Solving the Cost of Confusion: How Research Safeguarded Customers and Boosted the Bottom Line

Quick Summary:

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  Solar became easier to afford, which boosted adoption
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  But confusion grew fast, especially around contracts and savings
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  Customers flooded support with complaints and questions
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  Scams and high-pressure sales made things worse
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  We had no system to track what deals people were actually signing
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  The situation was costing money and trust

A few years back, residential solar suddenly became a lot more affordable. New financing options like low interest loans and no money down PPAs hit the market, making solar feel like an easy win for homeowners. But as adoption rose, so did confusion. 

Customers started flooding the support center with questions about their contracts, their savings, and sometimes even claims that their electric bill should’ve disappeared entirely. At the same time, scams and pushy sales tactics were picking up. Internally we had no structured way of knowing what kinds of agreements people were signing or whether these deals were actually saving anyone money. 

The result? Rising customer frustration, more strain on our support team, and a growing sense that the situation was starting to cost us a lot of money.

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  Understand the variety of solar contracts being offered
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  Segment which types of customers were signing up
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  Capture customer perceptions of savings or losses
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  Perform a gap analysis to compare perception versus reality
  

My Approach

Data Mining (Extracted data from over 2,000 solar contracts)
Developed a data framework to collect and analyze details from 2,000 scanned PDF contracts. This included escalator clauses, PPA rates, contract lengths, interest terms, and more.
If I could identify the customer, we then collected as much relevant demographic data as possible to tie back to their contracts. 
Survey
Designed and launched a survey to identified customers via their solar contracts. The primary goal was to understand their perception of cost savings. Secondarily, we want to understand their primary source for information on solar, how long they spent researching solar systems, and how many companies they shopped around before deciding.
Bill Simulation Analysis
I wrote a custom script in SAS to recreate what each customer’s utility bill would’ve looked like without solar. It factored in rate plans, usage behavior, and even timing of solar exports to the grid. Then we compared that to what they were actually paying once you factored in the solar loan or PPA.
Linking It All Together
By combining survey results, contract data, and billing info, we created a full picture for each customer. That helped us identify patterns, flag harmful deals, and see where things were breaking down.
Key FindingsSuspicious Contracts
We found more than a few contracts that just weren’t good. Elderly customers locked into 20 year loans. PPAs priced higher than the standard utility rate. And loans with ballooning interest rates buried in the fine print.
Misinformation Was Everywhere
The majority of customers weren’t relying on trustworthy sources. Most were influenced by friends, family, or sales reps. Not objective data or vetted advisors. That explained a lot of the confusion we were seeing.
The Savings Myth
This was the big one. Most customers looked at their lower utility bill and figured they were saving. But once we added the solar contract cost, it turned out a lot of them were breaking even or worse. Only about one in five had done a full cost comparison.