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Machine learning has been around for more than 70 years and has come a long way since the late 1950s when Frank Rosenblatt paved the way with his design for the first artificial neural network that implemented patterns and shapes recognition. And while machine learning hasn’t been around quite as long in the oil and gas industry, its applicability and use is growing. We know for certain that price volatility in this industry continues to be a challenge, and this is where machine learning models can help. In the early 2000s, when things got tough for the Oakland A’s, Billy Beane turned to machine learning to change the game of baseball. Early machine learning methods were used to analyze on-base and slugging percentages to identify anomalies, uncovering players with potential that traditional scouting methods would have overlooked. Similarly, with machine learning, pricing teams can gain insights and react faster than traditional methods. Let’s look at how it works. The Science Behind Machine Learning: Patterns and Predictions As a former pricing manager, I know firsthand how margin leakage and volume performance can keep someone up at night and how easy it is to get tangled up in the data. Years of pricing history is typically captured in spreadsheets and databases, with pricing managers often lacking the time and tools to effectively mine and model the data. Together with constant market volatility and competitive pressures, it becomes increasingly harder to spend time optimizing pricing strategies. When using historical knowledge and data to predict the future, the human mind can only absorb and process so much. Machine learning allows for speed and scale by augmenting the intimate knowledge that typically resides with only a few people. It can be the vehicle to uncover margin opportunities, and given the scale of volumes in our business, seemingly small gains in unit margin can result in significant performance improvement. Like how Billy Beane used machine learning models to uncover small performance differentiators for players that added up a winning team, machine learning models are helping pricing managers improve sales performance and arrive at the optimal price. Using Pricing Tools to Arrive at the Optimal Price Pricing teams can use the predictive power of machine learning algorithms, taking action today based on analysis of what happened in the past. Without it, they rely primarily on organizational memory or their team’s knowledge about what happened in previous periods…

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