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.
Contents
1. BUSINESS CHALLENGE
2. TECHNICAL APPROACH
2.1. Information sources
2.1.1. Company information
2.1.2. Internal company data
2.2. Artificial Intelligence
2.2.1. Machine Learning techniques
2.2.2. Deep Learning techniques
2.3. Tested architectures
2.4. Final architecture
3. CONCLUSION
3.1. Objectives revision and functional requirements
3.2. Future guidelines
Research Objectives and Core Themes
The primary objective of this bachelor's thesis is to develop a predictive model that identifies sales opportunities within the B2B sector that have a high propensity to be lost, enabling sales personnel to intervene proactively. The research explores the integration of internal CRM data with external company information sourced via social network APIs to improve forecasting accuracy and support revenue retention.
- Development of a predictive customer retention model for the B2B telecommunications sector.
- Implementation of data enrichment strategies using public social network APIs to overcome internal data gaps.
- Empirical evaluation of various machine learning and deep learning architectures, including ensembles.
- Design of a Graphical User Interface (GUI) to streamline the application of predictive analytics in a business environment.
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Introduction
Within the past few years, the term Machine Learning has swept over the world. According to Arthur Samuel [1], the computer scientist who brought up the term in the ’50s, machine learning is a subfield of computer science, which with the use of large data sets and training algorithms, aims to ”give computers the ability to learn without being explicitly programmed”.
If one would search how the popularity of the term Machine Learning has evolved in the past few years, for example in Google Trends[2], there would be no doubt that the searches for the term have skyrocketed. So much so, that through a recent survey [3] conducted by PwC, 30% of business leaders forecasted AI to be the biggest disruption to their industries within the next five years starting 2017. Two years later, in the present day, this Machine Learning bubble slowly begins to mature as a recent Crunchbase study suggests[4], thus the once startup-based funding becomes a more corporate one. This shift, in turn, means that bigger companies with more resources are becoming more aware of the capabilities of this once visionary field of artificial intelligence and are now able to implement it on their daily challenges.
One of the most important performance indicators of any business is its revenue, which is the basis a company is rated to their shareholders and investors. Therefore, it is in every company’s best interest to maximize sales and to accurately forecast it’s highs and lows and try to prevent the latter. This approach begs the question if any subfield of artificial intelligence can be used to help shed light into the future of a company’s sales forecast and therefore help predict its revenue more accurately.
Summary of Chapters
1. BUSINESS CHALLENGE: Discusses the growing importance of Artificial Intelligence in business, the shift toward B2B forecasting, and the role of CRM systems in tracking sales opportunities.
2. TECHNICAL APPROACH: Details the sourcing of external and internal data, the exploratory analysis performed on variables, and the comparative study of Machine Learning and Deep Learning architectures.
3. CONCLUSION: Reviews the project's success in achieving high recall in predicting lost opportunities and outlines recommendations for future implementation and data usage.
Key Terms
Artificial Intelligence, Machine Learning, Deep Learning, Sales Funnel, Customer Retention, Churn Prevention, B2B, Predictive Analytics, Data Enrichment, LinkedIn API, Ensemble Models, Neural Networks, Feature Engineering, CRM, Forecasting.
Frequently Asked Questions
What is the core purpose of this research?
The study aims to create a predictive system for a telecommunications company to identify B2B sales opportunities at high risk of failure, allowing sales teams to react quickly and reduce churn.
Which sectors and companies are targeted by this model?
The research focuses on a large company in the telecommunications sector, specifically investigating the B2B sales funnel in the Argentinian market.
What is the primary research goal?
The goal is to maximize recall in order to capture the highest possible number of failing opportunities, thereby enabling effective preventive sales actions.
Which specific methodologies were applied?
The author employed a combination of traditional machine learning algorithms (e.g., Random Forest, Gradient Boosting) and deep learning techniques (e.g., MLP, LSTM) within an ensemble framework.
What is the main focus of the internal data analysis?
The analysis covers the sales funnel states and various contract-related variables, cleaning the data for noise and preparing it for predictive modeling.
How is the model evaluated?
The performance is evaluated primarily using recall and precision metrics, with a specific emphasis on the F1-score to balance the trade-off between identifying failures and avoiding false alarms.
How does the project handle data protection?
The project utilizes publicly available, GDPR-compliant data from LinkedIn to enrich the internal company information, ensuring that commercial use is legally permissible.
What role does the GUI play in the research?
The custom-built Python-based GUI simplifies the complex data gathering and predictive execution processes, making the developed tools accessible and user-friendly for business applications.
- Arbeit zitieren
- Herr Juan Ruiz de Bustillo Ohngemach (Autor:in), 2019, Predicting sales funnel with a customer-relationship-management tool, München, GRIN Verlag, https://www.grin.com/document/503216