Predictive Analytics Will Change Energy Pricing

  • Posted by Brian Fino

…And Make Winners Out of The Companies Who Use It

Imagine Google without the search engine. Apple without Siri’s speech recognition. Amazon without the shopping recommendations. These companies have created significant competitive advantage using the power of machine learning.

Predictive analytics for the retail energy market

Consumer retail isn’t the only industry that benefits from predictive analytics—it’s just an obvious one. But another that stands to make huge strides is the retail energy industry.

Right now, retail energy companies have the chance to leverage machine learning to dominate the energy space the way Apple has theirs by completely transforming the way they look at their business and, in the process, bring innovative, value-added, competitive products and services to market.

The time is now. Why? Because in an environment of consistently thin margins, core capabilities will drive considerable competitive advantage for the companies that move first.

And because so few are taking advantage of it.

The following steps are critical for putting predictive analytics in place—not just to optimize efforts and maximize profit, but to run more smoothly, make better, more informed decisions, and, most importantly, leave the competition in the dust.

1. First: Automate.
Many energy retailers operate their businesses with Excel spreadsheets, using them to drive price, position, and to manage overall business risk. It goes without saying that manual spreadsheet and processes are difficult to update and maintain, time-consuming, and rife with errors. Automating processes lays the groundwork for more effective, efficient analysis—and helps you become more cost efficient.

2. Next: Improve precision.
Once you’ve automated, precision is the name of the game — which is critical in a high-volume, low-margin business. This in turn fuels more informed decisions that lead to the most successful outcomes possible. Calculations and models should track every watt and every therm to understand actual business performance so that accurate, wise, and advantageous improvements can be made.

3. Then, implement machine learning.
This is where predictive analytics comes into play. Because when you can predict future load, detect anomalies in data, and classify customers based on their unique characteristics, you begin to differentiate yourself from the competition in a very big way, rather than fighting over pennies and products.  New products emerge.  New services become possible.

Think Big. Start Small.

These small steps create the opportunity for retailer to achieve some big results —resulting in improved innovation and competitive advantage. The retailers who make these moves first will create the kind of game-changing innovations that can dominate a marketplace for the years to come.

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