Turning retail and viewing data into advertising intelligence

For advertisers, better targeting has always promised the same outcome: reach the right audience, in the right context, with less wasted spend.

What has changed is the level of data now available to support that ambition.

Retailers can understand what customers buy, browse, abandon, repeat and switch from.Broadcasters, streaming platforms and media owners can understand what audiences watch, when they engage and which content environments hold attention. One dataset points towards commercial intent. The other gives context around attention and behaviour.

At Ardent, we see this fundamentally as a pipeline problem before it is an advertising problem.

The value does not come from simply holding retail data or viewing data. It comes from building the data pipelines that can prepare those signals, join them without losing meaning, apply governance, maintain quality and make the output available where it can improve advertising decisions.

That distinction matters.

The timing also matters. Retail Media and TV+ are becoming major areas of digital advertising growth, with IAB UK forecasting UK digital ad spend to reach £45 billion in 2026 and identifying both as key forces shaping the market.

As more advertising value depends on first-party signals and cross-platform measurement, the pipelines behind those signals become more commercially important.

The Ardent experience

Our view of targeted advertising starts with the pipeline, not the campaign interface.

A campaign platform can only act on the data it receives. If that data is late, incomplete or inconsistently defined, the targeting will reflect those weaknesses. If the pipeline is designed properly, the same signals become more valuable: audiences are fresher, measurement is clearer and optimisation can happen while there is still time to act.

We have seen this first-hand in our work with media and audience data. In one Ardent‘SuccessStory’, our team built scalable pipeline infrastructure for a market research client working with large volumes of TV, mobile location, point-of-interest and pixel data. These sources arrived in different formats and needed to be cleansed, processed, validated and enriched before they could support downstream insight.

The outcome was not just a better technical pipeline. It gave the client quicker access to accurate, usable data, including hourly reporting, so their data science teams could deliver faster insight to end customers.

The lesson is clear: useful data is engineered, not assumed. Whether the source is TV, location, pixel, retail or viewing data, its value depends on the pipeline that makes it accurate, accessible and ready for use.

Why different signals need careful handling

Retail data and viewing data work well together because they answer different questions.

• Retail data shows commercial behaviour: what people buy, how frequently they buy, which categories they return to, where they are price-sensitive and how they respond to offers.

• Viewing data shows attention and context: what people watch, when they engage, which formats hold attention and which content environments align with certain audience behaviours.

Together, these signals can help advertisers move beyond broad demographic assumptions and build audiences around observed behaviour and relevant media environments.

But joining these datasets is not straightforward.

Retail systems may be organised around products, baskets, transactions, loyalty IDs and promotions. Viewing systems may be organised around programmes, devices, impressions, sessions, households and content metadata. A well-designed pipeline needs to bridge those differences without flattening them.

That means aligning definitions, deciding which signals are reliable enough to use, handling delayed or incomplete records, and ensuring consent and usage rules follow the data.

This is where pipeline design becomes the difference between data that exists and data that canactually betrusted.

The pipeline work behind better targeting

At Ardent, we are focused on closing the gap between valuable data and usable data. In targeted advertising, that means turning fragmented retail, viewing and campaign signals into reliable flows that can support real business decisions.

Behind every usable audience segment is a series of pipeline decisions. Take a common use case: an advertiser wants to reach people whose recent buying behaviour suggests interest in a product, and whose viewing behaviour suggests they are likely to engage in the right content environment.

To make that possible, the pipeline must do more than move data from one place to another. It needs to ingest retail data, collect viewing data, align product, customer, household, impression and campaign definitions, then clean, match, validate and govern the data before it reaches activation or measurement tools.

If that process is weak, the campaign inherits the weakness. Audiences may be built from old purchase behaviour, performance reports may struggle to reconcile exposure and purchase data, and teams may lose confidence in the logic behind the outputs.

A strong advertising data pipeline preserves the meaning of each source, applies consistent business logic and makes quality visible.

It is not enough to know that a file arrived or a job finished. Teams need to understand whether the data is complete, whether key identifiers are missing, whether timestamps are valid and whether an audience segment has shifted unexpectedly.

