Data Science: Framing the business problems as machine-learning problems (Part I)

Solving the right problem is more important than solving a problem right away

WDIS AI-ML Series: Module 1 Lesson 1: Understanding Artificial Intelligence - What is to what could be

Artificial intelligence is all about giving machines an ability to think (and act) like a human

WDIS AI-ML Series: Module 1 Lesson 2: Introduction to AI, ML, DL, and Generative AI

Artificial intelligence is all about giving machines an ability to think (and act) like a human

Marketing Analytics: The Science and Art of Attribution Modeling (Part I) - An open problem

Discover the ever so tricky science behind the biggest unsolved challenge of Marketing

WDIS AI-ML Series: Module 1 Lesson 3: When to apply Rule Based Vs ML vs DL

Artificial intelligence is all about giving machines an ability to think (and act) like a human

WDIS AI-ML Series: Module 1 Lesson 4: How Machines (and Humans) Learn - Supervised, Unsupervised, and Reinforcement Learning

Artificial intelligence is all about giving machines an ability to think (and act) like a human

WDIS AI-ML Series: Module 1 Lesson 5: Machine Learning with Advanced Analytics - Descriptive, Predictive, and Prescriptive Analytics

Artificial intelligence is all about giving machines an ability to think (and act) like a human

WDIS AI-ML Series: Module 1 Lesson 6: Practical Exercise: Exploring real world Applications of AI/ML

Artificial intelligence is all about giving machines an ability to think (and act) like a human

WDIS AI-ML Series: Module 1 Wrap up and Quiz

Artificial intelligence is all about giving machines an ability to think (and act) like a human

WDIS AI-ML Series: Module 2 Lesson 1: Objective function - AI is nothing but an optimization problem

An objective function in the context of machine learning is a mathematical function that quantifies how well the algorithm is performing at a particular task.

WDIS AI-ML Series: Module 2 Lesson 2: End-to-end process map of Machine learning - Science or Magic

Before we go and start understanding various machine learning algorithms, let us discuss the end-to-end framework of data science deployment. We will talk about many interesting concepts and walk you through who should own which part and what should be the expected output of each part.

WDIS AI-ML Series: Module 2 Lesson 3: Business Objective and Framing of Business Problem into a Machine Learning Model

In this lesson, we will learn to do it the right way and we will also introduce the concept of PRD - Product Requirement Document, a wildly misused or unused tool that is needed to align people on a common mission.

Growth: What I learned from building growth engines for Marketplaces — An interplay of engines (Part I)

Marketplace is a dynamic interaction of demand and supply. We will discuss how to develop an efficient demand growth engine, which is an interaction of multiple engines - acquisition engines, conversion, incentive, and pricing engines.

WDIS AI-ML Series: Module 2 Lesson 4: Data Collection and Data Preprocessing

Not many companies invest enough in data as much as they do in Data Science. Albeit the realization is growing that to be seen as an ‘AI-first’ company, one needs to establish itself as a ‘Data-first’ company. The biggest challenge In this section we will give an overview of what end-to-end data processing looks like from the viewpoint of a data science project:

WDIS AI-ML Series: Module 2 Lesson 5: Feature Extraction, Feature Selection & Feature Engineering Techniques

How do we use this data that exists in the Data warehouse and/or Data Lake to build Machine Learning (ML) models? However, before we build models, we need to gain a deeper understanding of our data.

Growth: Self-Serve Sales Model (Part I) - How to use product qualified lead to enable growth

A deep dive into various aspects of self serve model such as when to use, risks and mitigation strategy, how to evaluate whether it is right for the business, how to launch, what to measure post launch, and what to do once the launch is successful.

Monetization: How to develop user Persona to enable an effective monetization strategy

Personas allow us to develop features that can best serve the users’ needs, give organizations a common language to talk about their users instead of using themselves or their friends as the user of the product

Data Science: Path to deploying machine learning models for product leaders

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.

Monetization: Aligning mission with monetization

Why firms that are working on solving real user problems fail to look at monetization, revenue, and profitability to fund their growth. I also explore ways to bring the virtuous cycle of ‘purpose driven profitability’ through monetization strategy.

