Sentiment Analysis (Sales + Social)¶

Definition¶

Sentiment Analysis (Sales + Social) measures the tone (positive, neutral, negative) of feedback and conversations related to your brand across sales call transcripts and social media platforms. It helps track brand perception, objection patterns, and positioning effectiveness.

Description¶

Sentiment Analysis (Sales + Social) is a key indicator of brand health, emotional resonance, and market perception, analyzing language and tone in both private and public spaces to gauge how people feel about your product, pricing, and positioning.

This plays out differently across channels:

In sales calls, it surfaces patterns like confusion, excitement, or pricing pushback.
In social media, it reflects community buzz, complaints, and customer love.
In reviews or forums, it reveals recurring themes of praise—or pain.

A positive trend in sentiment suggests strong narrative clarity and product love. A negative spike can signal PR issues, competitive threats, or broken experiences. By segmenting by channel, feature, or persona, you can uncover brand risks or advocacy opportunities.

Sentiment Analysis (Sales + Social) informs:

Strategic messaging, across GTM, brand, and product teams
Tactical adjustments, in campaign tone or sales rebuttals
CX insights, to feed into roadmap and success playbooks

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

Social Listening and Keyword Monitoring: Positive mentions and tone trends reflect brand health and buzz.
Sales Call Transcription Analysis: Tools like Gong/Chorus can extract emotional tone and objection triggers.
Competitive Positioning in Public Spaces: If you're seen as second-best, it shows up in tone.

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 mixed, use call AI to flag tone shifts or friction in sales objections.
Add a social listening tool (e.g., Brandwatch, Sprout) to track mentions by ICP segment.
Run reputation campaigns that highlight product wins, customer quotes, and community success.
Refine public responses — turn complaints into customer wins in comment threads or DMs.
Partner with brand and social teams to monitor sentiment across platforms and share insights in GTM syncs.

Required Datapoints to calculate the metric
- Transcribed sales call data (Gong, Chorus, etc.)
- Social listening platform output (Brandwatch, Sprout, etc.)
- Sentiment tagging (automated or manual)
- Timeframe + channels tracked
Example to show how the metric is derived

1,000 brand mentions and call excerpts analyzed in Q1 540 were positive, 300 neutral, 160 negative Net Sentiment Score = 540 – 160 = +38

Formula¶

Formula

\[ \begin{align*} \mathrm{Positive\ Mentions\ Percentage} &= \left( \frac{\mathrm{Positive\ Mentions}}{\mathrm{Total\ Mentions}} \right) \times 100 \\ \mathrm{Neutral\ Mentions\ Percentage} &= \left( \frac{\mathrm{Neutral\ Mentions}}{\mathrm{Total\ Mentions}} \right) \times 100 \\ \mathrm{Negative\ Mentions\ Percentage} &= \left( \frac{\mathrm{Negative\ Mentions}}{\mathrm{Total\ Mentions}} \right) \times 100 \\ \mathrm{Net\ Sentiment\ Score} &= \mathrm{Positive\ Mentions\ Percentage} - \mathrm{Negative\ Mentions\ Percentage} \end{align*} \]

Data Model Definition¶

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

SalesCallTranscriptsSocialMediaFeedback

cube('SalesCallTranscripts', {
  sql: `SELECT * FROM sales_call_transcripts`,
  measures: {
    positiveSentimentCount: {
      sql: `positive_sentiment`,
      type: 'count',
      title: 'Positive Sentiment Count',
      description: 'Count of positive sentiment tags in sales call transcripts.'
    },
    neutralSentimentCount: {
      sql: `neutral_sentiment`,
      type: 'count',
      title: 'Neutral Sentiment Count',
      description: 'Count of neutral sentiment tags in sales call transcripts.'
    },
    negativeSentimentCount: {
      sql: `negative_sentiment`,
      type: 'count',
      title: 'Negative Sentiment Count',
      description: 'Count of negative sentiment tags in sales call transcripts.'
    }
  },
  dimensions: {
    id: {
      sql: `id`,
      type: 'string',
      primaryKey: true
    },
    callDate: {
      sql: `call_date`,
      type: 'time',
      title: 'Call Date',
      description: 'Date of the sales call.'
    },
    channel: {
      sql: `channel`,
      type: 'string',
      title: 'Channel',
      description: 'Channel through which the sales call was conducted.'
    }
  }
})