The problem is not that the data lacks value. The problem is that, without the right pipeline, the value is difficult to access, explain or act on.

Responsiveness has to be designed into the pipeline

Not every advertising data pipeline needs to operate in real time, and treating every flow as if it does can add unnecessary cost and complexity. The stronger approach is to understand which data needs to be responsive, which can run on scheduled refresh cycles, and where latency creates a genuine constraint.

For targeted advertising, some datasets may support planning, modelling or longer-term analysis. Others may need to support recent purchaser suppression, audience refresh, exposure matching or early performance reporting. These requirements place different demands on the pipeline.

Ardent’s approach to pipeline modernisation starts with those usage patterns. The priority is not speed for its ownsake, butdesigning each pipeline around the decision it needs tosupport:how fresh the data must be, how quickly it should be processed, where quality checks belong and how outputs need to reach downstream systems.

The value is not simply faster data. It is data delivered at the right level of freshness, reliability and control for the decision it supports.

New tools still depend on prepared data

As advertising data becomes more collaborative, many organisations are turning to clean rooms: controlled environments where partners can compare or match data without simply exchanging raw customer information. AI is also becoming more common in how audiences are modelled, campaigns are forecast and performance is interpreted.

But neither changes the underlying requirement.

A clean room can control how data is accessed, but it cannot make poor-quality inputs reliable.An AI model can identify patterns, but it cannot compensate for stale, incomplete or poorly governed data. In both cases, the quality of the output depends on the quality of the pipeline feeding it.

That makes upstream preparation critical. Before data reaches a clean room, AI model or activation platform, it needs to be shaped, checked, governed and refreshed at the right cadence.

At Ardent, we see these tools as part of the wider data architecture, not substitutes for the pipeline design that makes their inputs reliable. They can increase the value of advertising data, but only when the pipelines feeding them are dependable.

Better tools do not compensate for weak data flows. They expose them faster.

What clients should do next

For those embarking on this process internally, we recommend that organisations start by reviewing whether their current data pipelines are ready to support the advertising use cases they want to deliver by asking three important questions.

• Where does the data flow today? Understand how key advertising data moves from source to output.

• Where is value being lost? Spot delays, gaps or quality issues that reduce confidence in the data.

• Which flows matter most? Prioritise the pipelines with the greatest commercial impact or risk.

Answering these questions objectively requires specialized expertise. That is whyevery Ardent engagement begins with a deep-dive discovery phase.

Wetypicallylook atfour core areas:

• Performance and latency: analysing runtime delays, bottlenecks and whether the pipeline can support high-frequency or real-time data streaming where instant ad targeting depends on it.

• Reliability and data integrity: identifying past failure points, data loss, incomplete records or quality issues that could affect campaign attribution, measurement confidence and ROI.

• Scalability and cost optimisation: evaluating whether the architecture can handle massive, fluctuating data volumes efficiently, without compute costs increasing unnecessarily.

• Automation and observability: assessing where end-to-end monitoring, automated alerts, triggers and self-healing mechanisms could reduce manual intervention and improve operational control.

The goal isn’t to tear everything down and rebuild from scratch. It’s to identify exactly what needs to be modernised, automated, or better governed so your data foundation is rock-solid.

Better advertising intelligence starts with pipeline readiness. You must have accurate, resilient, and well-designed data flows before your activation, measurement, or AI tools can truly perform.

What this means now

Targeted advertising is becoming more data-driven, but the advantage will not come from data access alone.

Retail data, viewing data, clean rooms, AI and measurement platforms all create opportunity. But that opportunity only becomes useful when the pipelines behind it can prepare, connect, validate and govern the data with confidence.

That is where Ardent’s focus sits: upstream of the campaign, in the data foundations that make better decisions possible.

The organisations that get this right will not just have more data to work with. They will have data they can trust, explain and act on.

And in a market where everyone is trying to reach the right audience, the question is no longer just who you want to target. It is whether your data is ready before your campaign is.