The Data Science Virtual Workshop, "An Experiential Journey with Data to Inspire Your Work", will make you think differently about data and how it can solve problems! This workshop includes surprising use cases that will make you think differently about data, sometimes laugh, and hopefully inspire your own work to discover actionable insights in the mounds of data available.
The use cases and introductory material will be followed by a hands-on experiential journey addressing a common challenge across industries – how to improve the customer experience. The most valuable part of this workshop is that it is designed to help you gain experience and relate it to your work – so at the end you have a plan of action on how you can make data more useful in your organization to solve a key challenge.
“Improving Customer Experiences with Real-Time Insights”, will be used as an example during the workshop. This experiential session will include a step-by-step journey based on how data science is helping companies to predict the customer experience journey and proactively address the issues, leading to the improvement of Net Promoter Score. The session will highlight the importance of interpretability in AI using SHAP ((SHapley Additive exPlanations), using AI Canvas, CRISP-DM (Cross Industry Standard Process for Data Mining) and Agile in Data Science projects. The methodology involves consuming historical NPS data; using machine learning and artificial intelligence to identify the most important features and creating an algorithm to predict the customer experience.
The methodology involves consuming historical Net Promoter Score (NPS) data; using machine learning and artificial intelligence to identify the most important features and created an algorithm to predict the customer experience.
NPS has become the industry standard customer loyalty measurement. Businesses see customer experience as an imperative and would like to run analytics on and predict customer experience. Since competition is rife, keeping customers happy so they do not move their investments elsewhere is key to maintaining profitability.
Improving the customer experience is valuable because of its effect on our bottom line. Creating an ultimate experience that appeals to both the heart and the head is our goal. Customers give their money, fans give their hearts of consumers. 44% of consumers say that majority of customer experiences are bland and 69% of consumers say that emotions count for half their experiences.
In this notebook, to predict the customer experience, we will use scikit-learn to evaluate multiple classification algorithms, and select the best peroforming algorithm based on performance metrics. In additon, we will also use SHAP to intepret the predictions.