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. The best part of data is the idea of compounding largely due to the data network effect.

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 the problem is only a part. The last mile is to convey the solution to the business users.

If you have been involving them at the discovery stage then the task is much simpler. But if not then the chances of the project failing despite what the data suggested is…

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 of scenarios (S) to help achieve an objective function (L) and in turn help improve the business metric (M).

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 an attempt to stay relevant, firms have started putting the ‘machine learning’ keyword on their product page, in their investment deck, and also in their domain name — no wonder ‘.ai’ domain name sells so expensive — I know that because I thought of purchasing one for my lemonade stall. Anguilla, the…

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.

Blueprint of demand growth engine — shows the interaction between acquisition engine, conversion engine, incentive engine, and the pricing engine

In this article, we will introduce the other two engines of the growth engine — The pricing engine and the incentive engine. …

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 strategy, we assume that saying ‘increase conversion by 10%’ is a strategy. It is not. That is a goal and I am a big believer in not just setting a goal but setting up a system — a perpetual, self-reinforcing system that fuels growth. …

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. In one such meeting, my mentor, who had offered to help me with my leadership style highlighted how right phrasing is extremely important to fostering a culture of collaboration, brainstorming, and ideation.

She asked, “What happens when you start a sentence with I believe …”. I was confused. I had no clue what she meant. I thought that by saying ‘I believe…’ I was putting my ideas across confidently so that others could feel…

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. …

Data Science Process (Source: Author)

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. The diagram above will help you reference each of the stages visually.

Business Objective: It all starts here, understanding the problem space that you are trying to solve. I can’t emphasize the importance of clearly outlining the business objective, the business metric that you are trying to solve. Discuss various hypotheses that led to the problem statement and state Goals and…

Why monetization gets neglected at startups

Photo by Austin Distel on Unsplash

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 of the spectrum where firms lived by the philosophy that ‘Maximizing return on investment was the sole purpose of a firm’, leading to the rise of corporations that left scandal, pollution, and poverty in their wake, to the other end of the spectrum where startups, built on mission, started putting…

Vinay Roy — VP, Product, Growth & Pricing strategy @ — MBA, UC Berkeley

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