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...
Source : wikipedia.org Imagine you’re a bartender in a small village in the wild west. On one dull Thursday afternoon, you’re sitting there minding your own business and a tall blond man with a poncho, cowboy hat, cigar and visible gun belt walks in. You’ve never seen...
Imagine that you want to run a marketing campaign targeting your active customers with personalized recommendations. As described in earlier blog posts on this site, you have multiple options on what recommendation strategy to adopt: talk about their favourite...
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...
