PR Evaluation: SAS Marketing Optimization in 2026

For too long, Public Relations (PR) has struggled with proving its tangible impact, often relegated to qualitative reports and anecdotal evidence. But what if we could quantify PR’s contribution with the same rigor as paid media? Marketing mix modeling for holistic PR evaluation offers precisely that, transforming PR from an art into a measurable science that directly impacts the bottom line.

1. Define Your Business Objectives and Key Performance Indicators (KPIs)

Before you even think about data, you need to know what you’re trying to achieve. This seems obvious, but many teams jump straight into collecting media mentions without connecting them to actual business goals. Are you aiming for increased sales? Enhanced brand perception leading to higher customer lifetime value? Improved lead generation? Your objectives dictate which business outcome metrics you’ll track. For example, if the goal is direct sales, then weekly or monthly revenue data is paramount. If it’s brand consideration, look at website direct traffic, branded search queries, or even qualitative brand health surveys over time. I had a client last year, a regional craft brewery in Decatur, Georgia, who initially just wanted “more media hits.” We pushed them to define what those hits should do. They settled on increasing taproom foot traffic and online merchandise sales. This clarity was absolutely vital for our modeling efforts later.

Pro Tip: Be incredibly specific with your KPIs. Instead of “increase sales,” aim for “increase Q3 2026 e-commerce sales by 10% compared to Q3 2025.” This specificity makes it easier to find correlating data.

2. Gather Comprehensive Data: PR Inputs and Business Outcomes

This is where the rubber meets the road. For marketing mix modeling (MMM) to work, you need a substantial amount of historical data, typically two to three years’ worth, on both your PR activities and your business outcomes. Data granularity is key; weekly or even daily data is far superior to monthly.

• PR Inputs: This includes not just the volume of media mentions, but also their quality (e.g., tier-1 vs. tier-2 publications), sentiment (positive, neutral, negative), key message pull-through, and estimated audience reach. Tools like Meltwater or Cision can aggregate media mentions and provide sentiment analysis. You’ll also need to track your PR team’s efforts, such as the number of pitches sent, events hosted, or influencer collaborations. Quantify everything you can. For instance, I always advise clients to assign a “quality score” to each mention based on its prominence and alignment with key messages.
• Marketing Inputs (Non-PR): Don’t forget other marketing efforts! Your model needs to account for paid advertising (spend on Google Ads, Meta Ads, programmatic display), organic search performance, email marketing campaigns, and even seasonal promotions. This helps isolate PR’s specific contribution.
• Business Outcomes: This is your dependent variable. Examples include weekly sales revenue, website unique visitors, lead generation volume, app downloads, or even stock price fluctuations if that’s your objective.
• External Factors: Critical for accurate modeling are external variables that can influence your outcomes but aren’t controlled by your marketing. Think seasonality, competitor activity (e.g., major competitor product launches), economic indicators, or even local news events.

Common Mistakes: Overlooking the need for a control group or baseline data is a huge error. Without understanding what your business outcomes would be without specific PR interventions, it’s impossible to truly attribute impact. Also, inconsistent data formatting across different sources can derail the entire process. Invest time in data cleaning and standardization.

Screenshot Description: Imagine a meticulously organized Excel spreadsheet. Column A: Date (e.g., 2024-01-01). Column B: Weekly Revenue. Column C: PR Mentions Volume (Tier 1). Column D: PR Mentions Sentiment Score. Column E: Google Ads Spend. Column F: Competitor Activity Index. Each row represents a week of data spanning several years.

3. Select Your Modeling Approach and Tools

Marketing mix modeling typically employs econometric techniques, primarily multiple regression analysis. While you can technically build a basic model in Excel, for robust, scalable, and statistically sound analysis, specialized tools are essential. We prefer using open-source solutions like R, specifically the ‘Robyn’ package developed by Meta. It’s powerful, flexible, and allows for sophisticated features like saturation curves and carryover effects.

Alternatively, commercial platforms like SAS Marketing Optimization or Nielsen’s Unified Measurement offer comprehensive solutions, though often at a higher price point. For smaller businesses, a well-structured Python script with libraries like statsmodels or scikit-learn can also work. I lean towards Robyn because it’s transparent, community-supported, and avoids the “black box” feel of some proprietary solutions. It truly empowers marketers to understand the underlying mechanics.

Pro Tip: Don’t try to reinvent the wheel. Start with established modeling frameworks. Robyn, for instance, provides excellent documentation and examples that guide you through setting up your data and running your first model. Focus on understanding the assumptions and limitations of your chosen method.

