by Ian Thompson, Executive VP, Global Solutions Consultancy
Automated pricing processes can feel like a risk. How will you know if your prices are optimized? Can you really depend on a software solution to boost profit? And how will you feel giving up some control over pricing tactics?
By Ethan Walker, Solutions Consultant
Utter the word "automation" in a room full of people with diverse careers and histories. Some will shudder. Some will ignore. Some will start to get excited at the possibilities. Utter it in a room full of best-in-class fuel pricing analysts, and they're liable to start clapping. While automation is sometimes perceived as threatening, scary or "too difficult," it is absolutely essential from an overall efficiency standpoint. And at the end of the day, it equates to increased revenue, which analysts and executives alike happen to enjoy.
Our high-performance race car is almost complete. We have pulled together a powerful turbo engine, great telemetry, winning powertrain, fantastic crew and the right spoilers and wings. In relation to our convenience store, we are set up for success with great facilities, the right data and analysis of the competition, excellent operations, the right merchandise and world-class pricing.
In Part 1, I introduced the analogy of the convenience retailer as a finely tuned performance car in a challenging race for volume. We explored pricing as the turbo charger, best used in short bursts for growing margin and exploiting daily opportunities, but not the best way to maintain or grow market share. We also looked at how dropping prices across the board to increase volume often results in price wars and minimal volume growth at reduced margins.
You don't always have control over your promotional items, and that means you don't always have a plan to understand their effects on sitewide sales. Your gut instinct on what works and what doesn't is a good start, but is it giving you all the strategic support you need? Access to critical, integrated data can raise your game to the next level.
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.
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.
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.
By Anila Siraj, EVP of Research and Applied Data Sciences
The concept of artificial intelligence has gained increasing importance in our daily conversations over the last two decades or so, but it actually existed long before. Forbes begins the timeline of AI at 1308, when "Catalan poet and theologian Ramon Llull publishe(d) Ars generalis ultima (The Ultimate General Art), further perfecting his method of using paper-based mechanical means to create new knowledge from combinations of concepts."
