Introduction #
The AdSpyder features provide users with valuable insights into historical ads and their relevant details. One of these features is the cohort map, which displays data based on specific day-hour combinations. However, there may be instances where this data does not display correctly. In this response, we will discuss potential causes for issues with the day-hour cohort map and offer suggestions for correcting them.
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Pointwise Information #
- Data Inconsistencies: The day-hour cohort map might not display data accurately due to inconsistencies in the underlying data source or its formatting.
- Platform Limitations: Certain advertising platforms may have limitations on the granularity of data available, which could impact the accuracy of the day-hour cohort map.
- Filtering Errors: Incorrect filter settings while using the day-hour cohort map can result in misrepresented or missing data.
- Data Collection and Processing Delays: A delay between when data is collected, processed, and displayed could cause discrepancies in the data shown on the day-hour cohort map.
- Incorrect Time Zone Settings: Any inconsistencies in time zone settings between AdSpyder and the advertising platforms can lead to incorrect data representation on the day-hour cohort map.
In-Depth Content #
1. Data Consistency: Ensure that the data used for generating the day-hour cohort map is consistent across all platforms. Check for any formatting issues, missing values, or incorrect metadata that could impact the accuracy of the displayed data. You can use tools like Excel or Google Sheets to clean and transform your data before importing it into AdSpyder.
2. Platform Limitations: Be aware of potential limitations when working with different advertising platforms. For instance, some platforms may not support granular time frames, leading to broader cohorts that could impact the accuracy of your day-hour analysis. In such cases, consider using aggregated data instead or adjusting your analysis methodology to accommodate the platform’s limitations.
3. Filter Settings: Double-check filter settings when using the day-hour cohort map. Ensure that you have selected the correct date range and other filters relevant to your analysis. Additionally, verify that the ‘Day’ and ‘Hour’ filters are set correctly based on your time zone settings.
4. Data Collection and Processing Delays: Monitor data collection and processing timelines for any potential delays. Ensure that data is being collected consistently across all platforms. Adjust your analysis methodology to account for any known processing delays, if necessary.
5. Time Zone Settings: Confirm that time zone settings in AdSpyder match those of the advertising platforms. Incorrect time zone settings can lead to inconsistencies in the data displayed on the day-hour cohort map. Update your time zone settings accordingly or adjust your analysis methodology to account for any differences.
Conclusion and Call to Action #
By addressing these potential causes, you can improve the accuracy of the day-hour cohort map in AdSpyder and gain more valuable insights from historical ad data. Regularly check for data consistency, platform limitations, filter settings, processing delays, and time zone settings to ensure the most accurate representation of your ad performance over time.
