One of the key insights from the Global State of Enterprise Analytics says that 65% of global enterprises increased their analytics spending in 2020. This statistic shows how much businesses globally are willing to invest in improving their data capabilities and leveraging insights for making strategic decisions.
The increased spending shows the growing recognition of analytics as a critical tool for gaining competitive advantages, optimizing operations, and enhancing customer experiences. As enterprises navigate a world led by data, the emphasis on analytics is likely to continue rising, reflecting its importance in achieving business objectives and responding to market dynamics
But data alone isn’t enough. To properly utilize its potential, businesses need to master different types of analytics, each serving a unique purpose in the decision-making process.
In this post, we’ll explore what analytics is, the types of analytics, and the various applications of analytics in business.
What role does data analytics play in the growth of a business?
With more businesses coming into the market daily, the competition has become increasingly fierce. To stay ahead, companies must leverage every advantage available to them, and one of the most significant tools available to them is data analytics. Data analytics plays a crucial role in business growth by enabling organizations to make informed decisions, optimize operations, enhance customer experiences, and identify new opportunities.
Businesses can gain a distinct advantage over competitors, increase efficiency, and gain a deeper understanding of their customers by using data.
Now, let’s take a deep look at the several types of data analytics, their definitions, techniques, benefits, and limitations.
What are the Four Types of Data Analytics?
Let’s consider a fictitious e-commerce company and call it “GreenLeaf Essentials.” GreenLeaf Essentials is an online retailer specializing in eco-friendly household products and personal care items.
GreenLeaf Essentials has noticed a decline in customer retention rates over the past year. They aim to understand the reasons behind this drop, predict future retention trends, and implement strategies to improve customer loyalty.
- Descriptive Analytics
Descriptive analytics is all about analyzing historical data to understand what has happened in the past. It’s the first step in data analysis, providing a clear picture of past events. You can say it’s the foundation upon which other types of analytics are built.
It helps answer the question, “What happened?” and we will apply that to the above business problem.
Using descriptive analytics, the company will analyze data from the past year to identify when and why customers stopped making purchases. They find that churn rates are higher after three months of inactivity.
Benefits and Limitations
The main benefit is that it provides a comprehensive overview of historical data, making it easier to identify patterns and trends. However, its limitation is that it only tells you what happened, not why it happened or what might happen next.
Diagnostic Analytics
Diagnostic analytics goes a step further than descriptive analytics. It aims to understand why something happened by examining the data in more detail. Its main purpose is to identify anomalies within the company’s data.
How it Builds on Descriptive Analytics
While descriptive analytics highlights what happened, diagnostic analytics digs into the data to uncover the reasons behind those outcomes. It involves identifying anomalies and correlations within the data.
With diagnostic analytics, GreenLeaf Essentials can analyze customer feedback and support tickets, which will help them discover that many customers are dissatisfied with the delivery times and customer service.
Benefits and Limitations
The benefit is gaining deeper insights into the causes of past performance, which can guide corrective actions. However, it can be time-consuming and complex, requiring specialized skills and tools.
Predictive Analytics
Predictive analytics uses historical data to make informed predictions about future events. It’s about looking forward and anticipating what might happen next.
It answers the question, “What is happening in the future?”.
With predictive analytics, GreenLeaf Essentials will build a predictive model to identify customers at high risk of churning within the next three months based on their purchase history and engagement metrics.
Benefits and Limitations
The primary benefit is anticipating future trends and outcomes, enabling proactive decision-making. However, it’s not always 100% accurate and depends heavily on the quality and relevance of historical data.
Prescriptive Analytics
Prescriptive analytics goes beyond predicting future outcomes to recommend actions that can influence those outcomes. It’s about determining the best course of action to achieve desired results. It answers the question, “What should we do in the future?”.
How it Incorporates Other Types of Analytics
Prescriptive analytics combines descriptive, diagnostic, and predictive analytics insights to provide actionable recommendations. It considers various scenarios and their potential impacts.
Now that Greenleaf Essentials has fully understood its business problems and what caused them, prescriptive analytics will be used to develop personalized retention strategies, such as targeted marketing campaigns, loyalty programs, and improved customer service protocols for at-risk customers.
Benefits and Limitations
The benefit is providing clear, actionable recommendations that can improve decision-making and operational efficiency. However, it can be complex to implement and requires significant expertise in both data analysis and the specific business context.
Conclusion
The Global State of Enterprise Analytics reveals that 65% of global enterprises increased their analytics spending in 2020, underscoring the essential role analytics plays in strategic decision-making. This investment reflects a growing recognition of analytics as a critical tool for gaining competitive advantages, optimizing operations, and enhancing customer experiences in an increasingly data-driven world.
To fully utilize data, businesses must master different types of analytics:
- Descriptive Analytics: Provides a comprehensive overview of historical data to identify patterns and trends.
- Diagnostic Analytics: investigates the reasons behind past events by identifying anomalies and correlations.
- Predictive Analytics: use historical data to forecast future events, enabling proactive decision-making.
- Prescriptive Analytics: recommends actions to influence future outcomes, combining insights from other types of analytics.
The example of GreenLeaf Essentials illustrates how these types of analytics can be applied to understand customer behavior, predict trends, and implement strategies to improve customer loyalty.
Take Action
To fully leverage data analytics:
- Invest in Tools and Expertise: Ensure your organization has the right tools and skilled professionals.
- Explore All Types of Analytics: Utilize descriptive, diagnostic, predictive, and prescriptive analytics for comprehensive insights.
- Apply analytics strategically: identify business problems, uncover underlying causes, predict trends, and implement actionable strategies.
By investing in analytics and applying it strategically, businesses can stay ahead of the competition, optimize operations, and enhance customer experiences, driving growth and success.
