Not all Reddit mentions are equal. A post asking 'what is the best X' signals research intent, while 'looking for alternative to Y' signals ready-to-switch intent. This classifier scores Reddit posts by purchase intent using keyword patterns and context, helping you prioritize which discussions to engage with.
Prerequisites
- Python 3.8+
- requests library
- A Scavio API key from scavio.dev
- Target product category or niche
Walkthrough
Step 1: Fetch Reddit posts via SERP
Search Reddit for posts in your product category.
Python
import os, requests, json
from collections import Counter
API_KEY = os.environ['SCAVIO_API_KEY']
SH = {'x-api-key': API_KEY, 'Content-Type': 'application/json'}
CATEGORY = 'search api'
QUERIES = [
f'{CATEGORY} recommendation site:reddit.com',
f'best {CATEGORY} site:reddit.com',
f'alternative to {CATEGORY} site:reddit.com',
f'{CATEGORY} pricing site:reddit.com',
f'{CATEGORY} not working site:reddit.com',
]
def fetch_reddit_posts(query):
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=SH, json={'query': query, 'country_code': 'us'}).json()
return [{'title': r.get('title', ''), 'snippet': r.get('snippet', ''),
'link': r.get('link', '')} for r in data.get('organic_results', [])]
all_posts = []
for q in QUERIES:
posts = fetch_reddit_posts(q)
all_posts.extend(posts)
print(f'Fetched {len(all_posts)} Reddit posts. Cost: ${len(QUERIES) * 0.005:.3f}')Step 2: Classify posts by purchase intent
Score each post for research, comparison, ready-to-buy, or complaint intent.
Python
INTENT_PATTERNS = {
'ready_to_switch': {
'keywords': ['alternative to', 'switching from', 'replacing', 'migrate from', 'looking for replacement'],
'score': 90,
'label': 'READY TO SWITCH'
},
'comparison': {
'keywords': ['vs', 'versus', 'compared to', 'better than', 'which is better', 'comparison'],
'score': 70,
'label': 'COMPARING'
},
'research': {
'keywords': ['best', 'recommend', 'suggestion', 'what do you use', 'looking for', 'need a'],
'score': 50,
'label': 'RESEARCHING'
},
'complaint': {
'keywords': ['not working', 'broken', 'issues with', 'problems', 'frustrated', 'terrible'],
'score': 80,
'label': 'FRUSTRATED (competitor)'
},
'pricing': {
'keywords': ['pricing', 'cost', 'expensive', 'cheap', 'free tier', 'budget'],
'score': 60,
'label': 'PRICE SENSITIVE'
}
}
def classify_intent(post):
text = f'{post["title"]} {post["snippet"]}'.lower()
best_intent = None
best_score = 0
for intent, config in INTENT_PATTERNS.items():
matches = sum(1 for kw in config['keywords'] if kw in text)
if matches > 0 and config['score'] > best_score:
best_intent = intent
best_score = config['score']
return best_intent or 'general', best_score
for post in all_posts:
intent, score = classify_intent(post)
post['intent'] = intent
post['intent_score'] = score
intents = Counter(p['intent'] for p in all_posts)
print(f'\nIntent Distribution:')
for intent, count in intents.most_common():
label = INTENT_PATTERNS.get(intent, {}).get('label', 'GENERAL')
print(f' {label:25} | {count:3} posts')Step 3: Prioritize leads by intent score
Rank posts by lead quality and output a prioritized engagement list.
Python
def lead_priority_report(posts):
sorted_posts = sorted(posts, key=lambda p: p.get('intent_score', 0), reverse=True)
print(f'\n=== Lead Priority Report ===')
print(f' Total posts: {len(sorted_posts)}')
# Tier 1: High intent (score >= 80)
tier1 = [p for p in sorted_posts if p.get('intent_score', 0) >= 80]
print(f'\n TIER 1 - High Intent ({len(tier1)} posts, engage immediately):')
for p in tier1[:5]:
label = INTENT_PATTERNS.get(p['intent'], {}).get('label', 'UNKNOWN')
print(f' [{label:20}] {p["title"][:50]}')
print(f' {p["link"][:60]}')
# Tier 2: Medium intent (50-79)
tier2 = [p for p in sorted_posts if 50 <= p.get('intent_score', 0) < 80]
print(f'\n TIER 2 - Medium Intent ({len(tier2)} posts, monitor):')
for p in tier2[:5]:
label = INTENT_PATTERNS.get(p['intent'], {}).get('label', 'UNKNOWN')
print(f' [{label:20}] {p["title"][:50]}')
# Tier 3: Low intent
tier3 = [p for p in sorted_posts if p.get('intent_score', 0) < 50]
print(f'\n TIER 3 - Low Intent ({len(tier3)} posts, skip or batch)')
print(f'\n Cost: ${len(QUERIES) * 0.005:.3f}')
lead_priority_report(all_posts)Python Example
Python
import os, requests
SH = {'x-api-key': os.environ['SCAVIO_API_KEY'], 'Content-Type': 'application/json'}
def classify_reddit(query):
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=SH, json={'query': f'{query} site:reddit.com', 'country_code': 'us'}).json()
for r in data.get('organic_results', [])[:3]:
title = r.get('title', '').lower()
intent = 'SWITCH' if 'alternative' in title else 'RESEARCH' if 'best' in title else 'GENERAL'
print(f' [{intent}] {r.get("title", "")[:50]}')
classify_reddit('search api recommendation')
print('Cost: $0.005')JavaScript Example
JavaScript
const SH = { 'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json' };
const data = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST', headers: SH,
body: JSON.stringify({ query: 'search api recommendation site:reddit.com', country_code: 'us' })
}).then(r => r.json());
(data.organic_results || []).slice(0, 3).forEach(r => {
const t = r.title.toLowerCase();
const intent = t.includes('alternative') ? 'SWITCH' : t.includes('best') ? 'RESEARCH' : 'GENERAL';
console.log(`[${intent}] ${r.title.slice(0, 50)}`);
});Expected Output
JSON
Fetched 35 Reddit posts. Cost: $0.025
Intent Distribution:
RESEARCHING | 12 posts
READY TO SWITCH | 8 posts
COMPARING | 7 posts
PRICE SENSITIVE | 4 posts
FRUSTRATED (competitor) | 2 posts
GENERAL | 2 posts
=== Lead Priority Report ===
TIER 1 - High Intent (10 posts, engage immediately):
[READY TO SWITCH ] Looking for alternative to SerpAPI, any sugges...
[FRUSTRATED (competitor)] Tavily not working after Nebius acquisition...
TIER 2 - Medium Intent (11 posts, monitor):
[COMPARING ] Scavio vs Tavily for AI agent search...