Use Case: Programmatic Comparison Shopping Across a Range of Service Categories
Guidance, support and data provision to help people compare products and services, and switch between them where appropriate.
Overview
People and households have an increasing number of products and services that are delivered as ongoing payments or subscriptions, often managed within a time bound contract. This includes significant utilities and financial services such as home energy, broadband and entertainment services, mobile telephony and data, computer storage; and an ever growing number of ‘software as a service’ (SaaS) categories.
Pricing, service quality, competitive options and customer needs can all change over time. For those reasons, regular, trustworthy and automated comparison services can be a very good means to ensure that people are always on the best fit product/ service for their needs.
DataPal comparison engine enables people to bring together their products and services from multiple providers; rate them and then uses AI to augment that relationship data. That rich, deep augmented relationship record can then be made available to alternative service providers who can use it to provide comparisons and alternative offers. Where a switch takes place, the data required to do so is already in place and DataPal can proactively support that process.
Context
Organisations providing services over time may rely on customer inertia to enable increases in prices, or reducing levels of service. Contract termination fees are common, as can be other forms of ‘lock-in’.
Customers can counter these practices by regularly using comparison processes or services to check their current service costs and terms versus those available in the market; or potentially alternative deals with the existing provider.
Comparison and switching are both data intense processes. Tariffs, contract details, serial numbers, termination fees and usage details all require accurate data to be provided in a timely manner and in the right format. But often these are available to be acquired from industry providers via APIs. So comparisons can be automated, and run as frequently as market conditions render sensible. And the customer can set the parameters around when a switch could be triggered; perhaps just using price/ money saved. Or potentially to include other service variables, perhaps sustainability or contract terms.
Challenge
Incumbent suppliers would prefer that comparison processes not exist, so will seek to avoid assisting them. But these services are for the buyer not the seller, so route around supply side barriers. In practice many of these challenges relate to the gathering of the data required to understand current product/ service status and use. There is also a requirement to understand and map the detailed processes around each product/ service provision type. This process mapping enables us to identify sub-processes that can be either partly or wholly supported by AI powered agents.
Solution
DataPal has engaged with experts from the comparison shopping sector, and with supply service providers, in order to understand the relevant processes and data requirements of a comparison and switching scheme.
We have built processes and data management tooling that support the main comparison scenarios; and will continue to build out the range over time.
How It Works (Flow)
Data Ingestion: Individuals import their transaction data via Open Banking feeds, file uploads (bank exports, receipts), or even manual entry.
Data Cleaning & Parsing: DataPal automatically standardises formats, matches related transactions, and fills in missing context.
AI-Based Augmentation: External data sources and machine learning enrich transaction records with product, merchant, and contextual metadata.
Human-in-the-Loop Review: Users can review and enhance records — adding personal notes, receipts, or clarifications.
Granular Permissioning: The user may choose to share all, some, or even just one transaction record with third parties — time-bound, purpose-specific, and revocable at any time.
Ongoing Learning: Once a transaction type has been augmented, DataPal auto-recognises and enriches future similar records.
Actors
Individual / Data Owner: Ingests, enriches, and manages their existing product/ service inventory data and permissions.
Comparison Engine / Service Provider: Requests specific, purpose-limited access to verified transaction data (e.g., for lending, loyalty, or service fulfilment).
DataPal Platform: Provides ingestion, cleaning, AI augmentation, and permission management under the IEEE 7012 MyTerms model.
AI Agent / Developer: Builds value-added services — such as contract/ subscription reminders, or price-comparison assistants — using DataPal’s APIs.
Benefits
We can outline the benfits to each stakeholder both in the immediate short-term and the longer-term:
Individuals gain clear, usable product/ service comparison records; unlock new value from their own data. Longer term individuals build an automated, personalised data service of their own that continuously learns and enriches itself over time.
Organisations / Service Providers Access accurate, consented, and purpose-specific comparison data. And then, where appropriate, a specific, well informed switch/ onboarding request. Longer term lower compliance risk, improved customer trust, and better service personalisation.
Developers / Ecosystem Access a composable, consent-based data platform for building innovative financial and AI services. Llonger term grow new business models based on privacy-first, customer-controlled data collaboration.
Society / Regulators Demonstrates practical privacy innovation and ethical data sharing. Longer term this encourages transparent, compliant data ecosystems aligned with public trust.
Outcomes
Significant increase in people/ households who regularly ensure they are in the best deal for them that they can be across a wide range of services.
50% improvement in transaction data quality and completeness through AI augmentation.
Increased user trust and engagement with DataPal measured by active consent renewals and sharing frequency.