Using Machine Learning to Plan Wine Pricing

How do you responsibly plan your business strategy while awaiting a critic's score for your newest wine? Start with data.

12/9/20234 min read

Navigating the wine industry as a small winery, especially for a winemaker in my home near the Lake Michigan Shore, comes with daily decisions that really can come down to life or death for the business. Possibly near the top of the list: Pricing. And setting the right price for that Riesling you've just sent off for a critic review? Given the difference between an 80 and 98 could mean double the price, you might as well drink a bottle first and make a random guess. But, go too high, and you may be waving goodbye to potential customers. Too low, and you're practically giving your hard work away, not to mention straining your already tight cash flow. Of course, there's more to it than this simplified scenario – but it highlights a point. Ideal market pricing isn't just a nice-to-have, it's the lifeblood of your business's sustainability. And let's face it, that Excel sheet with competitor prices? It's gathering virtual dust on your desktop.

Enter Data Analysis

First, I encourage you to test out the model here:

The interface at the link above is backed by a Python program running a regression model to predict the market price for a bottle of wine. By feeding the model details like grape variety, region, and important critic scores, it provides a starting point for pricing, backed by data, not guesswork. This model can also handle the inherent complexity of wine pricing variables (thanks Screaming Eagle…), thanks to its method of building multiple decision trees and then blending their predictions for a more accurate, less biased result.

And guess what? We live in a world where AI tools such as ChatGPT can code these models for you.

How It Works

Let's look at a specific example of how a model like this can impact an actual winery, let’s say in the Lake Michigan Shore AVA. Now imagine our Michigan winemaker stumbles across a vast dataset of over 150,000 points, encompassing real past wine prices, regions, varieties, and critic scores. That's a goldmine of information that can be fairly overwhelming for any human to process. But for a regression model, in this case, our random forest model, it can be analyzed it seconds.

From a slightly more scientific perspective: the model operates by performing a regression analysis on the extensive dataset. This process involves identifying relationships and patterns within the data – which includes price, region, variety, and critic scores – and applying these findings to predict a specific price for a bottle of wine. Essentially, it's a just a thorough statistical analysis, but at a scale and speed unattainable by humans. The model's algorithms analyze correlations and variations across thousands of data points, thereby deriving a calculated, data-driven price strategy for, say, a particular bottle of Riesling, specifically from the Lake Michigan Shore. This ensures that the pricing suggestion is rooted in a comprehensive understanding of the actual wine market captured by the data set.

What makes this approach particularly exciting is how it tailors these insights to our winery's unique profile. A Lake Michigan Shore Riesling isn't just any wine. It has its own story, its own flavor profile, and, importantly, its own very unique market niche. The model takes into account not just the broad strokes of the wine industry but also the specifics – like how Rieslings generally perform by region and how past similar wines have been priced according to critic scores.

Navigating Market Complexity with a Data-Informed Lens

While the model we've embraced here can be a powerful tool, it's important to recognize its role in the broader context of market dynamics. It’s not a crystal ball, but rather a compass – guiding, but not dictating our pricing strategy. The wine market has constant shifting of consumer preferences, emerging competitors, and fluctuating economic factors, requiring more than a “one software solution” to pricing.

This model, or any to be honest, is best understood as a starting point for deeper business conversations. It provides a data-informed foundation upon which we can build our discussions around branding, market positioning, and growth strategy. For instance, the model might suggest a certain price of $38 based on hundreds of thousands of historical data points. While it can tempting to trust data unquestioningly due to the near propaganda-level advertising of data and software companies, we need to overlay this $38 with our understanding of current market movements, potential inflation, changes in consumer spending power, and yes, even a bit of your gut feeling as a 40-year winemaker.

This approach acknowledges the reality of any data model – it's only as good as the decisions it later informs. But by using the model as a baseline, we can engage in more nuanced and strategic discussions about our winery's future. It prompts us to consider how a Lake Michigan Shore Riesling may fit into the broader narrative of our brand, how we want to position ourselves in the market, and how we can adapt to external factors like economic shifts or changes in consumer taste.

In essence, the model is not the end of our pricing journey; it's a key part of an ongoing dialogue. It helps us cut through the noise and complexity of the market, providing clarity and direction. But the final decisions – those that consider the soul of our brand and our aspirations as a winery – those are uniquely human and extend well beyond what any algorithm can offer.

Embracing the Data-Driven Revolution

In the end, for small wineries, embracing the power of data analysis and machine learning is not just about keeping up with the times and AI hype; it's about ensuring that bottle of wine that took over a year of love and work (it tastes better too…) maintains its rightful place in a market full of mass produced brands backed by budget for analysts and expensive software. A simple regression model that you, the winemaker, create yourself can represent a new era in winemaking – one where data-driven decisions actually help our business strategies, ensuring that passion for winemaking is matched by a modern approach to the market.