ML & Predictive Analytics
Practical Guide to Building an Accurate Customer Lifetime Value Prediction
Have you ever wondered how valuable a customer really is to your business—not just today but over the entire course of their relationship with you? That’s where customer Lifetime Value comes in. It’s a way to predict the total revenue a customer will bring to your business throughout their entire journey with your brand. Now,…
Read MoreSentiment Analysis in Python: Libraries, Models & Examples
How can you turn raw feedback into actionable insights? In a world flooded with tweets, reviews, and comments, understanding how people feel about your brand, product, or service is no longer optional. Your audience expects to be heard and understood. Sentiment analysis helps you meet this expectation by decoding emotions, identifying trends, and enabling data-backed…
Read MoreHow to Apply Machine Learning for Customer Segmentation
Customer segmentation is a big deal and challenge for marketing teams to personalize messaging, improve customer satisfaction, and optimize product offerings. This guide takes a detailed approach to building a customer segmentation model using machine learning and Python. Read on to get practical recommendations from our Data Scientists for each step to avoid common pitfalls.…
Read MoreUnderstanding Types of Machine Learning Models
Machine learning uses programmatic algorithms to process user queries and generate an output. This output can be predicted labels (spam, not spam), clusters (customer segmentation), or a sequence of actions. However, different machine learning algorithms help achieve various types of outputs. For example, supervised models generate predictions, and reinforcement models create a sequence of actions. …
Read MoreKey Steps to a Successful Machine Learning Project
Mastering the ML project lifecycle is essential for leveraging the full potential of machine learning and bringing innovation through it. Careful implementation of each step guides towards impactful results. Let’s explore the importance of the ML lifecycle and how each step contributes to the project’s success. The Importance of Understanding the Machine Learning Lifecycle The…
Read MoreWhy Statistics for Machine Learning Matters
All modern predictive models involving machine learning rely on statistical algorithms. Statistical techniques capture patterns within data which form the basis of data modeling and predictive analysis. Some of the basic patterns include mean, variance, and standard deviations. These statistical measures capture data distribution and allow machine-learning models to forecast unseen values. Let’s explore why…
Read MoreUnderstanding AI, Machine Learning, Data Science, and Deep Learning
AI, machine learning, data science, deep learning, we tend to use them interchangeably. While there are overlaps between these fields, they do have their own distinct characteristics and applications. In this post, we clarify these concepts providing a clear understanding of their roles, interconnections, and the distinct skill sets and tools required. Let’s dive in!…
Read MoreB2B Predictive Churn Analytics: Benefits, Models & Tools
Is there anything more frustrating than the potential of losing a customer? Perhaps they want to cancel their account because they aren’t using the tool enough to justify the costs. Maybe the key user has left the company and no one else has been trained to use it. Or it may be that the pricing…
Read More4 Ways Machine Learning Can Level Up Your Agency’s Client Reporting
As your marketing agency battles to win (and keep) clients in today’s competitive environment, finding a way to maintain an edge over your competition is a constant fight. One way to sharpen your agency’s value proposition is by leveling-up in an area that most agencies choose to put little effort into: client reporting. However, creating…
Read MoreWhy Demand Forecasting Is Important in Supply Chain
The COVID-19 pandemic was unanticipated and severely disrupted logistics worldwide. According to statistics, the global supply chain pressure indexar increased from 0.01 in December 2019 to 3.15 in April 2020. While few people could have predicted the chaos the pandemic would cause, the impact led several to start incorporating demand forecasting in the supply chain.…
Read MoreHow Machine Learning Can Improve Demand Forecasting
Machine learning has seen one of the most rapid growths, its market value increasing from $1.03 billion in 2016 to $15.44 billion in 2021. This innovation has also outperformed expert predictions and gained a strong foothold in the commercial industry. The reason is that businesses have benefited from its applications, succeeding at enhancing demand forecasting…
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