Data Analytics: Segmentation and Cohort Analysis

• Data analytics involves comparing items using segmentation, cohort analysis, and A/B testing

Segmentation

• A segment is a group sharing a common characteristic like Firefox users or first-class ticket buyers
• Websites use technical and demographic info to segment visitors for comparison
• If Firefox users make fewer purchases, further testing can establish the cause
• Success can be replicated in other markets by surveying engaged users from places like Australia
• Segmentation works across industries and marketing forms, exemplified by direct mail marketers

Cohort Analysis

• Cohort analysis compares similar groups over time
• Users joining in the first week of a product launch will have a different experience than later users due to constant iteration
• Most users go through a cycle of free trial, usage, payment, and abandonment
• Users experiencing a trial in month one will have a different onboarding experience than those in month five
• Cohort analysis helps analyze customer behavior throughout their life cycle in businesses
• Without it, businesses may struggle to understand the customer life cycle over a timeframe
• Analyzing trends and patterns allows tailoring offers to identified cohorts

A/B and Multivariate Testing

• A/B testing compares one attribute, such as link color, assuming all else is equal
• A/B tests can be problematic unless there is enough web traffic to run a test on a single factor
• Websites may want to test webpage color, calls to action, and pictures
• Multivariate analysis analyzes multiple factors at once, relying on statistical analysis to see which factors strongly correlate with key metric improvements

Lean Canvas

• Lean Canvas is useful for outlining problems, solutions, customer segments, and value propositions
• It includes unfair advantages, channels, and cost and revenue streams
• You should identify real problems people know they have
• Customer segments are target markets, and understanding how to reach them is important
• Unique value propositions should be clear, distinctive, and help explain why you are better or different
• Consider solutions that solve problems in the right way
• Consider how to get your product or service to customers and recoup your investment
• Revenue streams includes where the money will come from
• Onetime or recurring models and direct or indirect transactions are revenue stream considerations
• Consider the different types of costs to be accrued in your business
• Metrics will allow you to track and understand your progress
• An unfair advantage helps distinguish and impact your efforts over your competitors

Data Analytics Tools

• Data analytics has rapidly evolved with mathematical and statistical approaches
• Data analysts use software tools to acquire, store, analyze, and report data
• Collection and analysis tools involve charts, maps, and diagrams that help collect, interpret, and present data across applications
• These tools track Key Performance Indicators (KPIs) to allow for efficient growth
• Data analytics tools enable organizations to extract insights from large data sets
• They facilitate informed decision-making through patterns, trends, and correlation identification
• Utilizing concrete evidence over intuition leads to improved operations, customer understanding, and strategic planning
• Data-driven insights lead to better outcomes
• Understanding customer data assists with identifying needs, preferences and behaviors that improve customer experience
• Market trend analysis helps identify new opportunities and adapt strategies
• Analyzing operational data optimizes processes and reduces costs
• Advanced analytics allow forecasting of future trends and potential issues
• Insight are made easier to understand through data visualization with graphs, charts, etc
• Effective data analytics provide a competitive advantage by enabling faster, data-backed decisions

Data Analytics Tools Examples

• Excel is used for basic data manipulation and visualization
• Google Analytics allows website traffic monitoring and allows user behavior insights
• Tableau has a user-friendly interface with powerful interfaces and connects to various data sources
• Power BI integrates with Microsoft, and has strong interactive visualization
• SAS is a comprehensive statistical analysis suite with advanced modeling
• R is an open-source language with a robust statistical ecosystem
• KNIME has a graphical user interface for building complex data workflows for beginners
• Python is versatile, and has extensive data science libraries (Pandas, Scikit-learn, etc.)

Important Aspects when Choosing Tools

• Ease of Use is critical for non-technical users
• Data Connectivity is key to diverse data sources
• Visualization Capabilities are important for effective data communication
• Advanced Analytics Features are useful (statistical modeling, machine learning algorithms, etc.)
• Scalability is a factor for handling larger datasets

Assignment

• Select an existing data analytics tool and write down the following;
• Brief history
• How it works
• What it offers to the client
• Who its clients are
• What it can improve for those clients