What are the main types of data analytics? [basic definition+examle]

We live in an era where social media has become ubiquitous. In light of the influence and power of social networks, nothing is more important than tracking the results and performance of posts and content produced. Social media has created a big competition due to having a sizeable statistical community and also being accessible. Knowing what content works and what doesn’t is the key to success in social media. For this analysis, you can use social media analytics types. We will discuss this issue in the rest of this article.

Data analytics types (methods) 

Companies aiming to increase revenue, improve products, and retain customers should consider data analytics. Global management consulting firm McKinsey & Company found that data-driven companies acquire new customers 23 times more effectively than non-data-driven companies.

Nothing is more critical to social media success than social media analytics. To succeed in social media, you can use four methods of data analysis.

These four types of data analytics are:

1. Descriptive Analytics

2. Diagnostic Analytics

3. Predictive Analytics

4. Prescriptive Analytics

Each social media analytics method asks us different questions, and the solution presentation will differ. How we recognize which of these things is appropriate for us in which situation is very critical.

1. Descriptive analysis (what happened?)

How is your business doing? Do you want to know the state of your business? Descriptive analytics plays a key role here. As data comes in real-time, real-time analytics uses practical visualization tools such as dashboards to analyze it. As a result, it provides insight into how past behaviors can impact future decisions. Next, we want to check what descriptive analysis tells us.

What can descriptive analysis tell us?

All businesses use descriptive analysis to evaluate, compare, detect anomalies, and identify relative strengths and weaknesses. Next, we want to know what the descriptive analysis tells us.

Evaluate.

In the first step, you should analyze various aspects of business operations from the behavioral perspective of users. Essential criteria for evaluation are:

• Business performance (sales, costs, and profits)
• Website and social network performance (click rate and conversion rate)
• Customer satisfaction (customer satisfaction score and social media likes)

Compare

You can categorize the collected data on a specific basis. After classifying the data, you can compare them over a particular period.

Identify anomalies

In most cases, descriptive statistics may show worthless and outliers. You should review and analyze more of these cases and reports in this case.

Identify strengths and weaknesses.

Knowing our strengths and weaknesses in social networks makes us understand what we should pay more attention to overcome our weaknesses. For example, you may need more users to work with you. In this case, using content that encourages users to interact is better.

Descriptive analysis steps

To have a proper descriptive analysis, we need to consider specific steps. Therefore, you should perform these steps for better descriptive analysis. These steps are:

• Quantify goals.
• Identify relevant data
• Organize the data
• Present reports visuality

Here’s an example: 

Reporting is one type of descriptive analytics. You’ve already used descriptive analytics if you track social media engagement or website traffic. A trend report analyzes current metrics against historical metrics to compare them to current metrics generated from your website, advertising, or social media content.

As an example, you would like to know which media channels drive the most traffic to the product pages of your company’s website. Using descriptive analytics, you can analyze the page’s traffic data to determine the number of users from each source. If you want to go one step further, you might compare traffic source data to historical data from the same sources. It will be easier to update your team on trends; for example, you might highlight that paid advertisement traffic has increased 20 percent from last year.

2. Diagnostic analysis (what caused this to happen?)

Diagnostic analysis, unlike descriptive analysis, focuses on numbers. For example, figures such as the number of followers, page visitors, shares, and comments are among the things that affect the diagnostic analysis. This analysis focuses on the performance of posts and campaigns and tries to identify what made them successful.

It is important to note that both diagnostic and descriptive analysis are reactions that relate to events that have already happened.

In a diagnostic analysis, what should be considered?

As mentioned above, your attention should be more on data and figures in diagnostic analysis. To succeed in this analysis, you need to consider metrics and patterns.

Implementing advertising campaigns in the past and the statistics and figures of the content produced are beneficial. In diagnostic analysis, you can use performance metrics. These critical criteria are:

• Interaction between the user and the platform in a certain period

 In this case, you will determine whether the scent content engages the audience correctly.

• Increasing the number of followers

 Is the number of our followers increasing over a certain period?

• Click rate on links (UTM)

By doing this, you will find out which media leads users to your site. The best tool for measuring click rate through UTM is Google Analytics.

Here’s an example:

A description of customer behavior

Diagnostic analytics is critical for businesses that collect customer data to understand why customers behave as they do. A product audience-fit survey can provide insights into how to improve products, user experiences (UX), and brand messaging.

3. Predictive analytics(what is likely to happen?)

Predictive analysis is a type of analysis that uses past data from social media activity to predict our future movement in social networks. For instance, we can consider possible visits to a post at a specific time when performing predictive analysis.

 It is essential to consider the following factors when conducting predictive analysis:

• Analysis of our business status in social networks and communication with contacts
• Analysis of competitors’ situation
• Budget analysis of advertising campaigns

A Predictive analysis requires a series of information. We can forecast our activity’s future using analytical tools.

Here’s an example:

Marketing: Behavioral Targeting

Marketers leverage consumer data to create content, advertisements, and strategies to reach potential customers where they are. You engage in predictive analytics by examining historical behavioral data and using it to predict what will happen in the future.

Predictive analytics can assist marketing professionals in forecasting sales trends throughout the year and planning campaigns accordingly.

4. Prescriptive analysis(what should be done?)

The last stage of the data analytics type is prescriptive analysis. First, you have determined the position in social networks using descriptive and diagnostic analysis. After identifying your situation, it was time to analyze the forecast. In fact, by predicting your social networks, you draw a road map based on data. In the last stage, it is the turn of prescriptive analysis. At this stage, you should determine and implement your activities and strategies.

For example, start an advertising campaign. At this stage, you know what to emphasize in your advertising campaign. Identify your advertising pages and influencers.

Here’s an example:

Content Curation: Algorithmic Recommendations

If Among the examples of prescriptive analytics are algorithms that recommend content on social media platforms.

Data about your engagement history on businesses’ platforms (and possibly others, too) collects by algorithms. An algorithm can release a specific recommendation based on the combinations of your previous behaviors. In this case, the platform’s algorithm will likely analyze your past viewing habits and suggest similar content you might enjoy based on your data.

 conclusions

Throughout your analytics journey, it is necessary to remember the four types of analytics and how they interact. The first step is to use descriptive analytics to understand what has happened, followed by Diagnostic Analytics to understand why it happened. Following a Predictive Analysis, Prescriptive Analytics can suggest how to proceed based on what will happen next.

We recommend answering the following questions to determine the right mix of data analytics types for your organization:

• Exactly where does my company stand when it comes to data analytics?
• Is it necessary to dive deep into the data? Does my problem have an obvious solution?
• What is the gap between my current data insights and the insights I need?

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