Spiega documentation

Article lagged_metrics

What spiega is about

lagged metrics

In this project we want to investigate main differences between the metrics on the call and lagged metrics

difference

The lagged metrics considers a time span of 25 days when the customer could call again and turn around. The lagged metric loses around 10% customers

lagged_series

time series of on-call and lagged metrics

We have different metrics to predict

lagged_metric

metrics to predict, on call and lagged

We clean the features

lagged_boxplot

normalized feature distribution

We check the feature independency

lagged_corr

correlation between features

Effect of afiniti agent on feature distribution

lagged_overlay_on

overlay of feature distribution

Overlay distribution of features on saved customers

lagged_overlay

overlay of feature distribution

Overlay distribution of featured of saved/lost customers

lagged_corr

Overlay distribution of features: joyplot

We build a predictive model for the on-call metric

lagged_confMatrix

confusion matrix on prediction: on-call

We use the prediction of the previous model to predict the new metric

lagged_confMat

confusion matrix on prediction: lagged

We knock-out features to calculate the relative importance of that feature

lagged_knockCall

knock-out of features from trained model: on-call

lagged_knockLagged

knock-out of features from trained model: lagged

We finally check the relative difference between on-call and lagged

lagged_featDiff

relative difference between feature importance in lagged metrics

We can study the relative feature importance

lagged_featImp

feature importance (week overlay)

and see how it evolves week by week

lagged_weekImp

relative importance week by week

We study over week how the relative importance of that metric dropw over time

lagged_featImp

importance difference relative to lagged metrics

We calculate the lift depending on categorical metrics

$$ lift = SR_{on} - SR_{off}$$

lagged_featImp

lift relative to categorical metrics

cohort reshuffling

We look month per month how the association of agents per area varies over time

cohort_timeSer

time series volume changes in cohorts

Shuffling among cohorts

lagged_sankey

reshuffling of agents cohorts