cube('SocialMediaFeedback', {
  sql: `SELECT * FROM social_media_feedback`,
  measures: {
    positiveSentimentCount: {
      sql: `positive_sentiment`,
      type: 'count',
      title: 'Positive Sentiment Count',
      description: 'Count of positive sentiment tags in social media feedback.'
    },
    neutralSentimentCount: {
      sql: `neutral_sentiment`,
      type: 'count',
      title: 'Neutral Sentiment Count',
      description: 'Count of neutral sentiment tags in social media feedback.'
    },
    negativeSentimentCount: {
      sql: `negative_sentiment`,
      type: 'count',
      title: 'Negative Sentiment Count',
      description: 'Count of negative sentiment tags in social media feedback.'
    }
  },
  dimensions: {
    id: {
      sql: `id`,
      type: 'string',
      primaryKey: true
    },
    feedbackDate: {
      sql: `feedback_date`,
      type: 'time',
      title: 'Feedback Date',
      description: 'Date of the social media feedback.'
    },
    platform: {
      sql: `platform`,
      type: 'string',
      title: 'Platform',
      description: 'Social media platform where the feedback was posted.'
    }
  }
})

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.
- Negative Social Media Mentions: An increase in negative mentions on social media platforms can lead to a decline in overall sentiment, as these platforms are highly visible and influential in shaping public perception.
- Objection Patterns in Sales Calls: Frequent objections during sales calls, as identified through transcription analysis, can indicate dissatisfaction or misalignment with customer expectations, negatively impacting sentiment.
- Competitive Positioning: Being perceived as second-best or lagging behind competitors in public discussions can lead to a negative sentiment as it affects brand prestige and customer confidence.
- Negative Customer Reviews: A surge in negative reviews on platforms like Google or Yelp can directly decrease sentiment as they are often referenced by potential customers.
- Crisis Events: Events such as product recalls or public relations crises can cause a sharp decline in sentiment due to increased negative attention and scrutiny.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Positive Social Media Mentions: An increase in positive mentions and shares on social media can enhance sentiment by boosting brand visibility and approval.
- Successful Product Launches: Positive reception of new products can improve sentiment by demonstrating innovation and meeting customer needs.
- Customer Testimonials: Positive testimonials and case studies shared publicly can enhance sentiment by providing credible endorsements of the brand.
- Awards and Recognitions: Receiving industry awards or recognitions can boost sentiment by validating the brand's quality and leadership.
- Effective Influencer Partnerships: Collaborations with popular influencers who speak positively about the brand can significantly enhance sentiment by leveraging their reach and credibility.

Involved Roles & Activities¶

Involved Roles

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

Brand & Communications
Customer Success
Product Marketing (PMM)
Sales Enablement
Social Media & Community Manager
Activities

Common initiatives or actions associated with this KPI:

Social Listening
Sales Enablement
Competitive Intel
Messaging Strategy
Brand Health Monitoring

Funnel Stage & Type¶

AAARRR Funnel Stage

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

Awareness
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 Sentiment: Brand Sentiment provides a leading, real-time gauge of public and customer attitudes toward the brand, often preceding shifts in Sentiment Analysis (Sales + Social). Negative trends in Brand Sentiment can forecast future drops in overall sentiment across sales calls and social channels.
- Customer Loyalty: Customer Loyalty tracks the strength of ongoing customer relationships and advocacy. Declining loyalty frequently precedes negative sentiment in both sales interactions and social feedback, making it a valuable early warning indicator.
- Net Promoter Score: Net Promoter Score (NPS) captures customer willingness to recommend your brand. Drops in NPS scores often lead to increased negative sentiment in sales conversations and on social media, as detractors become more vocal.
- Activation Rate: Activation Rate measures how many users reach key engagement milestones. A falling Activation Rate can indicate early disengagement, which often translates to negative sentiment in both sales calls and public forums as users express dissatisfaction.
- Trial-to-Paid Conversion Rate: Trial-to-Paid Conversion Rate reflects customer satisfaction and perceived value early in the journey. Lower conversion rates may precede increases in negative sentiment, as failed conversions often result in critical feedback in sales and social channels.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Churn Risk Score: Churn Risk Score quantifies the likelihood of customer departures, which often aligns with or follows negative sentiment shifts. Elevated churn risk validates and amplifies trends detected in Sentiment Analysis (Sales + Social), confirming the business impact.
- Customer Downgrade Rate: An increase in Customer Downgrade Rate demonstrates customers’ dissatisfaction and reduction in value perception, commonly following negative shifts in sentiment. This metric confirms and quantifies the downstream revenue and account health impact of declining sentiment.
- Branded Search Volume: Changes in Branded Search Volume reflect shifts in public interest and intent. A drop in branded searches usually follows negative sentiment trends, serving as a lagging indicator that confirms reduced brand affinity.
- Conversion Rate: Conversion Rate captures the effectiveness of moving prospects through the funnel. Deteriorating sentiment in sales and social channels typically leads to lower conversion rates, validating the real-world impact of negative perceptions.
- Customer Feedback Retention Score: This metric measures whether customers who provide feedback remain loyal. A drop in this score often trails negative sentiment trends, confirming that poor sentiment correlates with actual retention and loyalty issues.