What is Stakeholder Sentiment Analysis -Reach & Frequency Analysis

Here are concise, sourced definitions and measurement notes for two common strategic communications/outreach analyses: 1) Stakeholder Sentiment Analysis - Definition: A method to identify and quantify how stakeholders feel (positive, neutral, negative) about an organization, project, or issue, and how that sentiment shifts over time. - How it’s measured: Analyze tone and language in stakeholder inputs such as surveys, interviews/calls, emails, meeting notes, and social/media comments; assign sentiment scores and track trends over time by stakeholder segment and topic. - Typical metrics: Overall sentiment score and distribution (positive/neutral/negative), sentiment by stakeholder group and issue, change in sentiment over time, share of supportive vs. opposing comments, key themes tied to sentiment. - Data sources: Survey responses, call/meeting transcripts, emails, CRM/stakeholder logs, public comments, social media posts. - Source: Simply Stakeholders guide (describes sentiment analysis and data sources in stakeholder engagement contexts): https://simplystakeholders.com/resources/guides/stakeholder-analysis/ 2) Reach & Frequency Analysis (for communications/outreach/media) - Definition: Reach is the number (or percentage) of unique people exposed to a message at least once within a period. Frequency is the average number of exposures per reached person during that period. - How it’s measured: Use media platform reporting or audience models to estimate unique reach and average frequency for the target audience across channels; analyze trade-offs between expanding reach vs. increasing frequency. - Typical metrics: Reach (unique individuals or % of target), Frequency (avg exposures per reached person), Impressions (total exposures), Effective frequency (e.g., % of audience reached at 3+ exposures), Cost metrics (e.g., CPM, cost per incremental reach point). - Data sources: Ad platform analytics, media mix modeling, third-party measurement (e.g., panels), web/social analytics. - Sources: Think with Google (reach: unique people/households exposed; frequency: number of times exposed): https://www.thinkwithgoogle.com/marketing-strategies/video/thinking-beyond-reach-and-frequency/; WARC definition overview: https://www.warc.com/content/article/bestprac/what-we-know-about-reach-and-frequency/en-gb/107153 Notes for use in strategic communications - Combine sentiment analysis with reach/frequency to understand both breadth of exposure and depth of stakeholder support/opposition. - Track sentiment shifts after campaigns at matched levels of reach/frequency to infer message effectiveness. - Segment by stakeholder type to ensure critical audiences are both reached sufficiently and show positive sentiment trends.

How Stakeholder Sentiment Analysis Works in Practice

Stakeholder sentiment analysis translates qualitative feedback into structured insight your team can act on. The goal is to understand not only how people feel, but how that feeling shifts by audience, topic, and over time.

What good looks like

Clear taxonomy: Define positive, neutral, and negative at a practical level. Add topic tags (e.g., policy area, feature request, concern) so you can connect sentiment to specific issues.
Reliable sources: Pull from surveys, interviews or calls, email threads, CRM/stakeholder logs, meeting notes, public comments, and social or media posts. Consistency of capture matters more than volume.
Mixed methods: Combine automated scoring with human review on a rotating sample. Auto scoring gives scale. Human review keeps nuance and corrects drift.
Trend lens: Report overall sentiment distribution and changes by segment and topic. Elevate "why" by surfacing the themes most tied to shifts.

Core metrics to report

Overall sentiment distribution (positive/neutral/negative)
Sentiment by stakeholder segment and by issue/topic
Change over time (e.g., pre/post campaign windows)
Share of supportive vs. opposing comments
Top themes contributing to sentiment movement

Data handling and rigor

Sampling rules: If you cannot score every item, sample consistently each week so trends stay comparable.
Outlier management: Flag spikes from single events and annotate reports so leadership does not confuse events with trend.
Validation loop: Re-run a human-scored check monthly to recalibrate automated models.

Source alignment: This approach reflects stakeholder engagement guidance that defines sentiment analysis as identifying how stakeholders feel and how that changes over time, using inputs like surveys, calls, emails, and other communications (Simply Stakeholders Guide).

Reach, Frequency, and the Quality of Exposure

Reach and frequency describe how broadly a message lands and how often people who saw it are exposed during a defined period. They are essential to planning and diagnosing communications performance.

Working definitions

Reach: the number or percentage of unique people (or households) exposed at least once in the period.
Frequency: the average number of exposures per reached person in the period.
Impressions: total exposures (reach × frequency).

These definitions are consistent with industry guidance that defines reach as unique exposure and frequency as the number of times a person is exposed within a campaign window (Think with Google; WARC).

How teams measure

Platform analytics: Use native reporting to estimate unique reach and average frequency by channel.
Cross-channel view: De-duplicate audiences when possible. If you cannot, report channel-level reach transparently and avoid double counting in roll-ups.
Effective frequency: Track the percentage of your audience at 3+ exposures (or your validated threshold) as a quality-of-exposure indicator.
Cost lens: Pair media metrics with CPM and cost per incremental reach point to see trade-offs between expanding reach and adding frequency.

Diagnosing trade-offs

Under-delivery risk: Low reach with high frequency often signals budget concentration and limited breadth of exposure.
Wastage risk: Very high frequency with flat outcomes points to creative wear-out or poor audience fit.
Optimization move: Shift spend to expand unique reach until marginal returns on new reach fall below returns on added frequency.

Applying Both Analyses Together: A Practical Mini-Playbook

Pairing sentiment with reach and frequency reveals whether messages are both seen and well-received by the audiences that matter.

Mini-playbook

Design the measurement window: Define pre, in-flight, and post periods for each campaign or key announcement.
Set your segmentation: Stakeholder type, geography, and priority topics. Plan to report sentiment and exposure metrics for each segment.
Collect consistently: Establish weekly ingestion from surveys, meeting notes, emails, CRM logs, and public comments. Capture media delivery by channel with unique reach and average frequency.
Link exposure to opinion: For the same period, compare sentiment shift against reach/frequency by segment. Look for patterns like positive movement at moderate frequency or stagnation despite heavy frequency.
Set thresholds: Choose an effective-frequency target (e.g., % reached 3+ times) and a minimum reach threshold for priority segments. Validate with past campaigns.
Act on findings: If sentiment improves only where frequency ≥ threshold, prioritize reinforcement. If sentiment is flat at high frequency, adjust message or audience rather than pushing more impressions.
Report for decisions: Create a standard view: reach, frequency, impressions, effective-frequency %, sentiment distribution, net sentiment change, and the top themes linked to movement.

Quality guardrails

Causality caution: Treat relationships as directional unless you have controls or matched comparisons.
Context logs: Annotate events that could explain sentiment swings independent of communications.
Stakeholder-first: When reach is sufficient but sentiment lags, revisit value propositions and message clarity before increasing spend.

Stakeholder Sentiment Analysis -Reach & Frequency Analysis