Business Discovery → Market Research

import os, requests, time
from collections import defaultdict

YELP = os.environ["YELP_API_KEY"]
MV = os.environ["MAVERA_API_KEY"]
YELP_BASE = "https://api.yelp.com/v3"
YELP_H = {"Authorization": f"Bearer {YELP}"}
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

# 1. Search businesses by category and location
CATEGORY = "coffee"
LOCATION = "Austin, TX"

businesses = []
for offset in range(0, 200, 50):
    r = requests.get(f"{YELP_BASE}/businesses/search",
        headers=YELP_H,
        params={
            "term": CATEGORY,
            "location": LOCATION,
            "limit": 50,
            "offset": offset,
            "sort_by": "review_count",
        })
    if r.status_code == 429:
        time.sleep(2)
        continue
    r.raise_for_status()
    batch = r.json().get("businesses", [])
    if not batch:
        break
    businesses.extend(batch)
    time.sleep(0.5)

print(f"Found {len(businesses)} {CATEGORY} businesses in {LOCATION}")

# 2. Aggregate market data
price_tiers = defaultdict(list)
categories_seen = defaultdict(int)
neighborhoods = defaultdict(int)

for biz in businesses:
    price = biz.get("price", "N/A")
    rating = biz.get("rating", 0)
    reviews = biz.get("review_count", 0)
    price_tiers[price].append({"rating": rating, "reviews": reviews, "name": biz["name"]})

    for cat in biz.get("categories", []):
        categories_seen[cat.get("alias", "")] += 1

    loc = biz.get("location", {})
    hood = loc.get("city", LOCATION)
    neighborhoods[hood] += 1

# 3. Build analysis context
market_summary = []
for price, bizes in sorted(price_tiers.items()):
    avg_rating = sum(b["rating"] for b in bizes) / len(bizes)
    avg_reviews = sum(b["reviews"] for b in bizes) / len(bizes)
    top = sorted(bizes, key=lambda x: -x["reviews"])[:3]
    top_names = ", ".join(b["name"] for b in top)
    market_summary.append(
        f"- {price} tier: {len(bizes)} businesses, avg {avg_rating:.1f}/5, "
        f"avg {avg_reviews:.0f} reviews. Leaders: {top_names}"
    )

cat_summary = ", ".join(f"{k} ({v})" for k, v in
    sorted(categories_seen.items(), key=lambda x: -x[1])[:10])

biz_block = "\n".join(
    f"- {b['name']}: {b.get('rating',0)}/5, {b.get('review_count',0)} reviews, "
    f"Price: {b.get('price','N/A')}, Categories: {', '.join(c.get('title','') for c in b.get('categories',[]))}"
    for b in sorted(businesses, key=lambda x: -x.get("review_count", 0))[:15]
)

# 4. Mave competitive analysis
analysis = requests.post("https://app.mavera.io/api/v1/mave/chat",
    headers=MV_H,
    json={"message": f"""Analyze the competitive landscape for "{CATEGORY}" in {LOCATION}.

MARKET OVERVIEW:
{len(businesses)} businesses found

BY PRICE TIER:
{chr(10).join(market_summary)}

CATEGORY OVERLAP:
{cat_summary}

TOP 15 BY REVIEW VOLUME:
{biz_block}

Produce:
1. Market saturation assessment (oversaturated / balanced / underserved)
2. Competitive clusters (which businesses compete directly)
3. White space opportunities (underserved niches, price gaps, location gaps)
4. Positioning recommendations for a new entrant
5. Differentiator suggestions based on what existing players lack
6. Pricing strategy recommendation"""}).json()

print(f"\n=== {CATEGORY.title()} Market in {LOCATION} ===")
print(analysis.get("content", "")[:2000])

const YELP = process.env.YELP_API_KEY;
const MV = process.env.MAVERA_API_KEY;
const YELP_BASE = "https://api.yelp.com/v3";
const YELP_H = { Authorization: `Bearer ${YELP}` };
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

const CATEGORY = "coffee";
const LOCATION = "Austin, TX";

// 1. Search businesses
const businesses = [];
for (let offset = 0; offset < 200; offset += 50) {
  const params = new URLSearchParams({
    term: CATEGORY, location: LOCATION, limit: "50", offset: String(offset), sort_by: "review_count",
  });
  const res = await fetch(`${YELP_BASE}/businesses/search?${params}`, { headers: YELP_H });
  if (res.status === 429) { await new Promise((r) => setTimeout(r, 2000)); continue; }
  if (!res.ok) throw new Error(`Yelp ${res.status}`);
  const batch = (await res.json()).businesses || [];
  if (!batch.length) break;
  businesses.push(...batch);
  await new Promise((r) => setTimeout(r, 500));
}

console.log(`Found ${businesses.length} ${CATEGORY} businesses in ${LOCATION}`);

// 2. Aggregate
const priceTiers = {};
for (const biz of businesses) {
  const price = biz.price || "N/A";
  (priceTiers[price] ??= []).push({ name: biz.name, rating: biz.rating || 0, reviews: biz.review_count || 0 });
}

const marketSummary = Object.entries(priceTiers)
  .sort(([a], [b]) => a.localeCompare(b))
  .map(([price, bizes]) => {
    const avgR = bizes.reduce((s, b) => s + b.rating, 0) / bizes.length;
    const avgRv = bizes.reduce((s, b) => s + b.reviews, 0) / bizes.length;
    const top = bizes.sort((a, b) => b.reviews - a.reviews).slice(0, 3).map((b) => b.name).join(", ");
    return `- ${price}: ${bizes.length} biz, avg ${avgR.toFixed(1)}/5, avg ${Math.round(avgRv)} reviews. Leaders: ${top}`;
  }).join("\n");

const bizBlock = businesses
  .sort((a, b) => (b.review_count || 0) - (a.review_count || 0)).slice(0, 15)
  .map((b) => `- ${b.name}: ${b.rating}/5, ${b.review_count} reviews, ${b.price || "N/A"}`).join("\n");

// 3. Analysis
const analysis = await fetch("https://app.mavera.io/api/v1/mave/chat", {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    message: `Competitive landscape for "${CATEGORY}" in ${LOCATION}:\n\n${businesses.length} businesses\n\nBY TIER:\n${marketSummary}\n\nTOP 15:\n${bizBlock}\n\nProduce: 1) Saturation 2) Clusters 3) White space 4) Positioning 5) Differentiators 6) Pricing strategy`,
  }),
}).then((r) => r.json());

console.log(`\n=== ${CATEGORY} Market in ${LOCATION} ===`);
console.log((analysis.content || "").slice(0, 2000));