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Netflix has been a data-driven company since its inception, arming decision makers with the metrics, insights, predictions, and analytic tools for better decision making. From product, content, studio, marketing, and business operations, Netflix performs context-rich analysis to provide insight into every aspect of their business, partners, and, most importantly, end user experience. With an impressive content library and millions of subscribers around the world, Netflix is no stranger to leveraging machine learning and analytics to provide best-in-class customer experiences and make scalable business decisions.
Like Netflix, utilities sit on a wealth of customer data, which can be leveraged for more powerful decision making and an elevated customer experience. The rise of brands in Solar PV (DER), Smart Home Devices and Apps and Electric Vehicles (EVs), has prompted customers to expect a higher level of personalization and ease of use when interacting with their home. While there have been improvements in the past 5 years, utilities still remain among the lowest-performing industry group when it comes to delivering distinct digital customer experiences. It becomes more imperative than ever for utilities to put forth a strategy that exceeds customer’s expectations through personalized experiences while lowering cost-to-serve and achieving efficiencies that scale.
In this presentation, we hear from Gagan Hasteer, VP Content Engineering from Netflix to learn how the company leverages data for better outcomes, followed by Varun Nagaraj, COO Bidgely, to hear how data based intelligence is actively being applied to utilities. Varun will share how similar techniques can easily be applied to energy consumers through personal and proactive engagement. Audience members can return to their organizations able to demonstrate how applying AI to customer data serves as the foundation for making each customer interaction personal, yet cost-effective and highly scalable to an entire service territory.
Feel free to ask questions in the discussion forum below - speakers will be responding as quickly as they can. View this webinar by the end of the day on June 9 and get entered to win a $100 Amazon Gift Card.
Is past consumption being tracked separately, to serve the appropriate artwork for each customer?
Yes. Past consumption is an important indicator that drives what artwork is shown to a member.
Has Bidgely done anything with Natgas utility smart meters? Can you ID what is running on the natgas side? WH, Furnace, Dryer, Range, Pool heater, BBQ pit, logs, etc.
Yes. Bidgely has worked with NatGas smart meters and “non smart” meters (i.e. monthly usage). The algorithm can identify space heating, water heating and cooking.
The gas disaggregation algorithm is in production use at several gas only utilities and also at dual fuel utilities.
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How do I access the actual podcasts and webinar. I only see the discussions.
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Bidgely – What type of data is required to get this level of insights?
We work with monthly meter data reads or hourly reads or 15 minute reads (that is smart meter and non smart meter data). But the more granular the meter data, the better the quality / accuracy of insights. That is, 15 minute data is better than monthly data.
This is for Varun from Bridgely. Is your data analytics and load disaggregation just for residential and electric, or can it also serve gas and SMB?
Hi Steve: Our disagg and analytics covers Resi and SMB. It also covers electric and gas.
For Varun- have state regulators shared concerns either about privacy issues for the consumer or with equity issues since the higher earning households are more likely to have savings opportunities (you identified EV charging, pool pumps…) versus lower income communal housing. Thank you. Excellent information!
Great 2 part question John. 1) On equity: Yes, the utilities are definitely interested in better serving the low/medium income group. In terms of our product, our analytics take this factor into consideration (for example if a customer has enrolled in a LMI program) and the resulting insights and actions are tuned to them. For example, a recommendation that involves high upfront spending (such as “consider upgrading your A/C”) might be shown to a high income user but not to a LMI user. Or if there are programs available for LMIs, they are promoted to LMU users and not to… Read more »
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