Confirmation bias in data analysis is the tendency for individuals to interpret data in ways that confirm their preexisting beliefs or hypotheses while disregarding or downplaying contradictory evidence. This...
Webinar: Automated Recommender Systems with Megaladata
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Date and Time: 3/3/2025 7:00 p.m. (Mexico City)
Meeting Format: Online (via Google Meet)
Meeting Format: Online (via Google Meet)
Overview: Discover how automated recommender systems can revolutionise customer experiences and drive business profitability. In this webinar, we’ll explore two key approaches for creating recommendation models using Megaladata:
- Recommendations in Digital Marketing: Learn how personalised recommendation engines can boost conversion rates, enhance customer engagement, and refine marketing strategies through the power of AI models and data analysis.
- Recommendations in Unit Economics for Online Stores: Explore how to optimise pricing, manage stock levels, and implement demand-driven sales strategies. Gain valuable insights into understanding customer behaviour and maximising profits in a digital marketplace.
What You’ll Learn:
- How data-driven recommender systems function
- Implementation of digital marketing and price optimisation models in Megaladata
- Real-world use cases and practical examples in e-commerce and CRM
- The advantages of personalization and automation using low-code AI platforms
Speakers:
Daniel Núñez, Data Scientist at Neural Factory
Samvel Arustamov, Project Manager at Megaladata
Language: English
Register now to discover the best route to personalization and profitability with AI and Megaladata!
See also
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How to Predict Sales Using the ARIMAX Algorithm
In time series analysis, Autoregressive Integrated Moving Average (ARIMA) models are used for short-term forecasting based on past values. In this article, we will analyze the ARIMA model built in...
About Megaladata
Megaladata is a low code platform for advanced analytics
A solution for a wide range of business problems that require processing large volumes of data, implementing complex logic, and applying machine learning methods.
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