2026 CPG Marketing Formula #3: Building Always-On Incrementality

The CPG marketing landscape has become immeasurable by traditional standards.

These brands now manage retail media across 8+ platforms, search across 4+ engines, social across 6+ networks, plus CTV, audio, and emerging AI shopping assistants. Each channel reports success differently. Consumers switch between Amazon, Walmart, Instacart, and direct-to-consumer sites within the same purchase journey.

And now, AI assistants like ChatGPT Shopping and Google’s AI Overviews are inserting themselves between brands and buyers in ways attribution models never anticipated.

According to Path to Purchase Institute research, 56% of US CPG manufacturers say performance measurement is the top factor in retail media budget allocation. But here’s the complexity gap: data timeliness remains a persistent challenge, with only about 23% of retailers sharing data in real time, 56% delivering results immediately at campaign end, and 21% taking even longer.

Meanwhile, brands coordinate across channels with incompatible metrics and measurement frameworks that weren’t built for this complexity.

This is the incrementality challenge: knowing which spend drives new sales, getting answers while campaigns run, and establishing causality across fragmented journeys. But there’s another layer: CPGs are increasingly looking beyond initial purchase to the long-term value associated with customer lifetime impact. They’re less concerned with just new sales and more focused on the overall market share and sustained customer relationships that true incrementality should measure.

Traditional approaches can’t solve this. What’s needed is infrastructure for always-on incrementality that guides spend continuously as complexity increases.

What is “incrementality infrastructure”?

Infrastructure is a vague word, but it’s the right one. Most brands think incrementality is a measurement question when it’s actually a systems question.

Running one geo test or looking at platform-reported lift isn’t infrastructure. It’s a tactic. Always-on incrementality requires four foundational elements working together:

1. Data unification. Integrated systems that connect retail media performance, search data, social results, SKU-level marketplace signals, digital shelf intelligence, and competitive context into a single causal measurement framework. Not dashboards. Not manual exports. Automated pipelines that enable cross-channel lift analysis at the product × retailer × channel × placement level.
2. Organizational capability. Processes and governance to run continuous incrementality tests, read lift mid-campaign, and act on results weekly instead of quarterly. Cross-functional coordination in which Marketing, Finance, and Analytics operate from shared definitions and move at the same speed.
3. Expertise and methodology. Knowing how to design valid tests at the granularity commerce media demands, isolate media lift from non-media factors (pricing, inventory, competitive shifts), and establish causality across fragmented consumer journeys. Understanding when geo holdouts work, when they break, and what to do instead.
4. Executive sponsorship and investment. Sustained commitment and resources to build infrastructure that compounds over time rather than funding one-off pilots. CMO-CFO partnership that frames measurement as a competitive advantage, not reporting overhead.

Without all four, incrementality stays theoretical. With them, it becomes operational.

Why incrementality is fundamentally different for commerce media 

Incrementality isn’t new. MMM works for TV. Geo tests work for regional brands. But commerce media breaks all of them. Commerce media isn’t just another digital channel. It’s where commerce and media intersect, which means performance depends on factors traditional media measurement methods ignore.

Consider Amazon Sponsored Products. Traditional incrementality tests ads versus holdouts. But that ignores these factors: Did the product go out of stock? Did a competitor drop price? Did review scores change? Did the retailer run a promotion?

Any of these non-media factors could drive sales independent of ads. Traditional methods measure correlation. Commerce media demands causality.

What commerce media requires: Decisions happen at the product × retailer × channel × audience × placement level. You need to know whether increasing spend for specific SKUs drives incremental sales after isolating Prime Day effects, competitor pricing, and seasonal patterns.

This demands unified causal measurement systems where retail media, search, social, SKU-level marketplace data, digital shelf signals, and competitive intelligence exist in a single framework.

And it demands always-on measurement. When pricing changes weekly, inventory fluctuates daily, and competitive actions happen constantly, quarterly tests arrive too late.

The incrementality infrastructure gap

Most CPG brands are managing complex channel mixes without the infrastructure to measure what actually matters at the speed that decisions demand.

When Finance asks which spend is truly incremental, Marketing shows platform-reported ROAS instead of lift. When opportunities emerge mid-campaign, reallocation waits until quarterly planning. When new channels launch, measurement frameworks start from scratch rather than integrating seamlessly.

Why always-on incrementality requires new infrastructure now

Traditional measurement approaches assume stability that markets no longer provide.

But that stability is gone.

The pressure isn’t just philosophical. According to eMarketer’s US CPG Industry Ad Spending Forecast 2025, CPG ad spending growth is slowing to 4.6%, below the national average for the first time in three years. Finance is demanding proof that every dollar drives incremental outcomes, not just correlated activity.

Meanwhile, technical debt compounds. According to industry research, 75% of martech pain points trace back to data issues (including integration) rather than the tools themselves.

