Brand Sentiment¶

Definition¶

Brand Sentiment measures the tone of opinions, feelings, and attitudes that customers, prospects, and the public express about your brand. It can be categorized as positive, neutral, or negative.

Description¶

Brand Sentiment goes beyond awareness and looks at how people feel about your brand, analyzing the tone of mentions, reviews, comments, and conversations across platforms. It reflects your brand’s emotional resonance and trustworthiness.

The relevance and interpretation of this metric shift depending on the model or product:

In B2B, it can signal customer satisfaction or dissatisfaction with product UX, support, or leadership
In consumer brands, it often reflects experience, perception, and cultural fit
In community-driven products, it’s a barometer for advocacy, backlash, or vibe shifts

A positive sentiment trend means your brand evokes trust and satisfaction. A negative shift may reflect product issues, tone-deaf messaging, or support friction. Segment sentiment by topic, channel, or customer tier to drive action and mitigate risk.

Brand Sentiment informs:

Strategic decisions, like brand positioning updates or crisis communications
Tactical actions, such as messaging pivots or quick customer response loops
Operational improvements, including feedback loops between support, product, and comms
Cross-functional alignment, by helping teams track emotional reactions, not just clicks

Key Drivers¶

These are the main factors that directly impact the metric. Understanding these lets you know what levers you can pull to improve the outcome

Product Quality and User Experience: Broken products = negative sentiment. Consistency and ease of use shape brand feelings more than logos do.
Support Experience and Resolution Tone: Fast, empathetic support boosts sentiment — even if the issue isn’t resolved right away. Cold or robotic service tanks perception.
Tone of Marketing and Messaging: Brands that feel authentic and human resonate more. Over-hype or jargon alienates.

Improvement Tactics & Quick Wins¶

Actionable ideas to optimize this KPI, from fast, low-effort wins to strategic initiatives that drive measurable impact.

If sentiment is skewing negative, analyze complaints by theme (e.g., pricing, bugs, UX) and prioritize fixes that affect trust the most.
Add post-interaction surveys with CSAT or NPS, giving you early signals before sentiment hits social channels.
Run a test using more empathetic, conversational copy in customer service macros, and monitor social response.
Refine your brand voice guidelines to emphasize clarity and humility, not just hype.
Partner with CX or community to publicly celebrate product updates that solve past user pain points, signaling progress.

Required Datapoints to calculate the metric
- Customer Feedback: Reviews, surveys, and ratings from platforms like Google Reviews, Trustpilot, or in-app feedback tools.
- Social Media Mentions: Comments, hashtags, and posts about your brand from platforms like Twitter, Facebook, or Instagram.
- Online Mentions: Mentions in news articles, forums, or blogs.
While sentiment is often qualitative, it can be categorized as positive, neutral, or negative through manual analysis or sentiment analysis tools, providing the data needed to calculate a sentiment score.:
```
- **Positive Mentions**: Total positive comments, reviews, or mentions.
- **Negative Mentions**: Total negative feedback or criticisms.
- **Total Mentions**: All mentions across channels during a specific period.
```
Example to show how the metric is derived

A tech company launches a new product and tracks sentiment:
- Positive Mentions: 500
- Negative Mentions: 100
- Neutral Mentions: 400
- Total Mentions: 1,000
- Brand Sentiment Score = (500 − 100) / 1,000 × 100 = 40%

Formula¶

Formula

\[ \mathrm{Brand\ Sentiment} = \left( \frac{\mathrm{Positive\ Mentions} - \mathrm{Negative\ Mentions}}{\mathrm{Total\ Mentions}} \right) \times 100 \]

Data Model Definition¶

How this KPI is structured in Cube.js, including its key measures, dimensions, and calculation logic for consistent reporting.

CustomerFeedbackSocialMediaMentionsOnlineMentions

cube('CustomerFeedback', {
  sql: `SELECT * FROM customer_feedback`,
  measures: {
    positiveMentions: {
      sql: `positive_mentions`,
      type: 'sum',
      title: 'Positive Mentions',
      description: 'Total positive comments, reviews, or mentions.'
    },
    negativeMentions: {
      sql: `negative_mentions`,
      type: 'sum',
      title: 'Negative Mentions',
      description: 'Total negative feedback or criticisms.'
    },
    totalMentions: {
      sql: `total_mentions`,
      type: 'sum',
      title: 'Total Mentions',
      description: 'All mentions across channels during a specific period.'
    }
  },
  dimensions: {
    id: {
      sql: `id`,
      type: 'string',
      primaryKey: true
    },
    feedbackDate: {
      sql: `feedback_date`,
      type: 'time',
      title: 'Feedback Date',
      description: 'The date when the feedback was received.'
    }
  }
})

cube('SocialMediaMentions', {
  sql: `SELECT * FROM social_media_mentions`,
  measures: {
    positiveMentions: {
      sql: `positive_mentions`,
      type: 'sum',
      title: 'Positive Mentions',
      description: 'Total positive comments, reviews, or mentions.'
    },
    negativeMentions: {
      sql: `negative_mentions`,
      type: 'sum',
      title: 'Negative Mentions',
      description: 'Total negative feedback or criticisms.'
    },
    totalMentions: {
      sql: `total_mentions`,
      type: 'sum',
      title: 'Total Mentions',
      description: 'All mentions across channels during a specific period.'
    }
  },
  dimensions: {
    id: {
      sql: `id`,
      type: 'string',
      primaryKey: true
    },
    mentionDate: {
      sql: `mention_date`,
      type: 'time',
      title: 'Mention Date',
      description: 'The date when the mention was made.'
    }
  }
})

