You’re probably aware of how challenging it can be to manage customer satisfaction when operating in dynamic and risk-prone environments. Today, I want to introduce you to AiDash‘s Climate Risk Intelligence System (CRIS), an AI in energy management solution that has been successful in addressing this issue with both precision and efficiency.
Pro-tip: The solution I’m sharing today is designed to be executed in the natural environment, but that doesn’t detract from both the elegance of the solution and the adaptability of this approach for execution in diverse environments. (I share about how to adapt use cases across verticals in my upcoming book The Data & AI Imperative – if you want to get in on the upcoming launch goodies, you can do so here.)
The risk-prone environment that Avista operates within is that of natural storm and weather events.
As a professional engineer of the environmental discipline myself, I can tell you first-hand how impossible it can be to predict anything from a vast, dynamic and complex web of overlapping satellite imagery… but AiDash has achieved this by successfully harnessing satellite-powered AI within their enterprise solution.
As a professional engineer of the environmental discipline myself, I can tell you first-hand how impossible it can be to predict anything from a vast, dynamic and complex web of overlapping satellite imagery… but AiDash has achieved this by successfully harnessing satellite-powered AI within their enterprise solution to optimize operations, mitigate business pain points, and deliver rapid ROI for their customers.
The Need To Safeguard Customer Satisfaction In Utilities And Beyond
It doesn’t matter what industry you’re in or what you’re selling, safeguarding customer satisfaction is of paramount importance. Here’s why:
- Increased customer loyalty – In general, customers appreciate transparency and efforts to keep them informed, which builds trust and long-term relationships. When customers have a better overall experience, this leads to higher satisfaction rates, positive reviews, and word-of-mouth referrals.
- Improved brand reputation – From the customer’s perspective, a company is reliable and customer-centric when it responds effectively and quickly to customer issues. Doing so improves brand reputation, and makes your company more attractive to potential customers.
- Increased customer retention – Higher customer satisfaction leads to increased retention rates. Since it’s generally more cost-effective to retain existing customers than it is to acquire new ones, this leads to improved profitability.
- Higher sales and revenue – Satisfied customers are more likely to purchase additional products or services, while also recommending the company to others. This of course drives higher sales and revenue growth.
There’s also a high cost to businesses that neglect customer satisfaction. Likely harms include:
- Customer churn – Failure to inform customers and quickly address issues leads to frustration and dissatisfaction, thereby causing customers to switch to competitors (just ask Adobe!)
- Negative brand reputation – Poor customer satisfaction invariably results in negative reviews and bad word-of-mouth. This damages brand reputation and makes it harder to attract and retain customers.
- Lost revenue opportunities – Dissatisfied customers are less likely to make repeat purchases or recommend the company. This results in lost sales and revenue opportunities.
- Loss of trust – Consistent failure to meet customer expectations erodes trust. Once lost, trust is difficult, if not impossible, to rebuild. Left unabated, this will adversely impact your long-term business relationships.
Now that we’re clear on why customer satisfaction is SO important to your company’s bottom line, let’s look at how AiDash addresses this issue for its clients.
AiDash’s AI in energy management solution, CRIS, leverages 20 years of historical weather data to build predictive models that forecast storm impacts with over 80% accuracy. This allows utility companies like Avista to prepare proactively for severe weather events, in order to ensure that resources are allocated efficiently and power restoration times are minimized.
The system improves communications with customers by providing advanced warnings about potential outages, which in turn improves overall customer satisfaction in the event of the inevitable.
Common Failures When Deploying AI in Energy Management
AiDash has engineered their AI in energy management system to address many mistakes that their competitors fall into, like:
- Security & compliance shortcomings: Competitors often overlook robust security measures and compliance standards, but not so with AiDash.
- Reliance on static data and learning: Many systems rely on static data models, while AiDash implements continuous learning to improve prediction accuracy over time.
- Inflexible deployment options: Competitors frequently offer limited deployment options, whereas AiDash provides flexible solutions, including cloud and hybrid deployments.
A Brief Technical Overview of AiDash CRIS
AiDash’s Climate Risk Intelligence System (CRIS) provides a robust and proactive approach to managing severe weather events and improving customer communication. The AI in energy management system has enabled Avista Utilities to significantly enhance their response to storms, ultimately improving customer satisfaction and operational efficiency.
- Data integration and predictive models:
- Utilizes 20 years of historical weather data combined with real-time inputs.
- Employs advanced machine learning algorithms to create predictive models with over 80% accuracy.
- Proactive communication and resource allocation:
- Forecasts storm impacts 72, 48, 24, 12, and 6 hours in advance.
- Enables early warnings and precise planning for resource deployment and incident management.
- Continuous learning and improvement:
- Continuously updates predictive models based on new data and feedback from customers.
- Ensures ongoing accuracy and adapts to changing weather patterns and operational needs.
By leveraging these capabilities, AiDash’s CRIS has enabled Avista Utilities to significantly improve their storm response and customer communication, leading to higher customer satisfaction and improved operational efficiency.
Where was AiDash back when I was designing water utilities systems in storm-prone Central Florida? Our customers would have paid an arm and a leg for results like these. Even back in 2012, I was contemplating how we could deploy ML to get advanced intel from satellite data — but the R&D was a long way off.
Given the avalanche of AI startups nowadays, It’s really nice to see startups like AiDash drive ROI by applying AI in energy management in ways that aren’t 100% based on generative AI models. Don’t you think?
If you enjoyed this case study on the use of AI in energy management, there’s much more where this came from within the upcoming book that I’m publishing in partnership with Wiley, called The Data & AI Imperative: Designing Strategies for Exponential Growth. The book comes out in the fall of 2024, and I may be able to get you a free review copy if you register your interest here.
🚀 If you’re a technical founder who’s looking for ways to improve your customer satisfaction, retention, and revenue like Avista did in this study, feel free to book a time in my calendar here so we can discuss ways that I can support your company’s growth and marketing needs.
