Sentiment Analysis

Definition

Sentiment Analysis is the process of analyzing text, speech, or other data to determine the emotional tone behind it. It categorizes feedback as positive, neutral, or negative, providing insights into how customers feel about a product, service, or brand.

Business Context

Sentiment Analysis is a powerful metric that uses natural language processing (NLP), machine learning, and text analytics to interpret how your customers feel—whether it’s delight, frustration, curiosity, or confusion.

It captures customer sentiment across:

• Support tickets and reviews (e.g., “This app is amazing!” or “Support is slow.”)
• Social media (shoutouts, complaints, or product mentions)
• Survey responses, like NPS feedback or CSAT verbatims
• Community forums and third-party platforms (e.g., Reddit, G2, Trustpilot)

Sentiment can be analyzed at different levels:

• Document-level: Overall tone (positive, neutral, negative)
• Sentence-level: Identifying key emotional signals
• Aspect-level: Tying emotion to specific features or topics (e.g., “Pricing is confusing” vs. “Support is excellent”)

A high positive sentiment trend signals strong brand love, satisfaction, or advocacy readiness. Negative sentiment spikes, on the other hand, may reveal UX issues, pricing friction, or service breakdowns.

How to use Sentiment Analysis:

• 🔍 Strategically:

  • Track brand perception over time

  • Feed into positioning and messaging refinement

  • Identify promoters or detractors for referral or win-back programs

• 🛠️ Tactically:

  • Detect early signals of product or support pain

  • Monitor launches or campaigns for real-time customer reactions

  • Address emerging issues before they snowball

• 🤝 Cross-functionally:

  • Share themes with Product to influence the roadmap

  • Enable CS with proactive outreach triggers

  • Give PMM real-time proof of what’s resonating—or not

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

• NPS, CSAT, and Feedback Loops: Regularly collected scores provide clear directional sentiment.
• Support Interaction Tone: Language in tickets or chats can highlight hidden dissatisfaction.
• Product Experience Quality: Bugs, slow performance, or confusing UX quickly generate negative sentiment.

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 unclear, introduce lightweight feedback prompts across the product (“How are we doing?”).
• Add sentiment tagging to support and success conversations using AI or keyword detection.
• Run proactive outreach to low-sentiment users offering help, not just surveys.
• Refine your feedback loop — make “you said, we did” a recurring moment in customer comms.
• Partner with product and CX to monitor sentiment trends by feature, segment, and role.

Related Activities

Activities commonly tied to improving or operationalizing this KPI.

Activity	Description
Brand Positioning	Brand Positioning involves defining and communicating the unique value proposition of a product or brand within its target market. It gives teams a clear plan for where to focus, how to sequence work, and what to measure. Relevant KPIs include Brand Awareness and Market Share.
Community Building	Community Building focuses on strategically nurturing meaningful connections among customers, prospects, partners, and internal teams. It helps teams translate strategy into repeatable execution. Relevant KPIs include Customer Loyalty and Daily Active Users.
Customer Advocacy	Customer Advocacy is a strategic process focused on building strong relationships with satisfied customers to amplify their positive experiences, strengthen loyalty, and inspire them to share their success stories. It helps teams translate strategy into repeatable execution. Relevant KPIs include Customer Loyalty and Customer Referral Rate.
Social Listening	Social Listening involves continuously tracking, analyzing, and interpreting online conversations, reviews, and social media activity related to a brand, product, competitors, or broader industry trends. It helps teams translate strategy into repeatable execution. Relevant KPIs include Brand Mentions and Sentiment Analysis.
VoC Analysis	VoC Analysis involves systematically collecting, analyzing, and interpreting feedback, preferences, pain points, and expectations from customers across multiple touchpoints. It turns signals into decisions, interventions, and measurable follow-up. Relevant KPIs include Sentiment Analysis.

Required Datapoints

• Customer Feedback Sources: Reviews, social media posts, survey responses, chat transcripts, etc.
• Sentiment Scores: Categorized as positive, negative, or neutral (sometimes scored on a numerical scale).
• Volume of Feedback: The number of data points analyzed over time.

Example

An e-commerce brand conducts Sentiment Analysis on social media comments about a new product launch:

• 60% Positive
• 25% Neutral
• 15% Negative

Formula

$$\mathrm{Sentiment\ Analysis} = \left( \frac{\mathrm{Positive\ Sentiments} - \mathrm{Negative\ Sentiments}}{\mathrm{Total\ Sentiments}} \right) \times 100$$

Positive & Negative Influences

Negative Influences

• Support Interaction Tone: Negative language or tone in customer support interactions often correlates with negative sentiment, as it reflects dissatisfaction or frustration.
• Product Experience Quality: Issues such as bugs, slow performance, or confusing user interfaces lead to negative sentiment as they directly impact user satisfaction.
• NPS: A low Net Promoter Score indicates a likelihood of negative sentiment, as it reflects customers’ unwillingness to recommend the product or service.
• CSAT: Low Customer Satisfaction scores are directly linked to negative sentiment, as they indicate that customer expectations are not being met.
• Feedback Loops: Negative feedback collected through regular feedback loops can indicate underlying issues that contribute to negative sentiment.

