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Submission to the Treasury consultation on CDR energy datasets

Our views on the data that should be covered by the Consumer Data Right in the energy sector

September 2019: This submission is in response to the Treasury consultation on the Consumer Data Right (CDR) priority energy datasets. Visit our government submissions hub for more of our submissions to government consultations and inquiries.

Finder continues to be very supportive of the introduction of CDR in Australia. We believe that CDR will empower Australians to take control of their personal data and that this will equip them with the information they need to make better financial decisions. As our business sits across many of the impacted categories, we are excited about the role that we can play in helping Australians connect the dots between their newly available datasets.

When it comes to using the data created by CDR for energy, our primary use case will be helping Australians compare and switch to energy plans that better suit their needs. We believe that CDR has the potential to have a big impact on the retail energy market in Australia, as currently, there are many consumers that have a limited understanding of how much energy they use, how much they pay for it and what type of energy plan they are on. Our consumer survey of 1,006 Australians from September 2019 showed that 48.2% of people paying for electricity are not sure they're getting good value for money. The research also showed that just 4.4% of people paying for electricity are planning on switching providers in the next 6 months.

In this submission, we have answered only the questions where we can add value to the consultation. As we get closer to the launch of CDR for energy, we would be very interested in working closely with the Treasury and the Data Standards Body on how the designated datasets should be structured, particularly for the metering, billing and retail product datasets.

National Metering Identifier (NMI) standing data fields:

Finder does not have particular expertise on the specific data points available in the NMI datasets, but we do believe that the NMI data points referenced in the consultation paper would be useful in a basic price comparison for users. Average Daily Load (ADL) is a good starting point for making assumptions about a customer's energy usage which leads to more accurate pricing estimations. The Network Tariff Code will help us to understand which tariff the consumer is on, and details of the meter type and whether there is a controlled load ensure that we can recommend the right type of plan for that customer.

One additional field that we would like to see included in the CDR from the NMI dataset is the supply address related to a particular NMI. This will reassure customers that the NMI information is related to the meter at their property and the supply address can also be helpful when trying to resolve metering issues for a property (for example, when meters have been set up incorrectly after building work).

Finder would also recommend making some of the data points from the NMI dataset available to consumers for a property that they are about to move in to. In order to find the energy plans that are relevant to them, consumers need to know what type of meter is in their new home and whether there is a controlled load present. However, these are questions that most consumers are not able to answer when they have no previous billing information for the new property. Giving consumers access to the meter type and details of any controlled load for their new home would resolve this issue. We believe there could even be a case for making a basic level of NMI information available in a public database.

Metering data:

For the basic comparison use case, we would use metering data to assess how much energy customers are using as this is a good indicator for how much energy that consumer will use going forward. Given the nature of energy pricing, the more accurate we can be with estimated usage, the better the pricing estimates we provide will be. In an ideal world, a consumer would be able to share 12 months' worth of usage data with us so that we could account for any seasonal differences in energy usage patterns.

When it comes to meter types, our energy comparison category is currently focused on residential and small business customers only, so it's unlikely we'd use Type 1-3 metering data. However, we would like to see the designation include metering data from Type 4/4A, Type 5 and Type 6 meters.

The most important use case supported by smart and interval meter data for Finder is accurate pricing estimates for customers comparing time of use (ToU) plans. Our understanding is that many Australians with these types of meters are only eligible for ToU plans. Unless they are extremely engaged in their energy usage, many of these consumers are not able to accurately provide information on what time of day they use energy across the year.
If a consumer provides inaccurate ToU information, it can dramatically impact the estimated prices that they are provided with and, in many cases, this can lead to a user selecting a sub-optimal plan for their needs. As such, we believe interval meter data will be a key part of the CDR for energy, as it will allow third parties like Finder to provide consumers with more accurate price estimates for ToU plans. This is particularly important as the smart meter rollout continues and as more Australian consumers are moved on to ToU energy plans.

Similar to the consent framework rolled out with Open Banking, we believe that empowering users to proactively manage their privacy settings will help mitigate privacy risks. It's also important to note that CDR for all sectors in Australia will be "opt-in". This means that any consumer with particular concerns about sharing their metering data can choose not to do so.

Billing data:

For the basic comparison use case, we would use billing data to see what users have been paying for their energy usage. This information allows us to make estimations about how much a consumer could possibly save by switching to a different plan.

We cannot comment on how retail customer billing data is currently being shared between market participants or if there is an industry standard for billing information. However, anecdotal evidence from our user testing has shown a variety of formats when it comes to how energy bills are set out and what information is included on them.

We believe that it's important that the CDR designation outlines a clear format for this information, as this will enable comparators like Finder to show consumers potential cost savings when comparing energy plans. We would be very interested in engaging further with the Treasury or the Data Standards Body on how this dataset should be structured.

Retail product data:

Finder believes that the designation of all product data classes currently held by Energy Made Easy and Victorian EnergyCompare will support the majority of basic comparison and switching use cases. To date, this product information has not been readily accessible in a machine-readable format.

To maximise the utility of the retail product dataset, we hope to see a data format which is consistent across the various jurisdictions in Australia. Again, we would be very interested in engaging with the Treasury or the Data Standards Body directly on the format of this dataset. Finder worked closely with the ACCC and Data61 to test the product reference data for the Open Banking regime in Australia against our own product database when it was released on 1 July 2019. We note that these tests highlighted potential improvements in the format of product reference dataset which could have been addressed before the first release.

Data sets for future implementation:

An area that could be explored further is the potential to aggregate and anonymise the data being accessed by CDR energy consumers to create new datasets that provide insights about the overall state of the retail energy market in Australia. We believe the AEMO gateway model makes this easier to execute for CDR for energy than it would be in the Open Banking model. To enable this, we would suggest that during the consent flow that there could be a checkbox where the user consents to their data being used in this way.

From an energy usage perspective, we believe this aggregate and anonymised information could be useful for consumers moving to a new home. Moving home is a key trigger for switching energy providers for lots of consumers but, at this stage, it is very difficult for a consumer to accurately estimate how much energy they will use in their new property. Aggregated metering and/or billing data could be used to create energy usage benchmarks that help consumers with this estimation. The output of this could be similar to the benchmarks created by the AER but with more regular updates and improved granularity in the data.

Similarly, billing data could be used to get an overall picture of the cost and affordability of energy in Australia. Australian Bureau of Statistics (ABS) data on inflation has shown that the cost of electricity has grown significantly faster than the general rate of inflation over the last decade. An aggregate view of billing data from the CDR regime would allow for this to be tracked in real time and would provide another helpful benchmark for consumers trying to assess if they are getting a good deal or not.

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