Descriptive Analytics: Smart Business Decisions Fueled by Facts

This post is part of our Analytics of Things series.

Descriptive analytics involves examining historical data to understand what has happened in the past. It consists of viewing and analyzing raw information to reach an event consensus. The analytics gained provide context and help visualizations so users can understand trends and any resulting patterns.

Popularly, it is known as the simplest type of analytics. For example, it can answer questions like:

• How many people downloaded an app in a week?
• Which page was the most visited on a website?

Main Objectives of Descriptive Analytics

Any business's premise to implement an analytical method would be to assist in making data-driven and informed decisions. Some of the benefits that organizations can get from descriptive analytics are:

• Monitor key performance indicators (KPIs), such as sales, revenue, customer satisfaction, retention, and loyalty.
• Understanding the past is crucial to understanding patterns, trends and any correlations.
• You can identify areas of strength and any overlooked weaknesses by comparing results with your goals and benchmarks. Comparing against competitors provides a better understanding of where you stand in the market.
• Measuring objectives via insights driven by decision-making. Such decisions would have been made by stakeholders, considering specific goals.
• Package complex data into something easily digestible, such as visually appealing charts or graphs, to improve communication and ensure everyone is on the same page without being overwhelmed.

Discover how diagnostic analytics focuses on cause and effect, to discover the root cause of the 'why.'

Challenges in Implementing Descriptive Analytics

As with all analytical techniques, descriptive has its fair share of challenges. How an organization deals with them and implements procedures to alleviate hurdles would provide valuable insights to improve decision-making, operational efficiency, and competitiveness.

• Quality: Accuracy and consistency are significant challenges, as only working with accurate or complete data can lead to correct conclusions and decisions.
• Integration: Sometimes, organizations distribute their data across multiple systems and databases. Integrating this for analysis can be complex and time-consuming.
• Volume: With volume increasing daily, IT departments need robust hardware and software solutions to handle processing large datasets.
• Security and Privacy: Protecting the business's and its customers' sensitive data while keeping it accessible to various departments and stakeholders can be a juggle.
• Talent and Expertise: From analysts, scientists and engineers - data requires someone experienced to work for you so that the data can give you the results you need.
• Technology Stack: Selecting the right tools and technologies for storage, processing, and visualization is crucial.
• Costs: Software, hardware, personnel, and ongoing maintenance expenses for systems that perform descriptive analytics can be costly.

Transform Your Business

How would all of this transform my business? Below are some examples of how these analytics can impact your organization.

• Improved Decision Making: Identify what has worked and what hasn't. Example: Retail businesses can optimize inventory and marketing by analyzing historical sales data for categories, seasons, and regions.
• Performance Monitoring: Track and measure KPIs and assess performance against benchmarks and targets. Example: a SaaS company can track user engagement and subscription renewal rates; this enables them to improve customer retention.
• Historical Trend Analysis: Identify patterns, trends, and cyclical behaviors that may have gone unnoticed. Example: A hotel chain can adjust pricing and marketing strategies based on historical occupancy and peak booking trends.
• Customer Insights: Segment your customer base, tailor your marketing strategies, and increase customer satisfaction. Example: E-commerce businesses can create personalized product recommendations based on past purchase behavior, to improve the experience and sales.
• Operational Efficiency: By identifying areas for improvement, you can streamline operations, reduce costs, and enhance efficiency. Example: Optimize manufacturing processes by identifying production bottlenecks and reducing downtime based on historical analysis.
• Competitive Advantage: Understanding how your business performs compared to competitors is crucial. Example: Businesses can gain a competitive edge by comparing pricing strategies with competitors.
• Customer Satisfaction and Loyalty: Analyze historical customer feedback to identify areas where you can improve customer satisfaction. Example: Retailers can address common dissatisfaction identified through historical feedback and product return analysis.

In conclusion, if you want to unlock valuable insights from historical data to transform businesses by enhancing performance, efficiency, and customer satisfaction, look no further than descriptive analytics.