Data Analysis Methods
Without further ado, here are the seven essential types of data analysis methods among with some use cases in the business world:
1. Cluster analysis
The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.
Let’s look at it from a business perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let’s face it, with a large customer base, it is timely impossible to do that. That’s where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.
2. Cohort analysis
This type of data analysis method uses historical data to examine and compare a determined segment of users’ behavior, which can then be grouped with others with similar characteristics. By using this data analysis methodology, it’s possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.
Cohort analysis can be really useful to perform analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up to your site. For this you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.
A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide. In the bottom image you see an example of how you visualize a cohort analysis in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.
3. Regression analysis
The regression analysis uses historical data to understand how a dependent variable’s value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable’s relationship and how they developed in the past, you can anticipate possible outcomes and make better business decisions in the future.
Let’s bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Like this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.
If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.
4. Neural networks
The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of data-driven analytics that attempts, with minimal intervention, to understand how the human brain would process insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.
A typical area of application for neural networks is predictive data analysis. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced data scientist.
Here is an example of how you can use the predictive analysis tool from datapine:
5. Factor analysis
The factor analysis, also called “dimension reduction,” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal analysis method for streamlining specific data segments.
A good example to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, place where they bought the product, frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes to the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.
If you want to start analyzing data using factor analysis we recommend you to take a look at this practical guide from UCLA.
6. Data mining
A method of analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, data patterns, and trends to generate and advanced knowledge. When considering how to analyze data, adopting a data mining mindset is essential to success – as such, it’s an area that is worth exploring in greater detail.
An excellent example of data mining is datapine intelligent data alerts. With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs, you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.
In the following picture you can see an example of how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions and revenues, the alarms will notify you if the goal was not completed or if it exceeded the expectations.
Example on how to use intelligent alerts from datapine
**click to enlarge**
7. Text analysis
Text analysis, also known in the industry as text mining, is the process of taking large sets of textual data and arranging it in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your business and use it to develop actionable insights that will propel you forward.
Modern data analyst tools and techniques accelerate the process of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it’s positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant for your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article.
By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. One of the most effective data analysis tools and techniques you will ever invest in.
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How To Analyze Data? Top 15 Data Analysis Techniques To Apply