As a data scientist at Spotify, if I didn't have access to user-level data or attribution IDs to directly link marketing exposure to individual users, I would need to employ alternative methods to estimate the impact of marketing. Here's an approach I would consider:
Define a Control Group: Select a representative group of Spotify users who were not exposed to the marketing campaign. Ensure that this control group is similar in characteristics (demographics, usage behavior, etc.) to the group that was exposed to the marketing campaign. The control group will serve as a baseline for comparison.
Define Metrics: Determine the key metrics that indicate the impact of the marketing campaign. These could include user engagement, such as the number of streams, playlists created, or time spent listening to music.
Time Period: Establish a pre-defined time period for the analysis, which includes both the duration of the marketing campaign and a comparable period before the campaign.
Collect Data: Gather data on the selected metrics for both the exposed group (users who were potentially exposed to the marketing campaign) and the control group (users who were not exposed). Ensure that the data collection methodology is consistent across both groups.
Analyze the Difference: Compare the aggregated metrics of the exposed group and the control group during the defined time period. If there is a noticeable difference between the two groups, it could suggest the impact of the marketing campaign.
Consider External Factors: Take into account any external factors that may have influenced the observed differences. For example, if the marketing campaign coincided with a major event or seasonal trend that could have affected user behavior, it is important to consider these factors in the analysis.
Statistical Testing: Perform statistical tests to evaluate the significance of the observed differences between the exposed and control groups. This will help determine if the differences are statistically significant or if they could be due to random chance.
Sensitivity Analysis: Conduct sensitivity analysis to assess the robustness of the results. Vary the parameters or assumptions of the analysis to determine if the conclusions hold under different scenarios.
It's important to note that this approach provides an estimation rather than definitive attribution. Without user-level data or attribution IDs, it's challenging to directly attribute the impact to the marketing campaign. However, by carefully selecting a control group and conducting rigorous analysis, it is possible to gain insights into the overall impact of the campaign on user engagement.
If we do not have the information on which specific users were exposed to the marketing campaign, it becomes difficult to directly measure the impact of the campaign on user behavior. However, there are a few possible methods to estimate the overall impact of the campaign:
Controlled Experiment: One approach could be to conduct a controlled experiment where we randomly divide the user base into two groups - one exposed to the marketing campaign and the other not exposed. We can then compare the behavior of the two groups to estimate the impact of the campaign. However, this approach requires us to have control over the exposure of users to the campaign.
Time-series analysis: Another approach could be to use time-series analysis to study the patterns of user behavior before and after the marketing campaign. We can identify trends and seasonal patterns in user behavior and then observe if there was any deviation from those patterns after the campaign. However, this approach assumes that the user behavior patterns are stable and can be predicted.
Correlation analysis: Another approach could be to analyze the correlation between marketing metrics such as ad spend or reach, and user behavior metrics such as user engagement or retention. However, this approach assumes that the correlation between marketing metrics and user behavior is strong and not influenced by other factors.
In summary, without the ability to attribute IDs to users exposed to a marketing campaign, it becomes challenging to directly measure the impact of the campaign on user behavior. However, there are still some possible methods to estimate the overall impact of the campaign.