127 points by janedoe_datascience 1 year ago flag hide 15 comments
ecommerce_analytics 1 year ago next
Exciting to see the progress of ML-based predictive analytics in e-commerce! I'm looking forward to the discussion.
ml_researcher 1 year ago next
Absolutely! ML-based predictive analytics can significantly improve e-commerce sales, customer experience, and inventory management.
bigdata_engineer 1 year ago next
Definitely, but let's be cautious about the pitfalls of over-engineering and privacy concerns while adopting ML solutions in our businesses.
data_scientist_sarah 1 year ago prev next
What are the key performance indicators (KPIs) that we should monitor while using ML-based predictive analytics in e-commerce?
algorithm_guru 1 year ago next
Good question! Key performance indicators can include conversion rate, customer lifetime value, returning customer rate, and inventory turnover rate.
js_developer 1 year ago next
How about cart abandonment rate? I believe it's also crucial in e-commerce sales funnels.
ecommerce_marketing_geek 1 year ago prev next
Interested to learn more about operationalizing ML-based predictive analytics into e-commerce sites. Can you share success stories of using these approaches?
startup_founder 1 year ago next
Amazon implemented recommendation systems impacting 35% of their total sales. It's a game changer for e-commerce, not only for giants, but also for small businesses.
ai_investor 1 year ago next
True! Also, Netflix saves \\$1 billion a year by using ML-based predictive analytics for their content recommendations. The possibilities are just endless.
python_poweruser 1 year ago prev next
What are the best tools and techniques to build ML-based predictive analytics for e-commerce businesses?
tensorflow_enthusiast 1 year ago next
TensorFlow and PyTorch can be used to build ML models. You may want to use platforms like BigQuery ML, XGBoost, or Spark MLlib when working with large datasets.
linux_sysadmin 1 year ago next
To productionize ML-based predictive analytics, consider using containerized systems, with tools like Docker and Kubernetes, and deploying on the cloud.
product_manager_phil 1 year ago prev next
Any advice on how to sell 'black box' ML-solutions that provide predictive analytics to teams that may be hesitant on adoption?
growth_hacker 1 year ago next
Effectively communicate the benefits and established track record of AI/ML to teams. Start with small, targeted projects and ensure they have resources for understanding AI.
quantitative_analysis_samantha 1 year ago next
Show thorough testing and real-world ROI to build trust. It also helps to create visualizations that explain how predictions and decisions are made by ML models.