We were creating demo images for a dummy retail website we wanted to create for a Solus demo instance. So, when we were getting images of electronic equipment, this popped up: So, obviously, we leaned into it and decided to add a monitor to the product assortment. We...
This quote has been variously attributed to Yogi Berra, Neils Bohr, Mark Twain and others. Nevertheless, it contains a nugget of wisdom about predictive models, especially those dealing with social systems rather than physical ones: people change, the underlying...
In an earlier blog post, we spoke about typical predictive models that are built in a CRM setting: the propensity of a customer to transact (either unconditionally, or of a specific type or in a specific product category), churn, repeat a transaction etc. In this pot,...
Bias propagation, and the tug-of-war between popular and personal Yeah, yeah, we know that’s not the original quote. So why are we deliberately misquoting it? Imagine you’re a marketing executive tasked with running customer outreach campaigns. One year, during the...
Deploying predictive models, and why the modelling algorithm is perhaps the easiest aspect of the problem. Predictive models, specifically propensity models, are a staple of data science practice across organizations and verticals. Be it to understand whether a...
In our previous post, we spoke about how various recommender algorithms work, and why the nature of the data suggests what might work for whom. But having a good list of personalized recommendations is only half the battle. So what’s the other half? Imagine picking a...
