Review Themes → Local Content Strategy

Extract review themes per location and generate location-specific marketing content with Mavera

Scenario

Each location has distinct review themes — downtown customers love the espresso, suburban customers love the parking. You extract themes per location and generate location-specific marketing content that highlights what each location does best, in the language customers already use.

Flow: Per-location review themes → Mavera POST /mave/chat (theme extraction) → POST /generations (location content) → Per-location marketing assets

Code

Python

import os, requests, time

GOOG = os.environ["GOOGLE_ACCESS_TOKEN"]
ACCT = os.environ["GOOGLE_ACCOUNT_ID"]
MV = os.environ["MAVERA_API_KEY"]
GB_BASE = "https://mybusiness.googleapis.com/v4"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}
GB_H = {"Authorization": f"Bearer {GOOG}"}
STAR_MAP = {"ONE": 1, "TWO": 2, "THREE": 3, "FOUR": 4, "FIVE": 5}

locations = requests.get(f"{GB_BASE}/{ACCT}/locations",
    headers=GB_H, params={"pageSize": 50}).json().get("locations", [])

for loc in locations[:5]:
    loc_id = loc.get("name", "")
    loc_name = loc.get("locationName", "Unknown")
    city = loc.get("address", {}).get("locality", "Unknown")

    r = requests.get(f"{GB_BASE}/{loc_id}/reviews",
        headers=GB_H, params={"pageSize": 50})
    if r.status_code != 200:
        continue
    reviews = r.json().get("reviews", [])

    positive = [rev.get("comment", "")[:250] for rev in reviews
                if STAR_MAP.get(rev.get("starRating"), 3) >= 4 and rev.get("comment")]
    negative = [rev.get("comment", "")[:250] for rev in reviews
                if STAR_MAP.get(rev.get("starRating"), 3) <= 2 and rev.get("comment")]

    # 1. Theme extraction
    themes = requests.post("https://app.mavera.io/api/v1/mave/chat", headers=MV_H,
        json={"message": f"""Extract themes from reviews for {loc_name} ({city}).

POSITIVE ({len(positive)} reviews):
{chr(10).join(positive[:10])}

NEGATIVE ({len(negative)} reviews):
{chr(10).join(negative[:10])}

Return: top 5 positive themes, top 3 negative themes, each with frequency and representative quote."""}).json()

    print(f"\n{'='*50}")
    print(f"THEMES: {loc_name} ({city})")
    print(themes.get("content", "")[:500])

    # 2. Generate location-specific content
    gen = requests.post("https://app.mavera.io/api/v1/generations", headers=MV_H,
        json={"prompt": (
            f"Generate location-specific marketing content for {loc_name} in {city}.\n\n"
            f"Themes from customer reviews:\n{themes.get('content','')[:500]}\n\n"
            f"Create:\n"
            f"1. Google Business Profile post (150 words, highlight top strength)\n"
            f"2. Local Instagram caption (casual, with hashtags)\n"
            f"3. Location-specific tagline (10 words max)\n"
            f"4. Local SEO paragraph for website (100 words, include city name naturally)"
        )}).json()

    print(f"\nCONTENT:")
    print(gen.get("output", gen.get("content", gen.get("text", "")))[:600])
    time.sleep(1)

Example Output

==================================================
THEMES: Downtown Café (Austin)
Positive: Espresso quality (18x), Fast service (14x), Staff friendliness (11x)
Negative: Crowding at noon (6x), No outdoor seating (4x)

CONTENT:

## Google Business Profile Post
The best espresso in downtown Austin — and your neighbors agree. Our baristas
pull 200+ shots daily, each one dialed in before the morning rush. Stop by
for your 11am pick-me-up. Lines move fast (we've heard you on the wait times).

## Instagram Caption
That 3pm espresso hit different when it's from downtown 🫠☕
Come see why Austin keeps coming back. Open 6am-8pm, every day.
#AustinCoffee #DowntownATX #EspressoLovers #ATXEats

## Tagline
"Downtown Austin's fastest espresso. No compromises."

## Local SEO
Looking for the best coffee in downtown Austin? Our Downtown Café
serves handcrafted espresso, cold brew, and pastries steps from
Congress Avenue. Austin locals rate us 4.6/5 on Google with 234 reviews.

Error Handling

Locations with few reviews

Locations with fewer than 10 reviews produce weak themes. Skip or combine with nearby locations for analysis.

Multi-language reviews

In tourist-heavy locations, reviews come in multiple languages. Pass ?language=en to filter, or let Mave handle mixed-language input.