Positive Influences

• Support Interaction Tone: Positive language or tone in customer support interactions can enhance positive sentiment, as it reflects a satisfactory resolution and customer care.
• Product Experience Quality: High-quality product experiences, characterized by smooth performance and intuitive design, foster positive sentiment by meeting or exceeding customer expectations.
• NPS: A high Net Promoter Score is associated with positive sentiment, as it indicates customers’ willingness to recommend the product or service.
• CSAT: High Customer Satisfaction scores are indicative of positive sentiment, as they show that customer expectations are being met or exceeded.
• Feedback Loops: Positive feedback collected through regular feedback loops can reinforce positive sentiment by highlighting areas of success and satisfaction.

Funnel Stage & Type

AAARRR Funnel Stage

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

• Retention

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.

Ownership

Primary Owner

This role is directly accountable for the KPI and is expected to drive progress and decisions around it.

• Customer Experience

Secondary Owners

These roles contribute directly to performance and typically partner on execution, reporting, or optimization.

• Insights Manager
• Product Management (PM)

Supporting Leading & Lagging Metrics

Leading

These leading indicators influence this KPI and act as early signals that forecast future changes in this KPI.

• Net Promoter Score: Net Promoter Score is a leading indicator of overall customer sentiment and loyalty. High NPS scores typically forecast more positive sentiment analysis results, while drops in NPS can foreshadow a rise in negative sentiment. This relationship helps anticipate shifts in customer mood before they are broadly reflected in sentiment analysis data.
• Brand Sentiment: Brand Sentiment provides real-time, qualitative data about how customers feel about the brand before these perceptions become evident in aggregate sentiment analysis. A surge in negative brand sentiment often precedes negative trends in sentiment analysis, making it a critical early warning indicator.
• Customer Satisfaction Score: Customer Satisfaction Score (CSAT) captures immediate reactions to specific experiences, serving as an early predictor for the tone and direction of broader sentiment analysis. Declines in CSAT scores usually translate into more negative sentiment trends over time.
• Customer Loyalty: Customer Loyalty measures customers’ long-term commitment and repeat engagement, which strongly influences overall sentiment. Lower loyalty often leads to more negative sentiment in feedback, while high loyalty is associated with more positive sentiment analysis outcomes.
• Brand Awareness: Brand Awareness reflects how well the brand is recognized and perceived by the target audience. Changes in brand awareness, especially if driven by negative publicity or loss of mindshare, can lead to shifts in sentiment analysis as customer perceptions evolve.

Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.

• Customer Churn Rate: Customer Churn Rate confirms the impact of negative sentiment identified in sentiment analysis. A rise in negative sentiment typically precedes increases in churn, making churn rate a downstream metric that validates sentiment trends.
• Conversion Rate: Conversion Rate quantifies the business implications of sentiment trends. Negative sentiment detected in sentiment analysis often leads to declining conversion rates, showing how customer feelings translate into actions.
• Revenue Churn Rate: Revenue Churn Rate measures the financial effect of sentiment-driven customer loss. Negative sentiment identified in sentiment analysis is frequently followed by increased revenue churn, confirming the broader business impact.
• Customer Downgrade Rate: Customer Downgrade Rate illustrates how negative sentiment translates into customer behavior, such as reducing subscription levels. This metric amplifies sentiment analysis findings by quantifying the degree of dissatisfaction.
• Net Revenue Retention: Net Revenue Retention captures the overall retention and expansion health of the customer base. Drops in sentiment analysis scores often lead to reduced retention, making this metric a lagging indicator that confirms and quantifies the lasting business effects of sentiment trends.

Data Model Definition

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

cube('CustomerFeedback', {
  sql: `SELECT * FROM customer_feedback`,

  measures: {
    feedbackVolume: {
      sql: `id`,
      type: 'count',
      title: 'Feedback Volume',
      description: 'The number of feedback entries analyzed over time.'
    },
    positiveFeedback: {
      sql: `CASE WHEN sentiment_score = 'positive' THEN 1 ELSE 0 END`,
      type: 'sum',
      title: 'Positive Feedback',
      description: 'The count of feedback entries categorized as positive.'
    },
    negativeFeedback: {
      sql: `CASE WHEN sentiment_score = 'negative' THEN 1 ELSE 0 END`,
      type: 'sum',
      title: 'Negative Feedback',
      description: 'The count of feedback entries categorized as negative.'
    },
    neutralFeedback: {
      sql: `CASE WHEN sentiment_score = 'neutral' THEN 1 ELSE 0 END`,
      type: 'sum',
      title: 'Neutral Feedback',
      description: 'The count of feedback entries categorized as neutral.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true,
      title: 'ID',
      description: 'Unique identifier for each feedback entry.'
    },
    feedbackSource: {
      sql: `source`,
      type: 'string',
      title: 'Feedback Source',
      description: 'The source of the feedback, such as reviews, social media posts, etc.'
    },
    sentimentScore: {
      sql: `sentiment_score`,
      type: 'string',
      title: 'Sentiment Score',
      description: 'The sentiment score categorized as positive, negative, or neutral.'
    },
    createdAt: {
      sql: `created_at`,
      type: 'time',
      title: 'Created At',
      description: 'The time when the feedback was created.'
    }
  }
});

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