In the Overview section, you can view marketing performance reports and conduct a preliminary analysis based on historical data you have uploaded or integrated. Understanding these reports is crucial for gaining actionable insights into your marketing performance.
Versions
The accuracy of the model is assessed using a subset of historical data, with performance evaluated through the Adjusted R-squared and Normalized Root Mean Squared Error (NRMSE) metrics.
These statistics provide a robust measure of model fit and predictive accuracy, ensuring that the model accounts for variations within the data and predicts outcomes effectively.
To facilitate informed decision-making, the top-performing models from over 3,000 iterations are displayed on a user interface. This allows brand marketers, data scientists, and other stakeholders to review and select the most appropriate model based on their specific expertise and contextual needs.
The interface provides a transparent and accessible means for stakeholders to leverage their insights in choosing the optimal model for strategic applications.
Platform spend vs. KPI
The graph presents your marketing spending from all platforms in clustered columns and overlays the KPI as a line.
Platform Engagement
The table displays ad spends on various platforms along with the corresponding impressions and/or clicks.
Model Input
The chart displays all input data, including data from integrations, in a clear, date-aligned tabular format. Review this chart to monitor data trends and evaluate the integration inputs over specific periods.
Correlation Matrix
A correlation matrix is a square matrix chart showing the correlation coefficients between two variables. The matrix shows how all the possible pairs of values in a table are related to each other. This chart helps in summarizing a large data set and finding and showing patterns in the data.
Each of your input variables is listed in both the rows and the columns and the correlation coefficient between each pair of variables is written in each cell. The correlation coefficient ranges from -1 to +1, where -1 means a perfect negative correlation, +1 means a perfect positive correlation, and 0 means there is no correlation between the variables.
By clicking on a specific cell, you can visualize the correlation scatter plot of the two variables.
How to interpret a Scatter Diagram?
While interpreting a scatter diagram, the given below points should be taken into consideration:
Dense or Scattered Points: If the plotted points are close to each other, then you can expect a high degree of correlation between the two variables. However, if the plotted points are widely scattered, then you can expect a poor correlation between the variables.
Trend or No Trend: If the points plotted on the scatter diagram show any trend either upward or downward, then it can be said that the variables are correlated. However, if the plotted points do not show any trend, then it can be said that the variables are uncorrelated.
Upward or Downward Trend: If the plotted points show an upward trend rising from the lower left-hand corner of the graph and going upward to the upper right-hand corner, then the correlation is positive. It means that the two variables move in the same direction. However, if the plotted points show a downward trend from the upper left-hand corner of the graph to the lower right-hand corner, then the correlation is negative. It means that the two variables move in the opposite direction.
Perfect Correlation: If the points plotted on the scatter diagram lie on a straight line and have a positive slope, then it can be said that the correlation is perfect and positive. However, if the points plotted lie on a straight line and have a negative slope, then it can be said that the correlation is perfect and negative.
If most of the points are dense and show a trend, a few outliers may lead to a lower correlation number, but they are well correlated.
If you have any further queries, please write to us at [email protected] and we'll get back to you at the earliest.