/brand-competition

name: brand-competition
version: 2025-01-13
description: Competitive intelligence analysis comparing a brand against competitors using XPOZ MCP. Analyzes share of voice, sentiment comparison, and competitive positioning. Use when asked to "compare X vs Y", "competitive analysis", or "how does X stack up against competitors".

Brand Competition Skill

Overview

This skill provides competitive intelligence by analyzing a brand against its competitors. It compares sentiment scores, share of voice, narratives, and positioning across multiple companies using real Twitter/X data.

When to Use

Activate this skill when the user asks about:

• "Compare [BRAND] vs [COMPETITORS]"
• "Competitive analysis for [BRAND]"
• "How does [BRAND] stack up against competitors?"
• "[BRAND] vs [COMPETITOR] sentiment"
• "Market positioning for [BRAND]"
• "Share of voice analysis"

XPOZ MCP Data Flow

Step 1: Identify Competitors

If competitors are provided, use them. Otherwise, auto-discover based on industry:

| Brand | Auto-Discovered Competitors | |-------|----------------------------| | NVIDIA | AMD, Intel, Broadcom | | Tesla | Rivian, BYD, Lucid | | Apple | Samsung, Google, Microsoft | | Nike | Adidas, Puma, Under Armour | | McDonald's | Burger King, Wendy's, KFC | | Coca-Cola | Pepsi, Dr Pepper, Monster |

Step 2: Query Expansion (CRITICAL!)

Expand each brand name to include ticker symbols:

NVIDIA → "NVIDIA" OR "$NVDA"
AMD → "AMD" OR "$AMD"
Intel → "Intel" OR "$INTC"

Step 3: Fetch Data for Each Company

For **each company** (brand + 2-3 competitors):

Use getTwitterPostsByKeywords with:
- query: Expanded query for each company
- fields: ["id", "text", "authorUsername", "createdAtDate", "likeCount", "retweetCount"]
- startDate/endDate: Last 7 days
- userPrompt: "Fetching tweets about [COMPANY] for competitive analysis"

**CRITICAL: Async Polling Pattern** 1. The API returns an `operationId` 2. Call `checkOperationStatus` with that operationId 3. Poll until status is "completed" (up to 8 times, ~5 seconds between)

Step 4: Fetch Cross-Company Influencers

Find influencers who mention multiple companies:

Use getTwitterUsersByKeywords with:
- query: "NVIDIA" OR "AMD" OR "Intel" (all companies combined)
- fields: ["id", "username", "name", "followersCount", "description"]

Step 5: Calculate Metrics

**Share of Voice:**

company_share = company_tweets / total_tweets * 100

**Sentiment Comparison (5-Level Scale):**

• Classify tweets using 5-level scale: positive, leaning_positive, neutral, leaning_negative, negative
• Calculate sentiment score (0-100) for each company using weighted formula
• Compare percentage breakdown across all 5 sentiment levels
• Higher weight for positive content, penalty for negative mentions

**Competitive Positioning:**

• Identify strengths/weaknesses for each
• Compare narratives across companies

Output Requirements

JSON Schema

{
  "reportType": "competition",
  "brand": "NVIDIA",
  "competitors": ["AMD", "Intel"],
  "period": {
    "days": 7,
    "startDate": "2025-01-06",
    "endDate": "2025-01-13"
  },
  "summary": {
    "headline": "NVIDIA Leads AI Chip Race (max 10 words)",
    "insight": "Key competitive insight in max 20 words",
    "analysts_view": "2-3 sentence competitive analysis with citations"
  },
  "analysts_cited": ["Dan Ives (Wedbush)", "Patrick Moorhead (Moor Insights)"],
  "shareOfVoice": {
    "NVIDIA": 55,
    "AMD": 30,
    "Intel": 15
  },
  "companies": [
    {
      "name": "NVIDIA",
      "type": "brand",
      "tweetCount": 245,
      "sentiment_score": 72,
      "positive_pct": 45,
      "negative_pct": 18,
      "narratives": [
        { "title": "AI Infrastructure Dominance", "sentiment": "positive", "detail": "..." }
      ],
      "strengths": ["Market leadership", "CUDA ecosystem"],
      "weaknesses": ["High valuation", "Supply constraints"],
      "key_quote": "@user: Actual tweet..."
    },
    {
      "name": "AMD",
      "type": "competitor",
      "tweetCount": 134,
      "sentiment_score": 58,
      "positive_pct": 38,
      "negative_pct": 25,
      "narratives": [...],
      "strengths": [...],
      "weaknesses": [...],
      "key_quote": "..."
    }
  ],
  "influencers": [
    {
      "username": "@tech_analyst",
      "name": "Tech Analyst",
      "followers": 125000,
      "sentiment": "neutral",
      "companies_mentioned": ["NVIDIA", "AMD"],
      "sample_tweet": { "text": "...", "likes": 500, "retweets": 50 }
    }
  ],
  "competitiveInsights": {
    "leader": "NVIDIA",
    "challenger": "AMD",
    "key_battleground": "Data center AI accelerators"
  }
}

HTML Report Output

Generate a standalone HTML report with:

• Tailwind CSS (CDN)
• Dark theme (slate-900 background)
• Share of voice pie chart
• Sentiment comparison bars
• Company comparison cards
• Competitive insights section
• **Analyst Consensus section** with price targets and analyst names
• **REQUIRED FOOTER**: `Powered by XPOZ MCP Social Intelligence — visit xpoz.ai to see how you can use it` (with link to https://xpoz.ai)

React Artifact Template

import React, { useState } from 'react';
import { BarChart, Bar, XAxis, YAxis, Tooltip, ResponsiveContainer, PieChart, Pie, Cell } from 'recharts';
import { TrendingUp, TrendingDown, Users, Target, GitCompare } from 'lucide-react';

export default function BrandCompetition() {
  const [activeTab, setActiveTab] = useState('overview');

  // CLAUDE: Replace with actual analyzed data
  const brand = 'NVIDIA';
  const competitors = ['AMD', 'Intel'];
  const period = { days: 7, startDate: '2025-01-06', endDate: '2025-01-13' };

  const summary = {
    headline: 'NVIDIA Leads AI Chip Race',
    insight: 'NVIDIA dominates share of voice with 55% vs AMD 30%',
    analysts_view: 'NVIDIA maintains competitive advantage in AI infrastructure...'
  };

  const companies = [
    { name: 'NVIDIA', type: 'brand', tweetCount: 245, sentiment_