Last summer, we zipped into summer lovin' with our Grease-themed review of summer fuel prices. This year, we're sticking with the theme. And this time around, prices are like greased lightning: full speed ahead. Check out the Energy Information Administration’s (EIA) Short-Term Energy and Summer Fuels Outlook for more, and read on for our analysis.
CREATING A FUELS PRICING CENTER OF EXCELLENCE
DEEP LEARNING VS. MACHINE LEARNING WHEN DETERMINING RETAIL FUEL PRICES
Artificial Intelligence (AI) - it's the technological focus of today and the science fiction focus of the past several decades, and has become key to the way many businesses now operate. With the rise of machine learning and a greater understanding of how it relates to AI, deep learning, too, has become more and more prevalent. In this post, we'll explore the differences between deep learning and machine learning — and the applications of each to retail fuel pricing.
A CLOSER LOOK AT ART VS. SCIENCE IN RETAIL FUEL PRICING AND LOCATION PLANNING
HOW TO ACHIEVE PRICING PROCESS MASTERY
MANAGING VOLATILITY USING SCIENCE, AUTOMATION AND EXPERIENCE
THE PSYCHOLOGICAL BENEFITS OF FUEL PRICING AUTOMATION
WHICH PEOPLE MAKE THE BEST FUEL PRICE ANALYSTS?
7 FACTORS THAT INFLUENCE FUEL PRICE CHANGES
To the average consumer, spikes and dips in fuel prices feel personal — like negative consequences and little rewards. The average consumer is unlikely to assess the oil price upstream or know what other factors determine whether or not the change at their favorite station is justified. Instead, they'll pay the price established and move along with their days. As a retailer, you do not have the same option. You need to understand which factors influence change in fuel prices so you can make better pricing decisions based on your pricing power and position.
HOW CAN RETAILERS LEVERAGE PRICING POWER?
PART 3: HOW MUCH SHOULD YOU RELY ON ARTIFICIAL INTELLIGENCE IN FUEL PRICING?
by Anila Siraj, EVP of Research and Applied Data Sciences
As we mentioned in part two of this series, blindly relying on computational processes and models for pricing, without consideration for your market needs and dynamics, can be dangerous. While your market progresses through the phases of maturity, you must maintain your commitment to keep pace with those phases via your pricing strategy — without sacrificing success due to impatience.
PART 2: HOW ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FIT INTO YOUR FUEL PRICING STRATEGY
By Anila Siraj, EVP of Research and Applied Data Sciences
As machine learning becomes more accessible for fuel retail teams, pricing strategies and processes will inevitably evolve. But caution is still necessary; you should not blindly entrust your entire strategy to a computer simply because you have access to better data and the means to automate pricing decisions via this intelligence.