PPC Advertising Guide — Google Ads Structure, Bidding, and Optimization

# keyword_research.py
# Simulate keyword research and grouping
import re
from collections import Counter

class KeywordResearch:
    def __init__(self):
        self.keywords = []
    
    def add_keyword(self, keyword: str, volume: int, competition: float, cpc: float):
        self.keywords.append({
            "keyword": keyword,
            "volume": volume,
            "competition": competition,  # 0.0 to 1.0
            "cpc": cpc,
        })
    
    def calculate_score(self, keyword: dict) -> float:
        """Score keywords by volume * (1 - competition) / cpc."""
        if keyword["cpc"] == 0:
            return 0
        return keyword["volume"] * (1 - keyword["competition"]) / keyword["cpc"]
    
    def group_keywords(self):
        """Group keywords by topic using word frequency."""
        words = []
        for kw in self.keywords:
            words.extend(kw["keyword"].lower().split())
        
        word_freq = Counter(words)
        common_words = {w for w, c in word_freq.most_common(3)}
        
        groups = {}
        for kw in self.keywords:
            kw_words = set(kw["keyword"].lower().split())
            topic = next((w for w in common_words if w in kw_words), "other")
            if topic not in groups:
                groups[topic] = []
            groups[topic].append(kw)
        
        return groups

# Sample keyword data
research = KeywordResearch()
research.add_keyword("dodatech browser", 12000, 0.3, 1.20)
research.add_keyword("privacy browser", 22000, 0.6, 2.50)
research.add_keyword("secure browser download", 8000, 0.4, 1.80)
research.add_keyword("ad blocker browser", 18000, 0.5, 2.00)
research.add_keyword("chrome alternative", 14000, 0.7, 3.00)
research.add_keyword("fast web browser", 10000, 0.5, 1.50)

print("Keyword Scores:")
for kw in sorted(research.keywords, key=lambda k: research.calculate_score(k), reverse=True):
    score = research.calculate_score(kw)
    print(f"  {kw['keyword']:30s} vol={kw['volume']:>5} comp={kw['competition']:.1f} cpc=${kw['cpc']:.2f} score={score:.0f}")

print("\nKeyword Groups:")
groups = research.group_keywords()
for topic, kws in groups.items():
    print(f"  {topic}: {', '.join(k['keyword'] for k in kws)}")

# ad_rank.py
# Simulate Ad Rank and auction calculation
class GoogleAdAuction:
    def __init__(self):
        self.advertisers = []
    
    def add_advertiser(self, name: str, bid: float, quality_score: int, extensions_boost: float = 1.0):
        self.advertisers.append({
            "name": name,
            "bid": bid,
            "quality_score": quality_score,
            "extensions_boost": extensions_boost,
        })
    
    def run_auction(self):
        # Calculate Ad Rank
        for adv in self.advertisers:
            adv["ad_rank"] = adv["bid"] * adv["quality_score"] * adv["extensions_boost"]
        
        # Sort by rank
        self.advertisers.sort(key=lambda a: a["ad_rank"], reverse=True)
        
        # Calculate actual CPC (second price auction)
        for i, adv in enumerate(self.advertisers):
            if i < len(self.advertisers) - 1:
                next_adv = self.advertisers[i + 1]
                # Actual CPC = next rank / this quality_score + $0.01
                adv["actual_cpc"] = (next_adv["ad_rank"] / adv["quality_score"]) + 0.01
            else:
                adv["actual_cpc"] = adv["bid"]  # Last position pays max
        
        self.display_results()
    
    def display_results(self):
        print("Auction Results (Ad Rank determines position):")
        print(f"{'Position':>8} {'Advertiser':20s} {'Bid':>6} {'QS':>3} {'Ad Rank':>8} {'Actual CPC':>10}")
        print("-" * 60)
        for i, adv in enumerate(self.advertisters, 1):
            print(f"{i:>8} {adv['name']:20s} ${adv['bid']:.2f} {adv['quality_score']:3d} {adv['ad_rank']:.2f} ${adv['actual_cpc']:.2f}")

auction = GoogleAdAuction()
auction.add_adventiser("DodaTech", 2.00, 8, 1.1)   # High QS
auction.add_advertiser("Competitor A", 3.00, 5, 1.0)  # Higher bid, lower QS
auction.add_adventiser("Competitor B", 1.50, 9, 1.0)  # Lower bid, high QS
auction.add_advertiser("Competitor C", 2.50, 4, 1.0)   # High bid, low QS

auction.run_auction()

