By Riya Sherchan, Senior Manager at Ipsos MMA
I recently had the opportunity to speak at General Motors’ Women in Data Science (WiDS) conference, connecting with women who are shaping the future of data science and translating analytics into real-world business impact. My presentation focused on a challenge every marketer faces: the pressure to prove business impact given how data and measurement are becoming more fragmented.
Traditional models—often siloed, retrospective, and slow—no longer serve the speed or depth of decision-making needed in today’s rapidly evolving media landscape. At Ipsos MMA, we’re redefining measurement through what we call “predictable incrementality,” a connected, forward-looking approach that helps brands not only understand past performance but also predict the incremental value in terms of both short and long-term sales.
The Rapidly Evolving Data Landscape
The evolution of the data landscape is accelerating. Consumers demand more control over their personal data, with regulators and platforms responding accordingly. Traditional measurement strategies that leaned heavily on third-party tracking are losing effectiveness due to data sparsity issues, challenging marketers to adapt in real time.
This shift represents an inflection point, pushing marketers and marketing measurement and optimization solution providers to modernize how they collect, interpret, and act on marketing data. It’s unlocking opportunities to build faster, smarter, and more resilient systems that project to and track sales, investment, and brand-based metrics.
Three foundational shifts are reshaping marketing measurement:
- Move to First Party Data: First-party data captured through owned websites, apps, and CRM systems has become the most valuable source of truth for marketers. It’s consent-driven, intent-rich, and creates a foundation for smarter audience targeting across the funnel.
- AI is becoming table stakes: With over 90% of digital media transacted programmatically, AI engines make microsecond decisions on who to reach, where to show up, and how to optimize delivery. These models analyze a complex web of signals—audience behavior, creative performance, time of day, device context—all to maximize results in real time.
- Real-time signals driving real-time actions: Organizations can no longer wait for end-of-quarter reports to adjust media strategy. Whether it’s a traffic spike, a shift in social sentiment, or a sudden drop in conversion rates, these signals trigger immediate optimization.
Consumer Journeys: Anything But Linear
Consumer engagement with brands has become increasingly fluid and anything but linear. The traditional funnel—awareness to consideration to conversion—no longer reflects actual behavior. Consumers bounce between stages, revisit touchpoints, abandon paths, and reengage days or weeks later.
A single journey might span Instagram discovery, price checking on Google, research on Reddit, a YouTube retargeting ad, and finally an email conversion, often across multiple devices and channels.
Key shifts shaping customer journeys:
- Journey fluidity is the new normal. People don’t follow a straight linear path, and we cannot expect every touchpoint to be directly measurable.
- Micro-moments can have a macro impact. Small interactions like a six-second bumper ad or a quick product page visit can meaningfully shape high-value actions.
- Context is everything. The same person behaves differently depending on mindset, device, time of day, or platform.
Measurement must evolve from tracking isolated touchpoints to understanding cumulative cross-channel influence over time dynamically
The Problem with Siloed Measurement
The most common measurement approaches are MMM (Marketing Mix Modeling), MTA (Multi-Touch Attribution), and incrementality testing. Each framework serves a purpose but comes with tradeoffs.
MMM is a top-down approach examining weekly variation by combining media and non-media factors (pricing, promotions, distribution, seasonality) to explain business outcomes. It’s valuable for budget planning or assessing long-term impacts. However, traditional MMM tends to be slower and more aggregated. It provides strategic altitude without tactical agility.
MTA is a bottom-up approach addressing tactical needs. It focuses on user-level interactions across digital touchpoints to analyze conversion paths, answering questions about which ad on which platform influenced which action. But MTA has limitations as it relies on increasingly restricted user-level tracking, has limited visibility into offline or cross-device behavior, and can overemphasize lower-funnel activity.
Incrementality Testing is a method to measure the impact of a specific marketing activity, like an ad campaign, on desired outcomes such as sales or conversions by isolating the effect of the activity by comparing a test group exposed to the marketing activity with a control group that isn’t. The difference in results between the two groups reveals the incremental lift or the impact attributed solely to the marketing activity.
What’s more, when these approaches are done independently, they could produce different answers, not because an individual approach is flawed, but because they each operate on different datasets, time horizons, and assumptions.
This creates a deeper challenge: when tools don’t align and messages conflict, confidence erodes. Marketers question the data, the models themselves, the decisions, and sometimes even their own instincts.
Unified Measurement: The Path Forward
Unified Measurement is the concept behind predictable incrementality. It is a framework helping marketers optimize smarter, act faster, and work with confidence by combining these three approaches into a single recursive system of measurements
The most effective measurement strategies bridge the gap between strategic planning and tactical execution, connecting long-term impact with real-time decisions. The unified measurement framework works like this:
- Marketing Mix Modeling forms the foundation, capturing both media and non-media factors to provide a broad, top-down understanding of what’s truly influencing business outcomes.
- Agile Attribution brings highly granular analysis of which channels, messages, creative formats, and audience segments are delivering incremental impact, all informed by MMM priors.
- Incrementality Testing enables structured in-market experimentation that validates assumptions surfacing new opportunities.
The process continues through continuous feedback loops where test results feed directly into the unified measurement framework, making models smarter and more aligned over time.
The result is a dynamic, always-on measurement system powering in-flight optimization, enabling channel-level precision, and informing decisions across media, creative, and strategy teams. At the center is one shared objective: measuring incrementality and understanding what drives lift beyond what would have happened anyway.
Predictable Incrementality in Action
A case study with a global pharmaceutical brand demonstrates what’s possible when unified measurement moves from theory to execution.
A strategic foundation was developed using Marketing Mix Modeling to quantify the impact of both media and non-media drivers like TV, paid search, sales force calls, copay card usage, seasonality, and competition. This provided a channel-agnostic view of performance essential for aligning investment decisions with business outcomes, not just media metrics.
From there, attribution across programmatic, social, video, and search provided day-to-day granularity to understand what drove lift by audience, messaging, and moments. This was then used to understand how to reallocate spend mid-flight.
Structured in-market testing was then leveraged to validate tactics, pressure-test model insights, and unlock whitespace opportunities. Experiments with sequencing, adjusted audience definitions, and refined messaging were based on real-world response.
All of this created a self-improving feedback loop, enhancing both attribution accuracy and MMM forecasting. To scale impact, AI-driven signal detection identified early shifts, reduced lag, and surfaced next-best actions at the placement level, such as pulling back from oversaturated audiences, doubling down on high-performing video sequences, or rotating fatigued creatives. This shifted from reactive to proactive optimization.
The results were measurable, predictive, actionable and remarkable:
- $86.5 million in incremental revenue across six months
- 34% drop in CPM through tighter targeting and smarter frequency capping
- $15.5 million in media cost savings by eliminating underperforming partners
It worked because MMM set strategic direction, attribution provided real-time clarity, in-market testing turned insight into proof, and AI scaled that proof into action. Unified measurement became the engine powering smarter, faster, more connected marketing.
The Future of Marketing Measurement and Predictable Incrementality
There’s no one-size-fits-all solution, but there’s always a smarter way to connect planning, optimization, and learning to move faster with greater confidence.
In measurement, we often focus on precision, but what’s truly needed is precision that is predictive and moves at the speed of decision-making. Predictable incrementality offers the ability to build systems that help anticipate what will drive true incremental value while there’s still time to act.
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Riya Sherchan is a Senior Manager at Ipsos MMA, specializing in digital strategies and media measurement across multiple industries including automotive, financial services, pharmaceuticals, and retail. With a background in applied mathematics and economics, she helps global brands translate complex analytics into actionable business strategies.