The organizations that can measure incrementality in-flight and optimize accordingly will capture share from competitors still operating on quarterly review cycles. Those stuck with legacy approaches will defend budgets with hope instead of proof.

Assessment framework: diagnosing measurement gaps

Before building new infrastructure, assess where measurement capabilities currently stand.

Quick gut check: How do you actually know what’s working?

1. We track what platforms tell us and hope it’s true
2. We run tests… when we remember to, and dig through results eventually
3. We have incrementality frameworks that work for some channels
4. We measure lift continuously and optimize mid-flight across all platforms

Now let’s get specific. Use these 15 questions to identify which gaps actively limit strategic decision-making today. Honest assessment reveals priorities faster than aspirational planning.

Cross-platform profitability measurement:

• Can you compare cost-per-incremental-sale across Amazon, Walmart, Instacart, Target, and other major retail partners using consistent methodology?
• Or does each platform report success differently with no unified view?
• Can you answer “Which retail partner drives the highest true profitability?” with data, not guesses?

Incrementality versus attribution:

• Do you measure true incrementality (new sales that wouldn’t have happened otherwise)?
• Or do you just accept platform-attributed results that claim credit for everything?
• Can you distinguish baseline sales (happening anyway) from genuinely new demand?
• When Finance questions whether spend drove lift, do you have lift data or just correlation?

Data timeliness for in-flight optimization:

• How long does it take to get performance data after campaigns launch?
• Can you optimize within the first week based on real results?
• Or do you wait until campaigns finish to see what worked?
• Do you have dashboards that enable action or monthly reports that arrive too late?

Cross-channel data integration:

• Is retail media data integrated with search and social for cross-channel comparison?
• Can you identify when paid social drives retail media conversions or when search keywords influence Amazon behavior?
• Or does each channel operate blindly without understanding how they influence each other?

Non-media factor isolation:

• Can you isolate media lift from non-media drivers like pricing changes, inventory fluctuations, competitive actions, and review velocity?
• Or do your incrementality tests measure aggregate sales changes without distinguishing what actually caused them?
• When a test shows lift, can you confidently attribute it to media versus confounding factors?

Emerging touchpoint measurement:

• When new AI shopping assistants like ChatGPT Shopping or Google’s AI Overviews influence purchases, can your measurement systems detect and quantify that impact?
• Or are those touchpoints completely invisible in your current attribution?
• How will you measure effectiveness as more commerce shifts to AI-mediated interactions?

Budget reallocation speed:

• Can you reallocate budgets weekly based on performance data?
• Or do approval processes lock spending for quarters at a time despite mid-campaign insights showing better opportunities?
• When tests reveal winning approaches, how fast can you scale: days or months?

Core capabilities that enable continuous incrementality measurement

The following capabilities represent the infrastructure that modern measurement complexity demands. These aren’t aspirational best practices. They’re operational requirements for managing 15+ platforms, AI-mediated touchpoints, and Finance scrutiny simultaneously.

Unified data infrastructure across retail media, search, and social

Fragmented data prevents the cross-channel comparison that strategic decisions require.

Building this capability means piping performance data from all major channels into a unified warehouse: retail media networks, search engines, social platforms, CTV, and emerging AI touchpoints. But it also means integrating non-media signals: SKU-level marketplace data, digital shelf intelligence (pricing, reviews, availability), competitive context, and retailer-specific promotional calendars.

This isn’t about fancy dashboards. It’s about creating a single causal measurement system where you can compare Amazon to Target after normalizing for Prime Day effects, measure social efficiency against search while accounting for seasonal patterns, and connect commerce signals with media signals so incrementality tests can isolate true lift.

API integrations replace manual exports. Automated ETL processes normalize inconsistent data formats. The infrastructure connects commerce signals with media signals so incrementality tests can establish causality, not just correlation.

Progress indicators include decision-making timelines dropping from quarterly to weekly, analyst time shifting from data stitching to strategic analysis, and Finance approving budget reallocations based on cross-channel lift data rather than questioning every line item.

This requires sustained IT partnership and ongoing maintenance.

Continuous incrementality testing that enables in-flight optimization

Building this capability means running continuous incrementality experiments that provide lift reads fast enough to optimize current spend, not just inform next quarter’s plans.

This involves establishing test-control frameworks that work across platforms (geo-based holdouts for retail media, audience splits for social, keyword-level experiments for search), pre-registering hypotheses with clear success criteria, and automating analysis so lift reads emerge within days.

Critically, tests must isolate media effects from non-media factors. This means tracking pricing changes, inventory levels, review scores, and competitive actions within test and control groups to ensure observed lift is attributable to media.

The goal is organizational muscle memory around in-flight optimization: when incrementality tests show certain retail partners driving higher lift, budgets shift mid-campaign. When creative variants prove more effective, winning approaches scale immediately.

