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 typically seen on a spectrum of intelligence starting from Narrow intelligence (ANI), where the Machine can perform one task extremely well such as language translation, image recognition, or playing chess but it fails to generalize to other tasks. This is where we are today - ANI or Artificial Narrow Intelligence.

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

A broad field of computer science that is focused on creating thinking systems that can perform tasks that typically require human intelligence. The range of such tasks could be reasoning, problem-solving, perception, learning, language understanding, and decision-making.

NeutoAI CoMarketer - Marketing Meets AI with Adaptive Content Optimization (ACO) System

NeutoAI CoMarketer introduces Adaptive Content Optimization (ACO), seamlessly merging AI and marketing for dynamic, personalized campaigns.

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

I am sure when you complete the series, you will start looking at every problem through the lens of Machine Learning. This is what many companies or tech teams are doing. They are applying Machine learning for the sake of it. Some wise person rightly said, “When you have a hammer in your hand everything around you will appear as a nail”.

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

If we say that AI is about enabling a machine to think (and act) like a human, then a question that needs to be answered is how do we determine that machine has actually started thinking like a human. The answer to this question was first answered with the introduction of the concept Turing Test.

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

we discussed how Machines learn i.e. Supervised, Unsupervised, and Reinforcement Learning. More and more companies are building products leveraging these learning techniques. But companies are not only leveraging data to teach machines but also leveraging data to learn more about the past, and the future, and make business recommendations. This is what is called Advanced Analytics.

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

We have reached a point in our journey where we can start playing with some key applications of AI to learn why suddenly AI has gained an inflection point. We discussed some key applications of AI in Lesson 1.

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

Now before we jump onto the next section, let us check our understanding of Module 1. Take the Module 1 Quiz by clicking on the button on the right side.You will see your scores and right answers upon completion of the quiz.

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

It is an initial phase of any data science project, is a critical step in the data analysis process, used to understand the underlying structure, patterns, and relationships within a dataset before formal modeling or hypothesis testing. It's like detective work, where you delve into your data to understand its characteristics, identify patterns, and uncover potential insights.

WDIS AI-ML Series: Module 2 Lesson 6: Model Selection and Evaluation Metrics

In most practical applications, data scientists often have a set of ML models that can be applied to solve a problem. Data scientists run a set of ML models and see which ones perform the best. This is called Racing ML models against each other to choose a winner.

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: The ASAP (As soon as possible) Antipattern — The Hidden Costs of ASAP Deadlines

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)

Monetization strategy should be an enabler of growth by maximizing the value captured and using that to create more value for existing users or to create value for new users.

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: Testing Ideas, Not Egos - How Phrasing Ideas as Hypotheses Fosters Collaboration and Innovation

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.

Generative Engine Optimization (GEO) for Modern B2B Visibility: An Actionable Guide

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

The self-serve model has its own risk. So before thinking of implementing that, it is important to evaluate the risk and its mitigation strategy.

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

However, as I started interfacing with the team members of the data science team, one thing that came up repeatedly was that despite shipping some ‘good’ machine learning models, the adoption of those models among business stakeholder teams was almost zero. As per the team, there was just no enthusiasm for any machine learning models. In the last year alone, the team developed a propensity model, booking projection curve, demand forecasting, etc.