess variability.
Typically, a dispersion analysis will consider two types of spread.
First, a dispersion analysis model will represent the variation among the data points themselves. It will then explain the variation between the extremes of the set and the average value. If the difference is significant, dispersion is high.
Dispersion is often used to assess risk. For example, if a venture capital firm is performing due diligence and wants to assess the risk of a particular investment, it may use a dispersion analysis.
In this scenario, analysts would likely look at the investment’s rate of return or losses over time. If the investment yielded a return of 1.2 last month and a loss of 0.5 the previous month, dispersion would be considered relatively low.
9. Artificial neural network analysis
Due to its sophistication and versatility, an artificial neural network analysis can be applied to qualitative or quantitative data.
This analysis leverages machine learning and artificial intelligence to make inferences and examine data by simulating the human brain. The more data that’s introduced, the more effective neural networks become at uncovering trends and identifying patterns.
Like many machine-learning-powered tools, artificial neural networks learn by trial and error. When the network receives data, it makes predictions, tests them for accuracy, and refines its predictions before starting the process over from the beginning. An artificial neural network can perform this cycle of analysis, verification, and modification in perpetuity.
As one of the most advanced data analysis techniques, artificial neural networks are frequently used in the finance sector to forecast market conditions, assess risk, and guide decision-making. These systems will likely become more prevalent in the near future with the continued advancement of machine learning technology.
10. Sentiment analysis
A sentiment analysis, alternatively referred to as a text analysis, is a form of qualitative analytics designed to provide a glimpse into consumers’ minds and understand how they feel.
Text analysis models use robust algorithms designed to associate certain words or phrases with feelings, opinions, or thoughts. Businesses use these insights to infer how customers feel about topics, products, ads, and other customer-facing brand elements.
Marketers can use sentiment analysis to gauge consumer response to products and content.
For instance, if a brand just launched its first major social media ad for a new flagship product, it could use sentiment analysis tools to learn more about how consumers feel about the product.
Interpreting the results of this type of analysis is relatively straightforward. If the comments section is flooded with positive words and phrases and the post receives many likes or other favorable reactions, analysts could reasonably deduce that the product will be well-received.
11. Grounded theory analysis
Grounded theory analysis is a qualitative method that prioritizes developing theories based on data that’s already been collected. Conversely, conventional methods use a confirmatory approach, meaning researchers will form a hypothesis before gathering data and attempting to confirm or disprove their theory.
The grounded theory analysis model relies on strict rules and procedures to mitigate the effects of confirmation bias. By following these procedures, analysts can remain “grounded.”
For example, a human resources department might apply the grounded theory analysis to understand why they’re having difficulty filling vacancies.
Once they identify the challenge they want to explore, analysts could collect data by conducting interviews and observing staff behaviors. Afterward, they would analyze this data to determine the potential causes of their hiring difficulties.
The grounded theory analysis model is useful because it allows researchers to explore a business challenge before forming a hypothesis as to why it’s occurring. This dispels preconceived ideas and prevents businesses from wasting time and resources fixing problems that don’t exist.
12. Discriminant analysis
In data mining, the quantitative technique known as discriminant analysis is one of the most useful ways to classify data.
Under this model, analysts will measure several independent variables to determine which group a piece of data should be placed in. The core purpose of discriminant analysis is to assign each observation to a category or group based on its independent characteristics.
Financial institutions can use discriminant analysis to classify loan applicants into high- and low-risk categories based on a series of independent variables. When performing this classification, analysts would likely consider factors such as an applicant’s credit score, length of credit history, average annual income, current debt, and debt-to-income ratio.
As with all forms of analysis, the effectiveness of discriminant analytics processes largely depends on the quality and quantity of the data used. For instance, if a financial institution only considered annual income and not debt-to-income ratio or credit history, the results would be severely skewed.
In this scenario, higher-income individuals would be placed in the low-risk category, even if they had low credit scores or subpar DTI. Conversely, lower-income individuals would be classified as high risk, even if they had exemplary credit and little to no debt.
Analyzing data with Adobe Analytics
Data analysis is a powerful tool for discovering actionable insights and strategies within user data.
Even more rudimentary data analysis methods can inform decision-making and lead to improved business outcomes. However, to realize the full potential of your data, you’ll need to implement more advanced analytics techniques, such as those outlined above.
When you’re ready to begin adopting innovative data analysis methods, choose a dataset you’re currently focusing on and identify whether it’s quantitative or qualitative. This will give you a starting point to determine which data analysis technique will work best for your dataset.
Your next task will be to empower your analytics team with Adobe Analytics. This robust toolset makes it possible to segment your users and analyze data from a wide range of sources. Adobe Analytics uses machine learning and AI to offer predictive analyses and identify patterns in user behavior.
Watch the overview video or request a demo today to learn more about how Analytics can help you analyze data and turn information into strategy.