{"id":1806,"date":"2025-08-22T13:58:58","date_gmt":"2025-08-22T13:58:58","guid":{"rendered":"https:\/\/nuprogrammatique.com\/?p=1806"},"modified":"2026-01-22T16:27:37","modified_gmt":"2026-01-22T16:27:37","slug":"why-incrementality-is-the-new-holy-grail-of-programmatic-advertising","status":"publish","type":"post","link":"https:\/\/nuprogrammatique.com\/en\/blogue\/why-incrementality-is-the-new-holy-grail-of-programmatic-advertising\/","title":{"rendered":"Why Incrementality Is the New Holy Grail of Programmatic Advertising ?"},"content":{"rendered":"\n

For decades, brands have invested heavily in advertising platforms built by major tech players. These technologies have reshaped market standards offering mass reach, precise audience targeting, and real-time performance optimization.<\/p>\n\n\n\n

But today, more and more experts are challenging this technological dominance and calling for a fairer, more transparent way to measure advertising performance, one that includes incrementality. In this article, we\u2019ll explore what incrementality really means, how to measure it, and why it\u2019s becoming an essential pillar of programmatic strategy.<\/p>\n\n\n\n

What Is Incrementality?<\/strong><\/h2>\n\n\n\n

Borrowed from the world of science, incrementality has found its place in digital marketing as a key way to measure real advertising impact. Unlike attribution models, which simply divide credit across multiple touchpoints, incrementality answers the one question that really matters: \u201cHow much incremental revenue did this campaign actually generate?\u201d<\/em><\/p>\n\n\n\n

In other words, incrementality helps you understand whether a programmatic campaign truly delivered added value or if the results would have happened anyway. Despite its relevance, few advertisers are truly leveraging this approach today. Yet it should be the compass guiding every programmatic investment, because incrementality directly measures whether your advertising efforts created real, measurable business growth.<\/p>\n\n\n\n

How Do You Measure Incrementality?<\/strong><\/h2>\n\n\n\n

Now that we\u2019ve defined the concept, let\u2019s break down the two most common approaches for measuring it reliably and effectively.<\/p>\n\n\n\n

1. A\/B Testing<\/strong><\/p>\n\n\n\n

This method compares performance between two groups: one exposed to the campaign, and one unexposed. A\/B testing is statistically sound and highly transparent but it requires a large enough sample and sufficient runtime. When implemented during the planning phase, it allows advertisers to fine-tune their strategy before launching full-scale campaigns.<\/p>\n\n\n\n

2. Lift Modelling (Post-Campaign Analysis)<\/strong><\/h4>\n\n\n\n

This method applies after a campaign has run. It measures the actual impact of your advertising beyond engagement metrics by isolating results directly caused by exposure. In a privacy-first context, lift modelling provides reliable insights on performance and return on investment (ROI), helping marketers make informed, data-driven budget decisions.<\/p>\n\n\n\n

Whether you measure incrementality upfront with A\/B testing or after the fact via lift modelling, the goal remains the same: prove and optimize the real business impact of every advertising dollar spent.<\/p>\n\n\n\n

Key Metrics to Measure Incrementality :<\/strong><\/strong><\/h2>\n\n\n\n

Out of all the programmatic KPIs available, two stand out when it comes to quantifying net impact:<\/p>\n\n\n\n

1.1 Incremental Lift:\u00a0<\/strong><\/h3>\n\n\n\n

This metric measures the increase in conversions (sales, leads, sign-ups) from a group exposed to ads versus a control group. It isolates the net effect of your campaign removing factors like organic sales, seasonality, or other concurrent marketing efforts.<\/p>\n\n\n\n

M\u00e9thode de calcul : <\/strong>
\"\"<\/p>\n\n\n\n

Example<\/strong> :
A brand typically sells 1,000 products per month with no advertising.
During a test, two groups of 1,000 users are observed:<\/p>\n\n\n\n