Descriptive Analytics: Definition, Steps, Examples, and More

In our modern data-driven business environments, understanding the vast amounts of information generated by everyday operations is critical for both decision-making, strategic planning and so much more. 

Among the various types of data analytics, descriptive analytics plays a foundational and primary role in painting an initial picture of past and current events. 

The following is a comprehensive guide which delves into the topic of descriptive analytics, covering its importance, how its implemented, its advantages and disadvantages, key metrics, real-world examples, and how it’s used in conjecture with other analysis forms.

What is Descriptive Analytics? 

Descriptive analytics is a secondary research process of analyzing historical data to summarize and understand changes that have occurred in businesses. It leverages both data aggregation and data mining techniques to provide insight into past and current performance. 

Descriptive analytics forms the base of most data analytics projects, which also includes diagnostic, predictive, and prescriptive analytics. 

Each type serves a unique purpose, but together, they offer a complete bird’s eye view of an organization's data and performance, enabling businesses to learn from past behaviors, predict future possibilities, and inform what are the best actions to take to achieve their goals.

Importance and Applications 

Descriptive analytics allows businesses to identify trends, and anomalies in their performance, and often serves as the basis of further, more complex analysis. 

By benchmarking against industry standards and competitors, organizations can pinpoint their strengths and weaknesses, to aid in working towards continuous improvement and strategic growth.

The many applications of descriptive analytics are vast, and include:

• Identifying historical trends in sales, revenue, and customer behavior.
• Benchmarking performance against industry standards and competitors.
• Understanding operational strengths and weakness within an organization.

Practical Implementations of Descriptive Analytics: From Data to Insights 

Understanding how descriptive analytics works and how to implement it is essential for utilizing historical data to guide market research and business decisions and future strategies. This process involves navigating through 4 sequential steps:

1. Metrics identification: The first step is to define the key metrics you wish to measure and analyze, these could include sales growth, customer retention and satisfaction rates. Usually, these key metrics will represent key aspects and segments of an organization that a business wishes to investigate. 
2. Data Collection and Aggregation: This step involves gathering raw data from various sources within an organization which could include sales figures, customer feedback surveys, operational metrics, and more. The aim is to create a unified and complete dataset that represents the relevant aspects of a business.
3. Analysis: Once we have collected this data, the next step is to analyze it to identify patterns, trends, and relationships, through a wide variety of analytical tools, which could include measures of frequency, central tendency, dispersion, and position. 
4. Visualization and Presentation: The final step is to present the analyzed data in an understandable format. Visualization methods like charts, graphs, and dashboards are invaluable here, with tools such as Power BI and Tableau being commonly used to create these visualizations. A key objective of this step is to be able to present what can often be complex results in a simple way to non-technical audiences.

Advantages and Disadvantages 

Understanding the strengths and limitations of descriptive analytics is key, as it's quite often most useful when combined with further statistical analysis that completes its initial findings. Let's examine these key advantages and disadvantages. 

Advantages of Descriptive Analytics 

1. Simplification of Complex Data: Descriptive analytics breaks down vast amounts of complex data into clear and readable results, making it easier for stakeholders to comprehend historical performances.
2. Facilitation of Benchmarking: By simplifying businesses present and past performance, organizations can compare themselves against industry standards and competitors, identifying areas of strength and opportunities for improvement.
3. Identification of Trends and Patterns: By analyzing historical data, businesses can identify trends and patterns within their performance and their industry that can inform strategic planning and decision-making.

Disadvantages of Descriptive Analytics 

1. Limited Predictive Capabilities: Descriptive analytics focuses on past events, and while it can equip us with powerful insights about the past and the present, it cannot be used to predict the future when used on its own.
2. Potential for Bias: The selection of metrics and data interpretation can be subjective, as it is chosen by the analyst and the business itself, leading to the risk of biased insights if not carefully managed.
3. Dependence on Data Quality: The accuracy of descriptive analytics insights heavily relies on the quality of the underlying data. Poor data quality or a misunderstanding of what data should be looked at in the first place, can lead to misleading conclusions.

4 Real-World Examples of Descriptive Analytics in Action 

Customer Satisfaction Survey Results 

A supermarket chain conducts an annual customer satisfaction survey to better understand the quality of their service across its stores. In the survey they ask their customers to rate their satisfaction on a scale of 1 to 7 for 4 metrics (checkout speed, staff helpfulness, product availability and value for money). 

By calculating the average rating for each question, it is concluded that while customers are relatively highly satisfied with staff helpfulness, value for money and checkout speed, results showed that product availability consistently falls below expectations across several stores. 

These insights lead to a wider variety of products being added to their shelfs, improving customer satisfaction scores in the following year’s survey.

Demand Trends 

The trend detection techniques of the streaming service Netflix are a great example of the application of descriptive analytics. As part of Netflix’s internal analytics efforts, information on users' on-platform activities is collected and alongside being used to personalize recommendations to its users, it’s also used on a macro level to identify the movies and TV shows that are popular right now, and which shows and movies they should renew.  
 
This information not only helps Netflix subscribers discover what content is popular and potentially enjoyable to watch, but it also helps Netflix identify the genres, themes, and performers that have the highest demand, and they should focus on.

Progress Towards Key Performance Indicators (KPIs) 

Descriptive analytics can also be used toreport on the status of key performance indicators (KPIs) to track whether a business is meeting its targets as planned and whether any changes need to be made.

For example, a software company wants to increase the number of unique page views their website gets, with their target being 300,000. By using traffic data they’re capable of seeing in real time that halfway through the month, they received 100,000 unique page views, indicating that they are not on track to reach their target. This descriptive analysis of the progress made towards a specific KPI alerts the business that it is off track and can allow further analysis to examine what can be done to increase website traffic.

Customer Segmentation 

A telecommunications company segments its customer base using descriptive analytics to understand usage patterns, preferences, and satisfaction levels across different demographic groups.

Analysis reveals that younger customers prefer unlimited data plans and use streaming services extensively, while older demographics prioritize voice call quality and customer service. By comparing it to previous year’s results, descriptive analytics can tell you if this correlation between age and purchases has always existed or if its something new.

Results like this can then be used alongside other techniques such a diagnostic, predictive and prescriptive analysis to explain why this correlation might exist and predict how that relationship might develop in the future. 

Descriptive vs. Predictive, Prescriptive, and Diagnostic Analytics 

While descriptive analytics outlines "What happened?" by analyzing past data, its counterparts offer different perspectives:

• Predictive Analytics (What may happen?) - Using past data, predictive analytics is used to predict future patterns and consequences.
• Prescriptive Analytics (What should we do?) -Allows us to know what is the best recommendation or action to take in the future based on what has already happened. 
• Diagnostic Analytics (Why did this happen?) – Explores the causes of historical occurrences or performances allowing us to know what the source of a business’s current situation was.

Together, these analytical solutions can help us make data-driven decisions by complementing each other’s findings.

Conclusion 

Descriptive analytics is an essential tool for providing businesses with insights into past performance and operational effectiveness. By monitoring and analysing key metrics organizations can make informed decisions that drive success and strategic growth. By integrating descriptive analytics with diagnostic, predictive, and prescriptive analysis, businesses may get additional insight into the causes of events, their expected future consequences, and possible courses of action. Despite its limitations, descriptive analytics can still be immensely valuable on its own, as its simplicity can make it an easily accessible tool for organizations to use.