Nowadays, businesses collect data at every point of the customer lifecycle. They gather and consolidate information from mobile apps, websites and social media interactions into a business analytics tool. The way businesses consume data varies depending on the industry. For instance, airlines rely on business analytics processes to determine ticket prices, while hospitals use them to schedule surgeries.
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This Article Covers:
What Is Business Analytics?
Business analytics is the process of analyzing huge datasets to unearth trends, patterns, correlations, outliers and anomalies to help organizations derive inferences and make data-driven decisions.
Components
Business analytics leverages the following components:
Data Aggregation: Aggregate raw data to provide summary statistics, including mean, minimum, maximum, sum and count.
Data Mining: Identify trends and patterns in large data sets to solve problems. Data mining employs various statistical techniques such as classification, regression and clustering for data-driven decisions.
Text Mining: Explore and analyze text data obtained from social media posts, blog comments, video or audio scripts, corporate documents, customer emails and more to uncover patterns, keywords, concepts, topics and other attributes. Offer in-demand products and services, promote experiences and analyze competitor performance through text mining.
Forecasting: Scrutinize historical data to predict future events. For instance, analyze past data to forecast retail sales during holiday seasons or energy consumption in summer. Leverage forecasting to determine budget allocation or anticipate expenses.
Data Visualization: Create intuitive visuals to find patterns and correlations in datasets. Data visualization supports exploratory data analysis, modeling and forecasting.
Optimization: Leverage simulation techniques like what-if analysis to determine the best-case scenario.
Primary Benefits
Business analytics offers the following benefits:
Make Data-driven Decisions: Business analytics allows you to make smarter decisions by analyzing the root causes of underlying issues. View manufacturing needs, marketing and sales outreach campaigns, supply chain processes and HR budgets objectively.
Weave Compelling Stories: Business analytics solutions consume large amounts of data to turn it into beautiful visuals while telling effective stories. Derive accessible insights in a few clicks and uncover innovative ideas by viewing data from different angles.
Generate What-if Scenarios: Leverage predictive analytics to create models that affect future business outcomes. Create what-if scenarios by replacing variables with different values to gauge results.
Leverage Augmented Analytics: Augmented analytics expedites data processing and analysis through advanced machine learning and artificial intelligence. Find and explore information, ask relevant questions and obtain insights.
Types
Let’s look at the business analytics types:
Descriptive Analytics
This analytics type lets you analyze historical data for a comprehensive view of business performance. Present KPIs and metrics in reports and dashboards to understand information quickly. Slice and dice data to gauge patterns, trends, outliers, anomalies and other points of interest.
Descriptive analytics helps you spot anomalies. See how sales regions stack up and why a specific product loses value over time. These anomalies can prompt diagnostic analytics to understand the root causes of problems.
Diagnostic Analytics
It examines the data to discover the reasons behind events. Determine the causes of trends and patterns by employing data discovery, mining and correlation analysis.
Find answers to questions like, ‘Why do sales peak during a particular season? Why is the clickthrough rate low during specific times?’ through diagnostic analytics.
Predictive Analytics
Leverage AI, machine learning and statistical techniques to create robust predictive models. These models assign a numerical value based on the occurrence or non-occurrence of specific events. For instance, healthcare organizations use predictive analytics to forecast patients likely to develop certain medical conditions and their progression.
Prescriptive Analytics
Analyze past data and performance to offer recommendations to handle similar scenarios. It gauges potential decisions and their impact on outcomes while suggesting an optimal course of action. For example, a loan approval engine sanctions loans based on credit score, income and profession.
Use Cases
Let’s look at some business analytics use cases:
Marketing: Which customers are likely to respond to marketing messages and emails? What was the last campaign’s ROI? It’s possible to leverage data to drive strategic marketing decisions and achieve objectives.
Human Resources: HR leaders can analyze employee performance and satisfaction, predict attrition, gauge recruitment channels and more through business analytics.
Sales: What does the sales cycle look like? Which sales techniques are most effective? Which stage of the sales funnel do users commonly abandon? Who is underperforming on the sales team?
In-depth analytics helps you answer these questions while deciphering useful insights to boost sales performance.
Finance: Perform ad hoc analysis and reporting to answer specific questions while predicting future scenarios. Derive insights from machine learning and predictive modeling to boost profitability, cash flow and business performance.
Create ad hoc reports for quick analysis. Source
Process
Let’s look at the business analytics process:
Define Business Requirements
Determine the problem and how to address it. Break the problem down into smaller goals to tackle them individually. Gather relevant data required to solve issues. Answer key questions like:
- What data is available?
- Is it sufficient?
- How can we leverage it?
Explore Information
Clean datasets by imputing missing values, eliminating outliers, removing duplicates and combining variables. Plot time series graphs to determine patterns and inconsistencies. Use scatter plots to discover correlations between variables. Summarize information with the help of appropriate visuals for clear insights.
Conduct Analysis
Leverage statistical analysis methods like regression, correlation and hypothesis testing to perform advanced analysis. Regression analysis estimates the relationship between dependent and independent variables. Hypothesis testing is a statistical inference technique that draws conclusions from sample data about the entire population.
Make Accurate Predictions
Leverage predictive analytics techniques like decision trees, neural networks and logistic regression to unearth insights and highlight relationships between variables. Compute predictive errors by comparing actual values with the predicted ones. Select the highest-performing model based on accuracy and outcomes.
Select Optimal Solutions
Run what-if scenarios to determine plausible solutions with the given constraints. Select the optimal solution based on minimum error, model coefficients and business objectives.
Measure Outcomes
Deploy, model and analyze outcomes over time. Measure favorable and unfavorable consequences to cater to business requirements.
Conclusion
It’s important to follow a robust business analytics process to discover patterns, correlations, insights and inferences for data-backed decisions. The above guide serves as a comprehensive resource to help you get started in the right direction.
What business analytics process do you follow in your organization? Let us know in the comments below!
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