The Mistaken Obsession with Model Accuracy

In a nutshell:

• The obsession with model accuracy can be a distraction from what really matters for your business.
• Focus on the concept of "lift" to measure how much better your predictive model performs compared to your current methods.
• Use real-world examples like next best offer predictions to understand the impact of predictive modeling on business outcomes.
• Address common concerns about accuracy and interpretability of complex models.
• Ultimately, prioritize models that lift your business performance and drive real results.

Our CEO and co-founder, Zohar Bronfman, explains this mistaken obsession — or read on for more!

Have you ever wondered if your predictive models are truly accurate? More importantly, does their accuracy actually matter for your business?

In this post, I'll dive into the often misunderstood world of predictive model accuracy and reveal why the numbers might not mean what you think they do.

The Accuracy Obsession: A Dangerous Distraction

When it comes to predictive models, there's an unhealthy obsession in the market with accuracy metrics. You've probably heard terms like precision, recall, area under the curve (AUC), F1 score, and other statistical jargon thrown around.

Data scientists and machine learning engineers often get caught up in optimizing these metrics, spending countless hours tweaking models to squeeze out an extra percentage point of accuracy.

But here's the thing: these metrics, while important from a technical standpoint, don't tell the whole story. They're often calculated on carefully curated datasets that may not reflect real-world conditions. Moreover, they can be misleading when dealing with imbalanced datasets, which are common in many business scenarios.

The truth is that accuracy only matters in the context of your business. It's not about achieving a perfect score on some arbitrary scale; it's about how well the model performs compared to your current methods.

This is where the concept of "lift" comes into play, and it's a game-changer for understanding model performance.

Lift: The Metric That Really Matters

So, what exactly is lift? Simply put, lift measures how much better your predictive model performs compared to your existing logic or methods. If a model has a 3x lift, it means it's three times more effective than your current approach. Now that's something worth getting excited about!

Think about it this way: Would you rather have a model that scores 95% on some accuracy test but only marginally improves your business outcomes, or a model with 80% accuracy that triples your results?

The answer is clear, and that's why lift is the metric you should focus on.

Calculating lift is straightforward:

1. Measure the performance of your current method (e.g., conversion rate, revenue generated)
2. Measure the performance of your new predictive model
3. Divide the model's performance by your current method's performance

For example, if your current email campaign has a 2% conversion rate, and a new model-driven campaign achieves a 6% conversion rate, the lift is 6% / 2% = 3x.

Putting It Into Practice: The Next Best Offer Example

Let's bring this down to earth with a real-world example: next best offer (NBO) predictions for your customers. Imagine you currently use a simple rule: if a customer hasn't made a purchase in two weeks, you offer them your most popular product that they haven't bought yet. This approach achieves a certain level of success, but how do you know if it's the best you can do?

Enter predictive modeling. Instead of using a one-size-fits-all approach, a model can recommend specific products for individual customers based on their browsing history, past purchases, demographic information, and even external data like seasonal trends or social media activity.

But here's the kicker: the accuracy of these individual predictions isn't what's important. What matters is how many more purchases you get using the model compared to your original method. By testing the model's recommendations against your current logic, you can see the real impact on your business.

If the model significantly outperforms your existing approach – congratulations! You've found a more accurate model for your specific use case.

Let's break down the process:

1. Establish a baseline: Track the performance of your current NBO strategy over a set period (e.g., one month).
2. Implement the predictive model: Run it alongside your current strategy for a similar period.
3. Compare results: Look at key metrics like conversion rate, average order value, and total revenue generated.
4. Calculate lift: Divide the model's performance by your baseline performance for each metric.

For instance, if your current NBO strategy generates $100,000 in monthly revenue and the predictive model generates $250,000, you've achieved a 2.5x revenue lift.

Addressing Common Concerns

At this point, you might be thinking, "But doesn't overall accuracy still matter?" It does, to an extent. A model with very low accuracy is unlikely to provide significant lift.

However, the relationship between accuracy and lift isn't always linear. A model with 70% accuracy could potentially provide more lift than one with 90% accuracy if it's better at identifying the most valuable opportunities.

Another concern might be the interpretability of complex models. While it's true that some highly accurate models (like deep neural networks) can be "black boxes," there are techniques to make them more interpretable.

Techniques like SHAP (SHapley Additive exPlanations) values can help you understand which features are driving your model's predictions, providing valuable insights into customer behavior.

The Ultimate Obsession: Models' Business Impact

Remember, when it comes to predictive models, it's not about chasing perfect accuracy scores. It's about finding models that lift your business performance and drive real results.

The next time someone brags about their model's accuracy, ask them about the lift – that's where the true value lies.

By focusing on lift, you'll be able to:

• Make data-driven decisions that directly impact your bottom line
• Allocate resources more effectively to high-impact predictive modeling projects
• Communicate the value of your models to non-technical stakeholders more easily

Ready to see how predictive modeling can lift your business performance? Don't let outdated metrics hold you back. Get in touch with us today and discover the power of business-focused predictive analytics. It's time to stop obsessing over abstract accuracy and start driving real results!