Business Intelligence Analysis

1. Overview

Business intelligence analysis requires data skills, business knowledge, and intuition. It explains past performance like below:

• What happened?
• What did it happen?
• How many units did we sell?
• In which region did we sell the most goods?

2. Description

2.1 Observation

Observe sales volume, new customers, and etc.

2.2 Quantification

The process of representing observations as numbers

2.3 Measures

The accumulation of observations to show some information. It is related to simple descriptive statistics of past performance

2.4 Metrics

A metric refers to a value that derives from the measures you obtain and aims at gauging business performance or progress to compare. for instance, you can track the average quarterly revenue per new customer every three months and thus compare customer retention and spending every quarter. It has a business application

METRIC = MEASURE + BUSINESS MEANING

2.5 Key Performance Indicators

KPIs = metrics + business objectives = key Performance Indicators

Key: Related to your main business goals

Performance: How successfully you have performed within a specified timeframe

Indicators: Generated only from users who have clicked on a link provided in your ad campaign

2.6 Visualization

• BI Report
• BI Dashboard

3. Applications

3.1 Price Optimization

Extracting the relevant information in real-time and compare it with historical. It allows you to adjust your strategy to pas data as soon as it is available.

3.2 Inventory Management

Effective inventory management means supplying enough stock to meet demand with a minimal amount of waste and cost to do this well. You can perform an in-depth analysis of past sales transactions for the purpose of identifying seasonality patterns and the times of the year with the highest sales. Additionally, you could track your inventory to identify the months of which you have over or understocked a detailed analysis can even pinpoint the day or time of day where the need for good is highest.

4. Reference

https://365datascience.com/