This also requires organizational change. Who has authority to reallocate budgets mid-campaign? What’s the approval threshold? How do Marketing and Finance coordinate on weekly optimization decisions?

Progress indicators include running 3-5 incrementality tests simultaneously across different channels, having lift reads available within 7 days of test launch, executing mid-campaign optimizations weekly, and cross-functional teams operating from shared definitions of valid incrementality evidence.

Cross-channel learning systems that multiply insights

When channels operate in silos, winning approaches stay isolated.

Building this capability means creating systematic feedback loops where insights from one channel improve others: audience performance in paid social informs retail media targeting, keyword efficiency in search shapes retail media campaign structure, retail media conversion patterns guide social creative development.

Connected measurement infrastructure makes cross-channel patterns visible. Pre-defined rules automate learning application: when testing reveals high-performing audiences in social, retail media campaigns expand targeting. When pricing analysis shows products facing competitive pressure, creative refreshes prioritize those SKUs.

This requires moving from static measurement frameworks to adaptive systems. Pre-define which insights trigger which actions: audience performance thresholds that activate broader targeting, creative fatigue signals that trigger asset refreshes, competitive context changes that adjust bidding strategies.

Beyond reactive optimization, advanced infrastructure enables predictive capabilities. Once you have reliable incrementality data, you can forecast next quarter’s outcomes based on planned spend, model marginal ROI at different investment levels, determine diminishing returns for each channel, and recommend optimal channel mixes before committing budgets.

Progress indicators include cross-channel optimization decisions happening weekly versus quarterly, insights from one channel improving another’s performance within days, and the ability to model “if we add $100K to Walmart, what’s the expected incremental lift?” with confidence.

Getting started: turning frameworks into action

Building measurement infrastructure requires systematic effort across multiple domains. While each organization’s journey differs, the following principles apply universally.

Start with infrastructure assessment. Map current data flows and identify integration gaps. Which platforms have API access? Where do measurement blind spots exist (AI assistants, emerging networks, non-media factor isolation)? This diagnostic reveals where to focus effort.

Build data infrastructure incrementally. Start with your three highest-spend channels plus essential commerce signals (pricing, inventory for top SKUs). Build API connections that feed a central warehouse. Prove value. Then expand.

Secure IT partnership early. Frame the business case around competitive advantage: faster decisions, cross-channel optimization, and establishing causality that traditional methods miss.

Address organizational readiness alongside technical build. Always-on incrementality changes how teams work. Who reviews lift data weekly? Who has authority to reallocate budgets? Build governance frameworks alongside technical infrastructure.

Run parallel systems during transition. Don’t shut down existing reporting while building new infrastructure. Run both in parallel for at least one quarter to validate accuracy and build stakeholder confidence.

Invest in testing discipline. Build capability around experiment design: appropriate sample sizes, sufficient test windows, pre-registered hypotheses, clear success criteria, and proper controls for non-media factors. Consider bringing in external expertise for the first few major tests.

Establish Finance alignment alongside technical build. Establish shared KPI definitions early: incremental ROAS (not platform-reported ROAS), new-to-brand percentage, contribution margin impact after all fees. Publishing test calendars showing which campaigns will run incrementality experiments creates stakeholder confidence.

Plan for 12-18 months. Measurement transformation takes time. Set realistic milestones: unified dashboard with commerce signals in Q1, automated weekly reviews in Q2, continuous optimization in Q3, cross-channel learning and predictive capabilities in Q4-Q1.

Against a backdrop where marketing complexity increases daily while CPG ad spending growth slows to 4.6%, measurement infrastructure determines who operates effectively and who drowns in data.

That makes always-on incrementality operationally necessary for brands managing modern commerce complexity.

Conclusion: Every working dollar should be part of an incrementality test

Remember, incrementality tests are not lab experiments. They are live campaigns doing their job in the market while you learn what’s actually working.

This is the fundamental shift to always-on incrementality. Unified causal measurement systems, continuous optimization capabilities, and cross-channel learning mechanisms create the foundation for always-on incrementality that can handle 15+ platforms, emerging AI touchpoints, and the dual complexity of commerce and media signals.

For CPG marketers managing complexity that traditional approaches can’t support, building this infrastructure means every dollar teaches you something while delivering results.

Always-on incrementality isn’t overhead. It’s how you make every dollar work harder while learning faster.

Skai’s Retail Media solutions enable marketers to plan, activate, and measure campaigns across 200+ retailers (including Amazon, Walmart, Target, and Instacart) as part of a broader commerce media strategy. AI-powered pacing, product intelligence, and keyword tools help teams meet shoppers across the journey and tie spend to sales with confidence. When market complexity demands unified measurement that connects retail media, search, and social with commerce signals into one causal framework, integrated platforms become essential for operating effectively across modern commerce’s fragmented landscape.

See how Skai transforms measurement from quarterly reporting to continuous optimization for leading CPG brands. Schedule a quick demo.

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