Machine-Learning

Solar Industry: Embracing Data Analytics and Machine Learning to Lower Costs

An Introduction to Data Analytics and Machine Learning

We are currently living in an era of data. As businesses today understand the importance of incorporating a more methodical, analytics-driven approach in decision-making, data is fast becoming their lifeblood. Having data insights gives an organization a competitive edge, helps cut down on costs and drives business growth. While most companies collect massive amounts of data, they seldom know how to effectively process it to yield insights. The primary problem is that as data stores expand and grow, becoming layered and complex entities known as ‘Big Data’, the data sets become too complex to be dealt with in a  traditional manner. This is where data analytics and machine learning comes into the picture.

Data Analytics

Making sense of Big Data is what Data Analytics is all about. It involves multi-faceted quantitative and qualitative approaches that eventually yield valuable insights. Data analytics is an umbrella term for a set of processes such as data extraction, categorization, pattern analysis, and insights generation. Today, organizations across the globe are embracing a data-driven approach. However, it is not a simple task and requires various tools and techniques to collect, transform, cleanse, classify and convert data into consumable insights.

Machine Learning

In a time when Artificial Intelligence (AI) is really enabling businesses to do better, Machine Learning is a term that is often used synonymously with AI. However, although the two have a lot in common, they are not the same. While AI refers to simulated intelligence in machines, Machine Learning is an analytical technique that helps us parse data with the help of algorithms that pick up patterns in the content. Simply put, Machine Learning algorithms allow a machine to train itself on a data set so that over time, tasks can be performed more efficiently.

Data Analytics and Machine Learning – Giving Solar Businesses an Edge

By now you may be wondering how data analytics and machine learning can help solar business. Well, turns out they are extremely helpful in understanding who the potential solar customer is, how best to leverage solar technology and cut down on overall costs. The thing is, businesses are always looking to expand and grow. This can, however, be quite a challenge what with the presence of intense competition and with customers becoming more and more demanding. Especially in the solar industry, competition is rife; having to establish a differentiating factor is being more and more complex for companies. Additionally, existing operations have become quite complex and costly. Hence, with the intention to streamline operations, cut down on costs and gain a competitive edge, solar organizations today are embracing data analytics and machine learning. Data analytics are not just an aid for business growth but also an important way to revolutionize the way organizations function internally. The insights gained are strategic and actionable in nature, aiding better business decisions. As per research by Statista, the value of the software segment of data analytics and other big data services will increase to $46 billion by 2027.

Let us focus on the primary challenge that the solar industry is currently facing, which is cost. Now, while solar energy is eco-friendly and a great way to transition the world from generating a large carbon footprint, consumers’ primary concern is the substantial installation cost. While solar is cost-effective in the long-term, unlike fossil fuels and other sources of energy the preliminary installation of a solar device is a considerable investment. Another challenge solar companies face is the lack of urgency in adopting solar. Many a customer may show interest in embracing a solar option, however, customers usually tend to get complacent and postpone investing in solar immediately. This makes the sales process tedious and extremely costly. What does this mean? Well, it is important for solar companies to study important parameters such as weather data involving historical wind patterns, sun-hours, solar radiation data sets, etc. to know how to maximize the output from a solar asset. But these analyses cannot be done manually. In order to overcome these challenges and ensure that operational efficiency is achieved, solar companies have turned to machine learning and data analytics.

Identifying ‘Valuable’ Customers

While solar companies around the world are seeing a steady year-on-year growth in business, effective sales still proves to be the industry’s Achilles heel. Most companies are still selling solar options door-to-door or via mass advertising, a move that is akin to shooting in the dark. As per an example stated by Fortune, in some markets, such as California, there are a lot of early adopters who already have solar. But since the solar companies do not know this, they tend to spend precious time trying to make sales to people who do not need another solar option.  According to Marc Guy, the CEO of Faze 1, the need for data analytics in solar sales is of particular importance as solar systems have a “unique selling proposition and typically represent a one-time sale”. With the help of data analytics, companies can gain knowledge on potential customers with an existing interest in solar, looking for a viable option or the ones who are currently dealing with high utility bills. This insight gives sales teams a considerable lead to make highly focused and targeted sales pitches. Additionally, machine learning helps provide consumer patterns and demographics that can predict with a certain amount of certainty, the likelihood that someone will purchase solar. These tools help companies cut down on ‘wasteful’ sales efforts, in turn reducing costs. Even adopting a software sales solution such as SunPro+ helps reduce customer acquisition costs up to 90 percent.

In addition to analyzing data to attract future customers, it is also important to study what drove existing solar users to the option. By studying the customer lifecycle of solar adopters with the help of analytics, companies are able to identify customer types or groups that are most likely to invest in solar.

Monitoring and Maintenance of solar assets

While it is easy to think that once a solar asset has been installed, most of the work is done, it is not the case. The output that solar assets generate is variable in nature. Much of it is dependent on weather conditions, topography, and other natural phenomena. And it is quite obvious that unpredictable weather leads to unpredictable energy generation. So what can companies do to optimize their solar operations? Well, most solar companies are leveraging data and predictive analytics to study weather patterns and understand how changes to ecology can have an impact on solar power generation. Smarter monitoring and maintenance of solar assets helps lower costs by optimizing solar production.

In addition to this, analytical tools can be used to generate performance reports of solar assets to evaluate if they are performing to their full potential. Machine learning enabled solar assets provide ‘failure patterns’ that can help asset owners formulate their future buying decisions. Most comprehensive data analytics and ML platforms today offer solar companies performance reports and benchmarks and help drive inventory and personnel management decisions. Monitoring the performance of employees & independent sales agents and identifying their strengths & weaknesses helps better planning of resources. It enhances the productivity of the sales team and helps manage solar leads efficiently. Hence, thanks to data analytics and ML, solar companies can now optimize their operations without having to endure additional infrastructure costs.

Increase ROI

As discussed earlier, data analysis helps companies understand their customer needs better. While this leads to changes in how a company projects its product, it is also important to know whether or not these changes are yielding an impact. Probably the most important measure of impact in the business scenario is Return on Investment (ROI). The insights gained from data analytics must generate recurrent value, revenue, and business opportunities in order to be considered useful. And data analytics solutions, when used effectively, do just that. By providing companies with valuable insights, analytics help companies reduce their time to business value. This in turn helps generate better ROI.

In Conclusion

While most solar companies look at data analytics and machine learning as tools to boost sales and gain an advantage over their competitors, it goes beyond just that. Data analysis is a medium through which companies can achieve business efficiency. Although we have analytical technology today that makes data analysis an effective governing factor in what route your business takes, at the end of the day, what matters most is setting your business goals, asking the right questions and acting on the insights from the analysis to streamline operations and cut down on overall costs.

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