Predicting sales funnel with a customer-relationship-management tool (eBook)

Customer acquisition and retention

Juan Ruiz de Bustillo Ohngemach (Autor)

eBook Download: PDF

2019 | 1. Auflage
GRIN Verlag
978-3-346-06943-6 (ISBN)

Infos

Bachelor Thesis from the year 2019 in the subject Computer Sciences - Artificial Intelligence, grade: 1,3, University of Pompeu Fabra, language: English, abstract: In this work the author attempts to examine a small part of artificial intelligence, producing a real-life approximation of what could be a predictive system based on sales funnel information provided by a customer-relationship-management tool like Salesforce. The work focusses on two main aspects, namely the prediction of the sales funnel and a LinkedIn-based enrichment tool which sources company data in bulk to enrich existing sales information. Along the lines of trying to fulfil these two goals, the thesis is comprised of the four typical elements of an end-to-end advanced analytics project: identification of needed data and it’s sourcing, exploratory analysis of said data, analytical model selection and design, validation and testing of the obtained results obtained in the previous step.

Artificial Intelligence has matured over the past few years to now become a standard in corporate market and business analyses. Those analyses focus mainly on customer acquisition and retention as they drive the revenue. This work attempts to create customer retention, for example a churn prevention model to help accurately predict the opportunities that have a high propensity to be lost, help the salesperson to identify them, and be able to quickly react.

Produkt-Details

• Erscheint lt. Verlag: 22.11.2019
• Verlagsort: München
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Netzwerke
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Schlagworte: AI • Artificial Intelligence • Big Data • Churn • Churn Prevention • Deep learning • funnel • Future • LinkedIn • machine learning • Sales • Salesforce • Technology
• ISBN-10: 3-346-06943-5 / 3346069435
• ISBN-13: 978-3-346-06943-6 / 9783346069436