4. Build and Calibrate Your Marketing Mix Model

This is the technical core. You’ll feed your cleaned historical data into your chosen tool. Here’s a simplified breakdown of the process using a conceptual framework applicable to Robyn:

1. Data Import and Preprocessing: Load your CSV or database into R. Ensure all variables are correctly formatted (e.g., numeric for spend, categorical for seasonality flags).
2. Define Adstock and Saturation: PR, like advertising, has both a “carryover” effect (its impact lingers after the initial exposure) and a “saturation” point (additional exposure yields diminishing returns). Robyn allows you to define these. For PR, we often see longer adstock rates than for paid ads, meaning a positive media hit can influence perception for weeks or even months. You’ll specify parameters for adstock_decay (how quickly impact fades) and saturation_hill (the shape of the diminishing returns curve).
3. Specify Model Variables: Designate your dependent variable (e.g., ‘weekly_sales’) and your independent variables (all your PR inputs, marketing inputs, and external factors).
4. Run the Model: Execute the modeling function. Robyn, for example, uses a Bayesian approach to explore multiple model specifications and identify the best-fitting ones. You might run hundreds or thousands of iterations to find optimal parameter values.
5. Validate the Model: This is critical. Does your model accurately predict past outcomes? A common method is to hold out a portion of your data (e.g., the last 10-20 weeks) and see how well your model predicts those weeks. Look at metrics like R-squared (how much variance the model explains) and Mean Absolute Percentage Error (MAPE) to assess accuracy. A good MAPE for MMM is often below 15%, but this varies by industry.

We ran into this exact issue at my previous firm when modeling for a fintech startup in Midtown Atlanta. Their initial data only went back 18 months. We had to explain that while we could try to model it, the statistical significance and predictive power would be limited compared to a 2-3 year dataset. They eventually extended their data collection period, and the resulting model was far more robust.

Screenshot Description: A screenshot from an R console or RStudio, showing the output of Robyn’s robyn_run() function. It would display progress bars, iteration counts, and eventually summary statistics for the model, including R-squared values for various model iterations.

5. Interpret Results and Calculate PR ROI

Once your model is built and validated, it’s time to extract insights. The model will provide coefficients for each of your PR and marketing inputs. These coefficients tell you the marginal impact of each channel on your business outcome. For example, a coefficient of 0.5 for “Tier 1 Media Mentions” might mean that every additional Tier 1 mention contributes $0.50 to your weekly sales, all else being equal.

Key metrics to focus on:

• Contribution: The absolute amount each channel contributed to your overall business outcome (e.g., “PR contributed $500,000 to sales last quarter”).
• Return on Investment (ROI): For PR, this is often calculated as (Revenue Attributed to PR – PR Spend) / PR Spend. If you spend $10,000 on a PR campaign and it’s attributed $50,000 in sales, your ROI is 400%.
• Elasticity: This measures the percentage change in your outcome for a one percent change in your input. A higher elasticity means a channel is more responsive.
• Marginal Return: How much additional return do you get from spending one more dollar on a specific PR activity? This is invaluable for budget allocation.

This is where the “holistic” part of PR evaluation really shines. You can now directly compare the ROI of your PR efforts to your paid search campaigns, social media ads, or email marketing. It’s not just about “how many articles did we get?” anymore; it’s “how much revenue did those articles generate?”

Common Mistakes: Misinterpreting correlation as causation. While MMM helps in attribution, it’s still a statistical model. Also, focusing solely on overall ROI without considering diminishing returns or the long-term brand equity built by PR is a mistake. Sometimes, a lower direct ROI for a specific PR activity might be acceptable if it significantly boosts brand awareness, which then fuels other channels.

Screenshot Description: A bar chart generated from Robyn’s output, showing the “contribution share” of different marketing channels to total sales. One bar would be labeled “PR Efforts,” showing its percentage contribution alongside “Paid Search,” “Social Media,” etc. Another chart might show the ROI for each channel.

6. Iterate, Refine, and Inform Strategy

Marketing mix modeling isn’t a one-and-done project. It’s an ongoing process. Markets change, consumer behavior evolves, and your PR strategies adapt. Therefore, your MMM needs regular recalibration, typically quarterly or bi-annually, with fresh data.

Use the insights to make informed strategic decisions:

• Budget Allocation: Shift resources to PR activities with higher ROI and marginal returns. If targeted media relations in industry publications consistently outperform general news releases, allocate more budget there.
• Campaign Optimization: Understand which types of stories, media outlets, or influencer collaborations yield the best results. For our brewery client, the model clearly showed that local food blogger features drove significantly more taproom visits than general business press mentions, leading them to reallocate their PR efforts.
• Forecasting: Use the model to forecast future outcomes based on planned PR and marketing spend. “If we increase our influencer outreach budget by 20%, what’s the projected impact on leads?”
• Demonstrate Value: Finally, and perhaps most importantly for PR professionals, you can confidently present data-backed evidence of PR’s contribution to leadership and stakeholders. This transforms PR from a cost center into a clear revenue driver.

This iterative process ensures your PR strategy is always data-driven and aligned with your core business objectives. It’s a continuous feedback loop that fosters growth and accountability. My strong opinion here is that any PR agency or in-house team not adopting MMM by 2027 will be significantly disadvantaged, unable to compete with those who can prove their worth in hard numbers.

Marketing mix modeling provides an invaluable framework for evaluating PR’s true impact, moving beyond simple impressions to demonstrate clear, quantifiable contributions to business objectives. By meticulously collecting data, applying rigorous analytical models, and continuously refining your approach, you can transform PR from a perceived cost into a recognized revenue driver, proving its essential role in holistic marketing success.