Carlos Omar Pardo Gomez (cop2108@columbia.edu) for Kantar Millward Brown (Mexico)
The intellectual property of this code is entirely owned by Kantar Millward Brown (Mexico) and cannot be exploited for any commercial purpose without their consent. Here my first draft is presented only for educational reasons.
You can try this Shiny app here!
Shiny app, useful to monitor the actual trend of a brand's KPIs, when they are estimated from survey data.
Features include:
- Filters by demographics
- Latest change versus last month, last quarter and last year
- Brand Health Map, to visualize KPIs' level and momentum in one plot
- Time series' noise filter
- Automatic structural changes finder
- ARIMA forecast
- Credible intervals and uncertainty measure
First of all, there is an option to select the brand which is going to be monitored.
Then, the fun begins...
This section allows nontechnical decision-makers to have a complete overview of the brand's situation, with regard to a different period.
Comparisson can be done versus the last month, quarter or year. Also, results can be decomposed by demographics!
This table shows if each one of the KPIs is growing, decreasing or with not enough information to think it is doing one of both, using statistically significant differences.
This section allows to deepen into each one of the KPI trends and understand more how the brand is behaving.
All the analysis in this section can be done filtering by demographics!
This tab allows the user to visualize the difference's distribution versus a previous period, for every KPI. This shows how close was the metric from the significant difference threshold, and how much uncertainty there is about the real value.
It shows the time series plot, adding some interesting elements:
- Real value's confidence interval, taking into account the sampling error (gray shadow)
- Structural changes found in the series (vertical lines)
- ARIMA model to forecast the next observations (yellow area)
- Noise filter by level -kalman filter- or momentum -linear regression for each stable period- (blue line)
Scatter plot with the last level (within the metric's historic data, which quantile represents the last observation) and momentum (how big is the current brand's growing or diminishing).
This grants the modeler to have a robust perspective of the brand's health and quickly identify which metrics should raise some attention.
