Data has repeatedly been tagged as the oil of the 21st century and rightly so. With so much advancement in computing, storage, network, analytics tools, the amount of data that gets generated has grown exponentially. …
In the previous articles, we discussed when to use machine learning to solve a business problem and how to frame the business problems as a machine learning problem.
Now that we have developed a model, are we done? No. framing a business problem into a machine learning problem and solving…
In the previous articles, we discussed when to use machine learning to solve a business problem and how to frame the business problems as a machine learning problem.
The brain map of where we are going is this: Machine learning is about developing a Function/Algorithm (F) that uses a set…
The commoditization of machine learning has happened at an unprecedented rate so much so that it is difficult to find a company — big or small, David or Goliath, that is not exploring Machine learning as a means to stay relevant in this rapidly changing business environment.
In the previous article here, we started with the introduction of how the marketplace is a play of engines, we discussed the two primary engines — The acquisition engine and the conversion engine.
In this article, we will introduce the other two engines of the growth engine — The pricing…
Marketplace, as we all know, is a dynamic interaction of demand and supply. In this article, we will discuss how to develop an efficient demand growth engine — which is an interaction of multiple engines such as acquisition engines, conversion, incentive, and pricing engines.
Many times, when we talk about…
When I started managing a team, I would often say, “I believe that adding this feature X would increase conversion by 10%” in my team meetings. …
Developing a phenomenal product that delivers value to users and helps capture value for organizations requires a deep understanding of users. In come ‘User personas’ — a representation of target segment/users. Personas allow us to develop features that can best serve the users’ needs, give organizations a common language to…
If you are a product leader then you must be wondering what are the various steps to deploy a machine learning model. This article is a quick handbook to guide you through the steps needed to achieve the goal. …
Why monetization gets neglected at startups
A thought experiment that started at Business school continued post graduation, when I interviewed product managers at startups to understand what percentage of time their teams spent thinking about monetization. The answer invariably was ‘less than 5%’. In most cases, monetization was an after-thought.
The pendulum swung from one end…
Vinay Roy
https://www.linkedin.com/in/royvinay — SVP, Product & Data Science @ Vistaglobal | MBA, UC Berkeley
