How to perform In-Depth Analysis of Your Campaign Data?
Analyzing campaign data effectively is crucial for understanding performance and making informed decisions.
This concept utilizes root cause analysis techniques from the 'Analytics Skills for Growth' and focuses on hierarchical data arrangement for efficient analysis.
Structured Data Analysis Approach:
- ☞ Hierarchical Data Arrangement: Ensure your reporting data is organized hierarchically to facilitate quick drill-downs and detailed analysis.
- ☞ Exploratory Data Analysis (EDA): Start with an initial visual inspection of trends to identify metrics that are diverging from past trends or benchmarks. For example, examine overall revenue and Return on Ad Spend (ROAS), then delve deeper into campaign, ad set, and individual ad performance.
- ☞ Dissecting Key Metrics: If a primary metric like revenue is down, break it down into traffic, conversion rate, and average purchase value. If traffic is declining, break it down into impressions and click-through rate (CTR).
Hypothesis Generation and Validation:
- ☞ Develop hypotheses based on initial findings. For instance, if CTR is low, hypothesize reasons such as ad creative effectiveness or targeting issues.
- ☞ Validate these hypotheses through deeper data analysis, comparing with historical data, and testing alternate scenarios.
Actionable Insights and Recommendations:
- ☞ The final step is to formulate corrective action recommendations based on your validated hypotheses.
- ☞ Example: If the analysis reveals a consistent drop in CTR for one particular creative format, or for a new type of product positioning, the action could be to remedy that specific root cause.
Connecting Analysis with Optimization:
This detailed analysis process is fundamental for understanding campaign performance and guiding strategic adjustments.
In the next skilleton, we will explore remedial measures for different input metrics like CTR and conversion rate (CVR), providing a comprehensive approach to campaign optimization.