Different Strokes for Different Folks: The Unique Nature of Business Problems
One size never fits all. Even you may be surprised how.
Throughout my career, I've conducted numerous churn analyses, each one unique in its own way. Recently, a potential customer approached me with a question about churn, which made me realize how often we use single labels to describe vastly different problems.
Different Strokes for Different Folks: The Unique Nature of Business Problems
In the business world, we're quick to slap labels on problems. Think of familiar terms like customer acquisition, employee satisfaction, overbooking, and understaffing. Sure, these labels help us think in models and find solutions. But here's the kicker: each term is like an iceberg, hiding a multitude of unique problem types below the surface, each demanding its own special approach.
Take customer churn, for instance. It sounds straightforward, right? But once you start peeling back the layers, you realize it's a whole different ball game. And remember, we're only using churn as an example here. You can apply these lessons to pretty much any business problem.
Understanding Churn
So, what's churn all about? In a nutshell, churn happens when customers break up with a business and start seeing others. It seems simple enough on the surface, but let's dive a bit deeper into why it's actually anything but.
To get a handle on churn, businesses usually create models. These churn models fall into three main categories: general, segment-based, and personalized. Each one has its unique quirks that make it more or less suited to certain situations.
General Models
These models predict churn across a broad population. They include the actuarial approach, which focuses on overall customer retention, and risk factors, which identify patterns that signal a higher likelihood of churn. The drawback? They don't tackle individual cases directly.
Segment-Based Models
These models split customers into groups based on similarities and look for patterns within each group to predict churn. By spotting commonalities among customer groups, these models help businesses tailor their approach to different populations. They can be actionable or non-actionable, depending on various factors.
Personalized Models
Personalized models predict churn by understanding each customer's unique quirks. Like segment-based models, they can be actionable or non-actionable, depending on how much control the business has over the factors at play.
Wrapping Up
The takeaway here is that business problems demand custom solutions. Every challenge has its unique aspects, so it's important to resist generalizing and embrace the complexity. By doing so, we can devise creative, tailored solutions to address the specific circumstances. So, the next time you face a problem like customer churn, remember that it's not as simple as it seems. Keep an open mind, and you'll be better equipped to tackle the challenge head-on.
Feel free to reach out to me for assistance in determining the optimal data strategy for your individual business challenge.