--Customer churn prediction in the telecommunications sector represents a critical business intelligence task that has evolved from subjective human assessment to sophisticated algorithmic approaches. In this work, we present a comprehensive framework for telecommunications churn prediction leveraging deep neural networks. Through systematic problem formulation, rigorous dataset analysis, and careful feature engineering, we develop a model that captures complex patterns in customer behavior indicative of potential churn. We conduct extensive empirical evaluations across multiple performance metrics, demonstrating that our proposed neural architecture achieves significant improvements over existing baseline methods. Our approach not only advances the state-of-the-art in churn prediction accuracy but also provides interpretable insights into the key factors driving customer attrition in telecommunications services.

This article is part of a series where we walk step by step in solving fintech problems with Machine Learning using "All lending club loan data". In previous articles, we prepared a dataset and built a Logistic Regression model, and we discussed the most common "ML model evaluation metrics" for a classification problem in the fintech space. This article will try to "understand" how our model decision works and what packages can help us to answer this question. Machine learning models are frequently named "black boxes". They produce highly accurate predictions.

In a previous blog, we explored why advanced machine learning matters for personalization overall. But, how does advanced machine learning impact 1-to-1 personalization? Because Machine Learning has become a widely used and highly evolved technology, advanced Machine Learning has become increasingly important to personalization software. In this post, we'll take a deeper look at why. All Machine Learning (ML) takes inputs from its environment and uses them to improve its performance when executing a given task.

We apply causal machine learning algorithms to assess the causal effect of a marketing intervention, namely a coupon campaign, on the sales of a retailer. Besides assessing the average impacts of different types of coupons, we also investigate the heterogeneity of causal effects across different subgroups of customers, e.g., between clients with relatively high vs. low prior purchases. Finally, we use optimal policy learning to determine (in a data-driven way) which customer groups should be targeted by the coupon campaign in order to maximize the marketing intervention's effectiveness in terms of sales. We find that only two out of the five coupon categories examined, namely coupons applicable to the product categories of drugstore items and other food, have a statistically significant positive effect on retailer sales. The assessment of group average treatment effects reveals substantial differences in the impact of coupon provision across customer groups, particularly across customer groups as defined by prior purchases at the store, with drugstore coupons being particularly effective among customers with high prior purchases and other food coupons among customers with low prior purchases. Our study provides a use case for the application of causal machine learning in business analytics to evaluate the causal impact of specific firm policies (like marketing campaigns) for decision support.

Clustering is an unsupervised algorithm that is used for determining the intrinsic groups present in unlabelled data. For instance, a B2C business might be interested in finding segments in its customer base. Clustering is hence used commonly for different use-cases like customer segmentation, market segmentation, pattern recognition, search result clustering etc. Some standard clustering techniques are K-means, DBSCAN, Hierarchical clustering amongst other methods. Clusters created using techniques like Kmeans are often not easy to decipher because it is difficult to determine why a particular row of data is classified in a particular bucket.

Projects have always been thought of as measurable improvements resulting from a result produced, which serve as the icing on the cake for achieving personal or corporate goals. Talking about individual projects, have you found it challenging to learn at home? Many of us are in the same boat -- there are far too many things to handle during these trying times, and learning has taken a back seat, contrary to our expectations. So, what are our options for getting back on track? How can we apply what we have learned about data science in the real world? Picking an open-source data science project and sticking with it is extremely beneficial.

Advertising, particularly online advertising, isn't a surefire way to bolster business. A report from ecommerce analytics platform Glew drives the point home: In 2015, 75% of retailers that spent at least $5,000 on Facebook ads ended up losing money on those ads, with the average return on investment landing around -66.7%. A 2018 survey of marketers by Rakuten Marketing found that companies waste an estimated 26% of their budgets on inefficient ad channels and strategies. Jaleh Rezaei, the CEO of Mutiny, believes that the problem doesn't lie with the ads themselves. Rather, she pegs it on static, templated websites that don't match the personalization delivered by ads.

The world is cautiously optimistic about using artificial intelligence (AI) from customer data due to privacy and AI-bias concerns. At the same time, AI is proven to boost sales for businesses. It saves the time it would get to get customer insights manually, can be used to tailor better customer experiences, and can work faster than any human could at finding anomalies in online behavior data. Businesses expect AI to impact their businesses. Esteemed industry analysts Gartner reports that AI is growing exponentially, with spending on Artificial Intelligence in eCommerce set to reach $7.3 billion per year by 2022- that's up by over 20%.

Artificial intelligence (AI) is ubiquitous and set to be a significant driver of the world's economic activity in the next decade. It's a constellation of many technologies working in tandem to enable machines to sense, comprehend, act and learn with human-like levels of intelligence. Tools like machine learning (e.g., your credit card company sends a text about potentially fraudulent activity) and natural language processing (e.g., your phone helps you with the next likely word in a sentence) are part of the AI landscape. They'll continue to affect everything we do as we collect more data and enhance algorithms for better decision-making. As in other industries, AI will transform every layer of self-storage operation, too, including customer service, tenant access, security, finance, sales, marketing and revenue management (RM).

Machine learning can help companies identify completely new metrics in a rapidly changing market. It is well known that machine learning is already helping companies achieve their performance goals by optimizing existing performance metrics. By leveraging the growing volume of data on customer behavior, pricing, competitive action, and operational statistics, it can deliver critical insights in a variety of ways. Machine learning offers many benefits from optimizing marketing or pricing to improving customer service and operational efficiency. However, a recent article in the MIT Sloan Management Review shows that companies are increasingly using machine learning to identify entirely new KPIs to correlate with overall performance.