Introduction
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 owner of all .ai TLD, would not complain because every time a .ai name is registered or renewed, the island collects a $50-a-year fee, which goes mostly to the government treasury (Source).
Key Takeaways
- Predicting which customers are likely to churn: Developing a model to identify customers at risk of leaving the company.
- Identifying key factors driving churn: Using ML to uncover the main reasons why customers are leaving.
- Recommending personalized retention interventions: Creating a system that suggests specific actions to retain individual customers.
- Forecasting long-term revenue impact of churn: Estimating how churn rates will affect future revenues over time.
Assess Data Feasibility
Typical questions to ask are — What exactly is the problem that we are trying to solve? Does it need to be solved at all? Why — Does solving the problem help your moat — your competitive advantage? By how much? How sustainable is the advantage? What does the ROI look like? These questions help evaluate firms focus on the most important aspect that is the essence of their existence.
In short, solving the right problem is more important than solving a problem right away.
Here is a caveat, do not keep discussing these questions — The goal is not to over-analyze it to an extent that you take your sports car out of the garage only when all the lights in the city are green. I remember the time when my previous team was planning on a major business transition and the leaders came up with 200 open questions. Unfortunately, most of these questions were so open-ended that the team spent weeks trying to get answers with no luck. While it is okay to register as many known unknowns as possible, don’t let that be the reason to keep your car in the garage.
Plan for Deployment & Beyond
Typical questions to ask are — What exactly is the problem that we are trying to solve? Does it need to be solved at all? Why — Does solving the problem help your moat — your competitive advantage? By how much? How sustainable is the advantage? What does the ROI look like? These questions help evaluate firms focus on the most important aspect that is the essence of their existence.
Assess Data Feasibility
Typical questions to ask are — What exactly is the problem that we are trying to solve? Does it need to be solved at all? Why — Does solving the problem help your moat — your competitive advantage? By how much? How sustainable is the advantage? What does the ROI look like? These questions help evaluate firms focus on the most important aspect that is the essence of their existence.
In short, solving the right problem is more important than solving a problem right away.
Here is a caveat, do not keep discussing these questions — The goal is not to over-analyze it to an extent that you take your sports car out of the garage only when all the lights in the city are green. I remember the time when my previous team was planning on a major business transition and the leaders came up with 200 open questions. Unfortunately, most of these questions were so open-ended that the team spent weeks trying to get answers with no luck. While it is okay to register as many known unknowns as possible, don’t let that be the reason to keep your car in the garage.
Conclusion
Framing ML problems with a structured, business-centric approach is critical for delivering successful outcomes. By aligning ML initiatives with strategic priorities from the onset, this framework maximizes the odds of driving meaningful organizational impact. It ensures that technical efforts are directly contributing to business goals and that resources are utilized effectively.
