Update and rename 11_targeting_variants_for_maximum_impact.md to 11_improving_business_metrics_for_maximum_impact.md

Signed-off-by: AlxdrPolyakov <[email protected]>

Author: AlxdrPolyakov

_case_studies/11_improving_business_metrics_for_maximum_impact.md

@@ -0,0 +1,11 @@
+---
+title: Improving business metrics for maximum impact using the CausalTune library
+layout: page
+description: >-
+  This tutorial provides an introduction to improving business metrics using the ERUPT metric and the CausalTune library in Python. It shows a practical example and the use of the ERUPT metric for optimizing clickthrough rates.
+summary: >-
+  This tutorial provides an introduction to improving business metrics using the ERUPT metric and the CausalTune library in Python. It shows a practical example and the use of the ERUPT metric for optimizing clickthrough rates. It also shows how to use ERUPT to evaluate previous experiments, as well as how to evaluate the potential effect of a future experiment with different assignments using a real business example. 
+image: assets/causaltune-targeting.png
+image-alt: Targeting variants for maximum impact
+link: https://towardsdatascience.com/targeting-variants-for-maximum-impact-bdf26213d7bc
+---