cube('OnlineMentions', {
  sql: `SELECT * FROM online_mentions`,
  measures: {
    positiveMentions: {
      sql: `positive_mentions`,
      type: 'sum',
      title: 'Positive Mentions',
      description: 'Total positive comments, reviews, or mentions.'
    },
    negativeMentions: {
      sql: `negative_mentions`,
      type: 'sum',
      title: 'Negative Mentions',
      description: 'Total negative feedback or criticisms.'
    },
    totalMentions: {
      sql: `total_mentions`,
      type: 'sum',
      title: 'Total Mentions',
      description: 'All mentions across channels during a specific period.'
    }
  },
  dimensions: {
    id: {
      sql: `id`,
      type: 'string',
      primaryKey: true
    },
    mentionDate: {
      sql: `mention_date`,
      type: 'time',
      title: 'Mention Date',
      description: 'The date when the mention was made.'
    }
  }
})

Note: This is a reference implementation and should be used as a starting point. You’ll need to adapt it to match your own data model and schema

Positive & Negative Influences¶

Negative influences

Factors that drive the metric in an undesirable direction, often signaling risk or decline.
- Product Quality Issues: Frequent product defects or failures lead to negative customer experiences, directly decreasing Brand Sentiment.
- Poor User Experience: Complicated or frustrating user interfaces result in customer dissatisfaction, negatively impacting Brand Sentiment.
- Cold Support Experience: Robotic or unempathetic customer service interactions diminish customer trust and reduce Brand Sentiment.
- Delayed Issue Resolution: Slow response times in addressing customer issues lead to frustration and a decline in Brand Sentiment.
- Inauthentic Marketing Tone: Overly hyped or jargon-filled marketing messages create a disconnect with customers, lowering Brand Sentiment.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- High Product Quality: Consistently reliable and well-performing products enhance customer satisfaction, boosting Brand Sentiment.
- Seamless User Experience: Intuitive and user-friendly interfaces lead to positive customer interactions, increasing Brand Sentiment.
- Empathetic Support Experience: Warm and understanding customer service interactions build trust and improve Brand Sentiment.
- Quick Issue Resolution: Efficient handling of customer problems leads to satisfaction and a rise in Brand Sentiment.
- Authentic Marketing Tone: Genuine and relatable marketing messages resonate with customers, enhancing Brand Sentiment.

Involved Roles & Activities¶

Involved Roles

These roles are typically responsible for implementing or monitoring this KPI:

Customer Experience
Product Marketing (PMM)
Insights Manager
Activities

Common initiatives or actions associated with this KPI:

Advocacy
CSAT/NPS Programs
Sentiment Analysis Tools

Funnel Stage & Type¶

AAARRR Funnel Stage

This KPI is associated with the following stages in the AAARRR (Pirate Metrics) funnel:

Retention
Referral
Type

This KPI is classified as a Lagging Indicator. It reflects the results of past actions or behaviors and is used to validate performance or assess the impact of previous strategies.

Supporting Leading & Lagging Metrics¶

Leading

These leading indicators influence this KPI and act as early signals that forecast future changes in this KPI.
- Brand Awareness: Brand Awareness is a foundational signal that measures how familiar target audiences are with the brand. High brand awareness often precedes positive (or negative) shifts in Brand Sentiment, as it increases the volume and diversity of sentiment-bearing interactions. Tracking both metrics together creates a robust early warning system for shifts in public perception.
- Customer Loyalty: Customer Loyalty reflects the strength of ongoing customer relationships, advocacy, and repeat purchase intent. As loyal customers are more likely to share positive sentiment, changes in Customer Loyalty can signal upcoming shifts in Brand Sentiment, especially in response to product updates or campaigns.
- Net Promoter Score: Net Promoter Score (NPS) directly measures customer advocacy and willingness to recommend the brand. High or improving NPS is often a precursor to improved Brand Sentiment, while a decline can be an early indicator of deteriorating sentiment.
- Engagement Rate: Engagement Rate indicates how actively users interact with brand content or campaigns. Spikes or drops in engagement can signal shifts in audience sentiment before these are reflected in broader Brand Sentiment metrics.
- Content Engagement: Content Engagement measures the depth and quality of user interactions with branded content. Increases in positive engagement (e.g., shares, comments) often forecast a positive shift in Brand Sentiment, while negative engagement can signal risk.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Sentiment Analysis (Sales + Social): This metric analyzes the tone of conversations across sales and social channels, providing nuanced context and validation for changes observed in Brand Sentiment. Insights from this deeper analysis can inform calibration of Brand Sentiment models and trigger strategy adjustments.
- Customer Downgrade Rate: A rise in Customer Downgrade Rate can highlight issues with product fit or customer satisfaction that may not yet be reflected in Brand Sentiment, prompting a closer look at leading indicators and potential mitigation strategies.
- Branded Search Volume: Increasing or decreasing Branded Search Volume provides a demand-side confirmation of shifts in Brand Sentiment. Lower search volume following negative sentiment signals can recalibrate expectations for awareness campaigns.
- Social Shares: The volume and context of Social Shares act as a real-world amplification (or dampening) of Brand Sentiment. High positive shares reinforce positive sentiment, while viral negative shares may require early intervention.
- Brand Recall Score in ICP Surveys: This metric validates whether shifts in Brand Sentiment translate into actual improvements (or deterioration) in recall among key buyer segments. It can be used to recalibrate sentiment measurement models and marketing strategies.