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...
The problem of personalizing customer engagement can be broken down into three broad problems: Recording how customers engage with the brand: the most obvious aspect of this is a transaction, but non-transactional signals such as web/app behaviour, service-related...
The first question obviously is, can LLMs work as product recommenders themselves? The short answer is: not really, no. Recommender systems develop an understanding of customer engagement with products (purchases, cart/wish list addition, browsing) and use it to...
In today’s competitive landscape, personalisation has become the watchword in marketing. In order to treat each customer as a unique individual with their own preferences and interests, we must learn from individual customers’ behaviour rather than...
Marketing is an umbrella term, encompassing an array of smaller activities. Some of these require sheer creativity, while others demand diligence and a knack for making sense of extensive data. The focus of our discussion will be on the latter and how the integration...