A/B Testing: What to do when you do not have enough traffic on your site? (Part II)

There are a few others issues that product managers and analytics have to deal with, most important of them all is the opportunity cost of running an A/B test.

A/B Testing: What to do when you do not have enough traffic on your site? (Part I)

A/B testing and decision making using statistical tools is as much a science as an art

Leadership: ASAP (As soon as possible) — A deadline that never works

Drop the word ‘asap’ asap from your vocabulary — do not use a self-destroying deadline to express urgency.

Metrics: ‘Satisficing’ Metric — Not all metrics need to be optimized

Discover the ever so tricky science behind the biggest unsolved challenge of Marketing

Metrics: Twyman’s law - Any figure that looks interesting or different is usually wrong

The more unusual or interesting the data, the more likely they are to have been the result of an error of one kind or another

A/B Testing : How to calculate sample size before launching your test

“How long are we planning to run the test? Do we have a significance yet?”. This is not an unusual situation. In fact all product managers run into this issue.

Monetization: Price is right — Monetization strategy (Part III)

Pricing is an iterative process. Best way to get it right is to keep doing qualitative research, quantitative analysis, and roll out A/B testing to confirm your hypothesis.

Monetization: Price is right — Monetization strategy (Part II)

Pricing is an iterative process. Best way to get it right is to keep doing qualitative research, quantitative analysis, and roll out A/B testing to confirm your hypothesis.

Monetization: Price is right — Monetization strategy (Part I)

Pricing should never be done in silos. It must align with the overall company goal and strategy

Product Design: Rapid prototyping for entrepreneurs and product managers

The objective of prototyping is not to get close to the final product as much as possible but to have enough fidelity to be able to collect feedback.

Monetization: SaaS subscription pricing and packaging

Pricing is not set in stone, it is in motion — Keep iterating and identifying the changes. Run A/B test and keep incorporating the learning in the product, price, and packaging.

Growth: Reactivated users — A growth opportunity

Acquisition with habit oriented engagement and compelling retention strategy fuels true product growth.

Product Design: Behavioral psychology behind great products

Product is a dance of two souls — the user and the brand. It is your obligation to lead and to make your partner shine.

Monetization: Product pricing for startups

Getting pricing right is as important, if not more, as it is to get the product right. Pricing separates a self funded firm from an investor dependent firm. The startups that are able to get their pricing strategy at the center stage early on will end up surviving this new age of value investing

Leadership: The commitment trap

The art of decision making separates great leaders from good ones. As we rise through the ranks, it is our decisions that give us an identity, recognition, and promotions. It also means that every decision that we take as leaders has a high cost associated with it.

A/B Testing: Hypothesis Testing for Product Management (Part I)

‘By doing X (experiment detail) will help users achieve Y (What value does user get out of it — informed by your user problem) and thus will have a% impact on metric b in Z days’.

Leadership: Why ‘hypothesis’ is an excellent way to phrase ideas

Transitioning organization culture from 'I believe' to 'my hypothesis' is

Protecting Sensitive Data in the Age of Large Language Models (LLMs)

How to safeguard against leaking sensitive PII data while allowing their employees to use LLM models and other 3rd party AI tools.

Data Science: Nurturing a data fluent culture that compounds growth

Creating a data fluent culture is hard but how are some companies able to do this

Growth: What I learned from building growth engines for Marketplaces — An interplay of engines (Part II)

Marketplace efficiency is all about understanding the ‘User Intent’.No wonder best growth teams spend a considerable amount of time to understand their users and their intent. The better we understand the intent, the better will be the product that can serve the users.

Marketing Analytics: How to calculate Customer LifeTime Value (CLTV)

Discover the ever so tricky science behind the biggest unsolved challenge of Marketing

Growth: Self-Serve Sales Model (Part II) - How to use product qualified lead to enable growth

Transitioning organization culture from 'I believe' to 'my hypothesis' is

Data Science: Framing the business problems as machine-learning problems (Part II)

The real value of data science is rarely in snapshot but in motion

Data Science: Framing the business problems as machine-learning problems (Part III)

The real value of data science is rarely in snapshot but in motion

Data Science: How to increase the adoption of data science models in your company

Discover the ever so tricky science behind the biggest unsolved challenge of Marketing