Funnel analysis

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It is used to analyze the flow of users through a process, canada phone number list such as the purchase process on an e-commerce website or the registration process on a mobile application.
Results : Identify the process steps with the highest abandonment rates and understand the causes of this abandonment. These analyses reveal potential optimizations and conversion boosters.
Dimensions and Metrics : Metrics that measure the progress of users through the funnel are used, such as the number of users who start the process, the number of users who complete each step, and the number of users who finish the process.
Example : Analyze the purchase funnel of an e-commerce website to identify the steps with the highest abandonment rate and optimize the purchase process to reduce abandonment and increase sales.
6. Comparison of areas
It is used to compare the performance of different areas of the business, such as different product lines, different customer segments, or different geographic regions.
Results : Helps identify areas with better and worse performance and understand the causes of the differences.
Dimensions and Metrics : Key business metrics (sessions, users, conversions, revenue, etc.) are used, and values ​​across different areas are compared. Differences and similarities between areas are analyzed, and explanations for variations in performance are sought.
Example : Compare the performance of two product lines to identify which is more profitable and understand the reasons for the differences in profitability. We can also compare the performance of different customer segments to identify the most valuable segments.
Other comparative analyses of macro metrics
Analysis of the evolution of macro metrics.
Comparison with previous periods.
Competition analysis.
Descriptive Trend Analysis (DTA)
7. Seasonality analysis
It allows you to identify seasonal and temporal patterns in your data. This is especially relevant for businesses affected by seasonal factors. It helps (a lot) to understand natural changes in our data versus those caused by business actions.
Results : Helps to understand how seasonal factors influence business performance.
Dimensions and Metrics : Metrics such as traffic, sales, conversions, etc. are used, and trends are analyzed over time, paying attention to patterns that repeat cyclically. Year over Year, Month over Month.
Example : Analyze the monthly sales of a swimwear website to identify the months of highest and lowest demand and adjust both forecasts and conclusions when these increase (as is logical) shortly before summer and fall drastically at the end of it.
8. Detection of stable patterns
It's used to find regular patterns in the evolution of metrics. It allows you to understand long-term business behavior. These patterns no longer relate to seasonality, but rather to the evolution of your business or sector.
Results : Helps to understand the long-term evolution of the business and detect possible changes in trends.
Dimensions and metrics : Metrics such as traffic, sales, social media engagement, etc. are used, and trends are analyzed over time, looking for patterns that repeat regularly.
Example : Analyze website traffic trends over the past few years to identify whether there's a consistent growth pattern or if traffic fluctuates erratically for some reason. Another example: Identify slow growth in brand searches and apply this to forecasts and predictions while we try to address the issue.