# bidding_strategies.py
# Compare PPC bidding strategies
class BiddingSimulator:
    def __init__(self, budget: float, conversion_rate: float, avg_order_value: float):
        self.budget = budget
        self.cvr = conversion_rate
        self.aov = avg_order_value
    
    def manual_cpc(self, max_cpc: float, clicks: int) -> dict:
        """Manual CPC: you set max bid per click."""
        total_cost = min(clicks * max_cpc, self.budget)
        actual_clicks = total_cost / max_cpc
        conversions = actual_clicks * self.cvr
        return {
            "strategy": "Manual CPC",
            "budget": self.budget,
            "clicks": int(actual_clicks),
            "cost": total_cost,
            "conversions": int(conversions),
            "revenue": int(conversions * self.aov),
            "roas": int(conversions * self.aov) / total_cost if total_cost > 0 else 0,
        }
    
    def target_cpa(self, target_cpa: float) -> dict:
        """Target CPA: Google optimizes to hit your cost-per-acquisition target."""
        estimated_conversions = self.budget / target_cpa
        estimated_clicks = estimated_conversions / self.cvr
        return {
            "strategy": "Target CPA ($" + str(target_cpa) + ")",
            "budget": self.budget,
            "clicks": int(estimated_clicks),
            "cost": self.budget,
            "conversions": int(estimated_conversions),
            "revenue": int(estimated_conversions * self.aov),
            "roas": int(estimated_conversions * self.aov) / self.budget if self.budget > 0 else 0,
        }
    
    def target_roas(self, target_roas: float) -> dict:
        """Target ROAS: Google optimizes to hit your return on ad spend target."""
        # Target ROAS = revenue / cost. So cost = revenue / target_roas
        estimated_revenue = self.budget * target_roas
        estimated_conversions = estimated_revenue / self.aov
        estimated_clicks = estimated_conversions / self.cvr
        return {
            "strategy": "Target ROAS (" + str(target_roas * 100) + "%)",
            "budget": self.budget,
            "clicks": int(estimated_clicks),
            "cost": self.budget,
            "conversions": int(estimated_conversions),
            "revenue": int(estimated_revenue),
            "roas": target_roas,
        }

sim = BiddingSimulator(budget=1000, conversion_rate=0.03, avg_order_value=50)
results = [
    sim.manual_cpc(max_cpc=2.00, clicks=600),
    sim.target_cpa(target_cpa=25),
    sim.target_roas(target_roas=4.0),
]

print("Bidding Strategy Comparison ($1,000 Budget):")
print(f"{'Strategy':25s} {'Clicks':>7} {'Cost':>8} {'Conversions':>12} {'Revenue':>9} {'ROAS':>6}")
print("-" * 70)
for r in results:
    print(f"{r['strategy']:25s} {r['clicks']:>7} ${r['cost']:>6.0f} {r['conversions']:>8} ${r['revenue']:>6} {r['roas']:.1f}x")

<!-- landing-page.html -->
<!-- Optimized landing page for PPC traffic -->
<!DOCTYPE html>
<html>
<head>
    <title>Download DodaTech Browser — Fast, Secure & Private</title>
    <meta name="description" content="Download DodaTech Browser. Built-in ad blocker, secure search engine, and privacy-first design. 10M+ users trust DodaTech.">
</head>
<body>
    <h1>Browse Faster. Browse Safer. Download DodaTech.</h1>
    <p>10M+ users trust DodaTech for private, ad-free browsing. Get started in 30 seconds.</p>
    
    <form action="/download" method="post">
        <input type="email" name="email" placeholder="Enter your email" required>
        <button type="submit">Download Free — 30MB</button>
    </form>
    
    <div class="trust-signals">
        <span>4.8 stars on Chrome Web Store</span>
        <span>Used in 150+ countries</span>
        <span>Open source</span>
    </div>
    
    <div class="features">
        <h2>What You Get</h2>
        <ul>
            <li>Built-in ad blocker — pages load 3x faster</li>
            <li>Secure search engine — no tracking</li>
            <li>Privacy dashboard — see who's tracking you</li>
            <li>Sync across devices — encrypted</li>
        </ul>
    </div>
</body>
</html>

