Use Case: MyTerms Intent Based Advertising
Adverts, marketing messages and offers driven by data that stays under personal control.
Overview
DataPal enables people to shape the adverts, marketing messages and offers that they receive without the typical loss of control associated with the current promotional model.
This means that people can share their forward buying intentions with selected product/ service providers who are willing and able to respect their terms under which this data is shared.
This Use Case demonstrates how personal data can shorten and improve buying processes from the individual perspective.
Context
Advertising and marketing is a half a trillion pounds a year business worldwide. It is funded by brands, selling to people and has many middle-men/ intermediaries involved. There are many variations of the discipline; almost all are intensely data-driven.
Much of the data used in today’s approaches comes from surveillance. That is to say, watching what people are doing online and inferring what adverts, marketing messages and offers they should be targeted with.
DataPal enables a different model altogether; one in which individuals make their buying intentions available to organisations willing and able to accept their terms for doing so. These organisations then have active permission to analyse and respond to these buying intentions in a range of ways. This leads to significant opportunities for innovative new ‘pull-based’ approaches to advertising, marketing and promotional activity.
Challenge
The advertising and marketing approaches that have evolved over the last 10-15 years are built on gathering significant volumes of personal data and packaging those into ‘profiles’. These profiles, or access to them, are re-sold many times over; often this is in ‘real time’, programmatic ways. But that approach is increasingly under pressure from regulators, and is causing declining levels of online trust. There is also a very significant amount of fraud in the current model; and AI/ Agentic AI is very much on the horizon as a disruptive effect.
Our challenge lies in understanding and credibly implementing a range of human/ buyer centric methods that can initially complement, and then compete with, surveillance based approaches.
Solution
Attrax is a DataPal powered suite of tools that enable individuals to safely and fruitfully engage with the advertising, marketing and promotional offers ecosystems.
Attrax puts the fundamental capabilities in place on the side of the customer that enable them to be an independent actor in the digital environment. This includes:
At least one customer controlled strong digital identifier that acts as their
Data management tools that make the customer as the curator of by far the best ‘profile’ of them, their context and their buying requirements.
AI powered agents acting on behalf of individuals or supply organisations to manage workflows and complete tasks
An audit log that records what data was shared with whom, when, for what purpose
MyTerms, aka IEEE P7012, the outer protective layer that enables the individual to genuinely control their own rich, deep data and share it under an appropriate standardised agreement.
There is then an equivalent set of connectors and listen/ respond agents provided to brands and their service providers to enable them to link their own adtech, martech, CRM and similar tools to those on the customer side.
How It Works (Flow)
Buying intention curation: Individuals use their transaction data via Open Banking feeds, to identify recurring buying scenarios, and then add further buying intentions via manual entry.
Requirement Data standardisation: DataPal automatically standardises requirements data to make it easily exchangeable.
AI-Based Augmentation: External data sources and machine learning enrich requirements records with context, preferences, product, merchant, and relevant meta data.
Human-in-the-Loop Review: Users can review and enhance requirement records — adding personal notes, receipts, or clarifications.
Granular Permissioning: The user may choose to share all, some, or even just one requirement record with third parties — time-bound, purpose-specific, and revocable at any time.
Ongoing Learning: Once a requirement type has been augmented, DataPal auto-recognises and enriches future similar records automatically.
In simple terms, this leads to the customer’s buying intent being expressed in very detailed form in ways that are ideal for the supply side to engage with. This moves well beyond the current scraping, minimised data and guesswork based model
Actors
Individual / Data Owner: Ingests, enriches, and manages their transaction data and permissions.
Data Provider/ Service/ App: Enabling the capture and ongoing flow of data into DataPal.
Organisation / 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 check-up reminders, prescription management, or decision-making assistants — using DataPal’s APIs.
Benefits
Individuals Gain clear, usable requirement records; control exactly what’s shared; unlock new value from their own data. Longer term individuals build an automated, personalised data vault that continuously learns and enriches itself over time.
Organisations / Service Providers Access accurate, consented, and purpose-specific requirements data. 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. Longer 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
Up to 90% reduction in over-shared data compared with conventional go to market tactics open to individuals.
Major improvement in requirement data quality and completeness through AI augmentation.
Increased customer trust and engagement, measured by active engagement and sales.
Reduction in wasted advertising and marketing expenditures that arises when targeting people with specific products, service offers when they are not in the market for them.