Business Analytics: Simplified

Saurabh Sharma

Business Analytics (BA) is like a GPS for businesses—it uses data to guide decisions, solve problems, and predict future outcomes. It turns raw numbers into actionable insights, helping companies answer questions like:

  • What’s happening?
  • Why did it happen?
  • What might happen next?
  • What should we do about it?

The 3 Main Analytics Techniques

1. Descriptive Analytics: “What Happened?”

  • What it does: Analyzes past data to summarize trends, patterns, or events.
  • Examples:
    • Monthly sales reports.
    • Website traffic analysis (e.g., most visited pages).
    • Customer demographics (e.g., age groups buying Product X).
  • Tools: Dashboards, charts, KPIs (Key Performance Indicators).
  • Significance:
    • Identifies strengths, weaknesses, and opportunities.
    • Answers: “How many customers churned last quarter?”
  • Objective: Understand the current state of the business.

Layman’s Example:
Think of it like checking your fitness tracker to see how many steps you walked yesterday.


2. Predictive Analytics: “What Could Happen?”

  • What it does: Uses historical data and statistical models to forecast future outcomes.
  • Examples:
    • Predicting customer churn (who might leave your service).
    • Forecasting sales for the holiday season.
    • Estimating inventory demand.
  • Tools: Machine learning (e.g., regression, decision trees).
  • Significance:
    • Helps businesses prepare for risks and opportunities.
    • Answers: “Will sales drop if we raise prices?”
  • Objective: Anticipate future trends or behaviors.

Layman’s Example:
Like a weather forecast predicting rain tomorrow—you grab an umbrella just in case.


3. Prescriptive Analytics: “What Should We Do?”

  • What it does: Recommends actions to achieve desired outcomes. Combines data, rules, and optimization.
  • Examples:
    • Suggesting the best marketing strategy to boost sales.
    • Optimizing delivery routes to save fuel costs.
    • Recommending personalized discounts to retain customers.
  • Tools: AI, simulation, optimization algorithms.
  • Significance:
    • Drives data-driven decision-making.
    • Answers: “Should we open a new store in City A or B?”
  • Objective: Provide actionable solutions to problems.

Layman’s Example:
Like a GPS suggesting the fastest route to avoid traffic—it doesn’t just predict delays, it tells you what to do.


Why These Techniques Matter in Business

  1. Descriptive:
    • Significance: Sets the foundation. You can’t fix a problem if you don’t know it exists.
    • Business Use: Track performance metrics (e.g., revenue, customer satisfaction).
  2. Predictive:
    • Significance: Reduces guesswork. Prepares businesses for future risks/opportunities.
    • Business Use: Plan inventory, manage cash flow, or target high-value customers.
  3. Prescriptive:
    • Significance: Turns insights into action. Maximizes efficiency and profitability.
    • Business Use: Optimize pricing, allocate resources, or design marketing campaigns.

Real-World Business Example

  • Problem: A retail store’s sales are dropping.
    • Descriptive“Sales fell by 20% in the last quarter, especially in urban stores.”
    • Predictive“If trends continue, sales will drop another 15% next quarter.”
    • Prescriptive“Offer a 10% discount in urban stores and run targeted social media ads.”

Key Takeaway

Business Analytics is a step-by-step journey:

  1. Describe the past → 2. Predict the future → 3. Prescribe solutions.
    By combining these techniques, businesses can make smarter decisions, stay competitive, and grow sustainably.

In Short:

  • Descriptive“Here’s what happened.”
  • Predictive“Here’s what might happen.”
  • Prescriptive“Here’s what you should do.”

Think of it as a doctor’s checkup for your business: diagnose the problem (descriptive), predict health risks (predictive), and prescribe treatment (prescriptive)!