# ab_test.py
# A/B test simulator for ad copy
import random

class ABTest:
    def __init__(self, control: str, variant: str):
        self.control = {"copy": control, "impressions": 0, "clicks": 0}
        self.variant = {"copy": variant, "impressions": 0, "clicks": 0}
    
    def simulate(self, impressions: int, control_ctr: float, lift: float):
        """Simulate an A/B test with a given CTR lift."""
        self.control["impressions"] = impressions
        self.variant["impressions"] = impressions
        
        control_ctr = control_ctr + random.uniform(-0.001, 0.001)
        variant_ctr = control_ctr * (1 + lift) + random.uniform(-0.001, 0.001)
        
        self.control["clicks"] = int(impressions * control_ctr)
        self.variant["clicks"] = int(impressions * variant_ctr)
        
        self.control["ctr"] = self.control["clicks"] / self.control["impressions"]
        self.variant["ctr"] = self.variant["clicks"] / self.variant["impressions"]
        
        print(f"A/B Test Results ({impressions:,} impressions each):")
        print(f"{'Version':15s} {'Impressions':>12} {'Clicks':>8} {'CTR':>8} {'Lift':>8}")
        print("-" * 55)
        
        c_ctr = self.control["ctr"]
        v_ctr = self.variant["ctr"]
        actual_lift = (v_ctr - c_ctr) / c_ctr
        
        print(f"{'Control':15s} {self.control['impressions']:>12,} {self.control['clicks']:>8} {c_ctr:>7.3%} {'---':>8}")
        print(f"{'Variant':15s} {self.variant['impressions']:>12,} {self.variant['clicks']:>8} {v_ctr:>7.3%} {actual_lift:>+7.1%}")
        
        # Statistical significance (simplified chi-squared)
        if actual_lift > 0.05 and impressions > 10000:
            print(f"\nResult: Variant {'WINS' if actual_lift > 0 else 'LOSES'} (statistically significant)")
        else:
            print("\nResult: Not statistically significant — run longer")

test = ABTest(
    control="Download Fast Browser",
    variant="Browse 3x Faster — Try Now"
)

test.simulate(impressions=50000, control_ctr=0.025, lift=0.20)

# ads_dashboard.py
# Simulate a PPC performance dashboard
from datetime import datetime, timedelta
import random

class PPCPerformanceDashboard:
    def __init__(self):
        self.metrics = {}
    
    def add_daily_data(self, date: str, impressions: int, clicks: int, cost: float, conversions: int):
        ctr = clicks / impressions if impressions > 0 else 0
        cpc = cost / clicks if clicks > 0 else 0
        cvr = conversions / clicks if clicks > 0 else 0
        cpa = cost / conversions if conversions > 0 else 0
        
        self.metrics[date] = {
            "impressions": impressions,
            "clicks": clicks,
            "cost": cost,
            "conversions": conversions,
            "ctr": ctr,
            "cpc": cpc,
            "cvr": cvr,
            "cpa": cpa,
        }
    
    def weekly_report(self):
        print("Weekly PPC Performance Report")
        print("=" * 60)
        print(f"{'Date':12s} {'Impr':>8} {'Clicks':>7} {'Cost':>8} {'Conv':>6} {'CTR':>7} {'CPC':>7} {'CPA':>7}")
        print("-" * 60)
        
        totals = {"impressions": 0, "clicks": 0, "cost": 0, "conversions": 0}
        
        for date in sorted(self.metrics.keys()):
            m = self.metrics[date]
            print(f"{date:12s} {m['impressions']:>8,} {m['clicks']:>7,} ${m['cost']:>6.0f} {m['conversions']:>5} {m['ctr']:>6.2%} ${m['cpc']:>.2f} ${m['cpa']:>.2f}")
            for k in totals:
                totals[k] += m[k]
        
        print("-" * 60)
        avg_ctr = totals["clicks"] / totals["impressions"] if totals["impressions"] > 0 else 0
        avg_cpc = totals["cost"] / totals["clicks"] if totals["clicks"] > 0 else 0
        avg_cpa = totals["cost"] / totals["conversions"] if totals["conversions"] > 0 else 0
        print(f"{'TOTAL':12s} {totals['impressions']:>8,} {totals['clicks']:>7,} ${totals['cost']:>6.0f} {totals['conversions']:>5} {avg_ctr:>6.2%} ${avg_cpc:.2f} ${avg_cpa:.2f}")

dashboard = PPCPerformanceDashboard()

today = datetime.now()
for i in range(7):
    date = (today - timedelta(days=6-i)).strftime("%m/%d")
    dashboard.add_daily_data(
        date=date,
        impressions=random.randint(8000, 15000),
        clicks=random.randint(200, 450),
        cost=random.uniform(400, 800),
        conversions=random.randint(8, 25),
    )

dashboard.weekly_report()