models Talks on probabilistic modelling from tech @ ThriveHive In the past month or so we've been giving talks and workshops on one of our projects to predict customer spend on different digital marketing services. Briefly, the project aimed to use
models Interpreting machine learning models: Picking low-hanging business insights, Part 3: Getting results In the previous blog posts we talked about interpretability in machine learning and how to set up your machine learning question. We also discussed a bit about the application to ThriveHive's data.
models Interpreting machine learning models: Picking low-hanging business insights, Part 2: Asking questions Interpreting machine learning models: Picking low-hanging business insights Part 2: Asking questions In the previous blog post we examined the importance of interpretability in machine learning models and how to interpret the parameters
models Interpreting machine learning models: Picking low-hanging business insights, Part 1: Linear thinking ThriveHive has been putting out a couple of exciting infographics based on some of our work here on the Data Science team. I thought it might be worthwhile to dig into the
social media What are the major social traffic drivers for ThriveHive small businesses? Small businesses (those employing less than 500 people) make up the majority of firms (>99%) and the majority of new jobs created in the last 10 years. As such, they're
news How Do ThriveHive Users’ Email Marketing Stats Compare with Industry Standards? Despite the proliferation of social media sites and connected apps, email still remains the marketing channel with the highest return on investment. According to the Direct Marketing Association, email has a median ROI