Marketer’s Paradox: How to Find the Right Method for Omnichannel Marketing Measurement

The P.A.C.E. Framework: A Smarter Way to Make Measurement Decisions

The P.A.C.E framework helps marketers align measurement techniques with the nature of the decision at hand, ensuring the right tool is applied in the right context. Marketing decisions typically fall into four distinct types, each requiring a different measurement approach. Here's how the framework maps to those decision levels.

Figure 2: P.A.C.E Framework mapped to Measurement Decisions
Source: Axtria Inc.

• Strategic-Level Decisions are made by marketers as they plan large-scale budgets across online and offline channels and as they seek to understand long-term ROI. The MMM technique plays a central role, as it is a top-down statistical model using historical data to quantify the impact of marketing channels and external factors, like seasonality, pricing, and weather, on outcomes like sales.
  
  Typical questions marketers ask at this level include: 
  
  Q: “How should I split my $10M budget across channels for next quarter?”
  Q: “How do I attribute Rx lift to different non-personal promotions like banner ads, speaker programs, and field visits across my oncology portfolio?”
  
  This is where MMM can provide holistic insight by controlling for confounders like seasonality, competitive noise, and formulary access.
  
  Applying the P.A.C.E. Lens to Strategic-level Decisions:
  • Purpose: Long-term ROI planning and resource allocation
  • Access to data: Aggregate and historical (e.g., sales + media spend).
  • Cadence or Refresh cycle: Quarterly or annual planning, aligning with strategic planning cycles and budget decisions
  • Evidence: Primarily causal and driven by high statistical rigor/models.
  • Actionability: Long-term
  • Strengths of the technique: Holistic view, offline + online, doesn’t require user-level data.
  • Limitations: Slow to update, not designed for in-flight campaign optimizations or tactical day-to-day decision-making.
    
    
• Journey-Level Decisions come into play when marketers need to optimize the digital mix and understand how different channels work together throughout a customer’s journey to assess each touchpoint’s contribution to conversions accurately. This dimension is essential in healthcare and pharma contexts, where patient and HCP customer journeys require distinct signals and privacy-safe methodologies. 
  
  Techniques that come in handy include MTA and probabilistic attribution (in cookie-less environments), which are ideal for tracking and assigning credit across digital touchpoints (e.g., email, search, social media). 
  
  In the cookie-less, privacy-first world, these methods help marketers track the relative contribution of non-personal digital tactics (emails, search, social media, display) without compromising compliance. Maintaining customer and patient privacy is crucial in therapeutic areas like oncology or rare diseases, where HCP engagement paths are complex, and each touchpoint may play a supportive versus final role in driving script behavior.
  
  Example Decisions: 
  
  Q: “Are my paid social ads assisting conversions or stealing credit from organic?”
  Q: “How do emails and banner ads work in tandem to drive HCP engagement post-field visit?”
  
  To answer these questions, marketers must think of MTA-like assessments.
  
  Applying the P.A.C.E. Lens to journey-level Decisions:
  • Purpose: To optimize the efficiency of digital channels, particularly in the mid-funnel, and refine retargeting strategies
  • Access: Cookie- or ID-level user interaction data
  • Cadence: Typically, weekly to monthly, depending on campaign activity
  • Evidence: Directional attribution not always causal
  • Actionability: Short- to mid-term (focusing on immediate adjustments in strategy)
  • Strengths: Granular, cross-touchpoint insights can inform budget shifts
  • Limitations: Often digital-only, cookie- or ID-dependent, vulnerable to privacy regulations

• Campaign-Level Decisions become relevant when marketers need to evaluate the effectiveness of specific campaigns over a few weeks/months and optimize performance, either for immediate action or to prove impact.
  
  Common questions include:
  
  Q: “Which ad creative is driving the lowest cost per acquisition (CPA) today?” 
  Q: “Would we have achieved these conversions even without last month’s campaign? How can we measure marketing campaign effectiveness?” 
  Q: “Did rep-triggered emails after a field call lead to higher new-to-brand prescription lift?”
  
  Applying the P.A.C.E. Lens to Campaign-level Decisions:
  • Purpose: To assess a specific campaign’s performance and optimize it in real time.
  • Access: Real-time or near-real-time platform data.
  • Cadence: Two choices – (a) daily to weekly with CPA optimizations; (b) weekly to monthly with causal studies and deeper analysis
  • Evidence: can vary from observational dashboards to structured experiments. The ideal techniques for campaign-level decisions often revolve around incrementality testing and campaign performance analytics

    Campaign Performance Analytics – CPA (Observational)	Incrementality Testing / Campaign Lift Analysis (Causal)
    Pulls real-time data from platforms to help manage active campaigns Best for on-the-fly optimizations, creative, and targeting tweaks Techniques include real-time performance monitoring through KPI dashboards with metrics like CTR, CPA, ROAS Strengths: Fast feedback for creatives and targeting Limitations: Doesn’t show causality, correlation only	Experimental setups with test/control groups to isolate the actual effect of a campaign Best for proving a campaign’s actual business value  Techniques include holdout experiments, geo tests, and brand lift studies Strengths: Causal, privacy-safe, excellent for stakeholder confidence Limitations: Requires careful,  clean       setup and may not scale for every test
    CTR: Click Through Rate   |   CPA: Cost Per Acquisition   |   ROAS: Return on Ad Spend
    Note: In the pharma world, all incrementality tests and touchpoint attribution assessments must comply with promotional regulations, content approval processes, and field force coordination.

  • Actionability: Immediate, with direct adjustments to creatives, audiences, and channels.
  • Strengths: Fast and clear readouts, which are strong for testing creatives, channels, or targeting.
  • Limitations: Needs scaled and controlled setup; findings may not generalize

• Future Value Decisions are made by marketers when they focus on optimizing long-term business health and sustained revenue, where techniques like customer lifetime value modeling and predictive analytics become indispensable. These methods leverage behavioral and transactional data to forecast revenue per user and optimize acquisition channels to drive future value.
  
  Marketers turn to future value modeling and measurement techniques when seeking answers to questions like:
  
  Q: “Which channels attract customers who will stay and spend over the next 6+ months?”
  Q: “Can we predict future Rx volume by analyzing early content interaction?”
  
  Predictive models surface those insights and help us act on them.
  
  Applying the P.A.C.E. Lens to Future-value Decisions:
  • Purpose: Maximize long-term customer value, enhance retention, nurture loyalty of high-value customers
  • Access: CRM systems and historical behavioral data
  • Cadence: Quarterly or Semi-annual with long-term payoff
  • Evidence: Statistically robust and forecast-ready
  • Actionability: Long-term customer strategies
  • Strengths: Drives business model evolutions (loyalty, etc.)
  • Limitations: Dependent on robust historical and behavioral data; dependent on assumptions about churn, repeat rate, etc.

These techniques are particularly effective when business shifts focus toward retention and strategic acquisition. Predictive analytics delivers actionable insights that shape long-term customer strategies when marketers ask, “Which channels bring in customers who’ll be valuable 6+ months from now?”

Together, these scenarios – from strategic budget planning to in-market optimizations – illustrate marketers’ diverse measurement needs. Choosing the fit-for-purpose measurement strategy is made easy with the P.A.C.E framework.