How AI Helps in Creating Content for Advertising Campaigns

In today's marketing world, artificial intelligence (AI) is becoming an essential tool for advertising specialists. AI makes it possible to create more personalized, effective, and cost-efficient advertising campaigns. In this article, we will discuss how AI supports the process of creating advertising content, from generating ideas to optimization and analyzing results.

1. Generating Ideas and Content

AI can significantly speed up the process of generating ideas for advertising content. Using machine learning algorithms, AI can analyze large amounts of data to identify trends and audience preferences.

Example: Generating Content Using AI

from transformers import pipeline

# Initializing the text generation model
generator = pipeline('text-generation', model='gpt-2')

# Generating ideas for advertising content
prompts = [
    "Write an advertising slogan for a new phone",
    "Create an idea for an advertising campaign for a new cosmetic product",
    "Write an ad text for a streaming service"
]

for prompt in prompts:
    print(f"Prompt: {prompt}")
    print("Generated content:")
    for text in generator(prompt, num_return_sequences=3):
        print(f"- {text['generated_text']}")
    print("\n")

2. Personalizing Content

AI allows for the creation of personalized advertising content that better meets the needs and preferences of individual recipients. By analyzing data on user behavior, AI can tailor content to be more appealing to specific target groups.

Example: Personalizing Content Using AI

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans

# Example data on user preferences
user_preferences = [
    "phones, technology, games",
    "fashion, cosmetics, beauty",
    "travel, culture, art"
]

# Text vectorization
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(user_preferences)

# User clustering
kmeans = KMeans(n_clusters=3, random_state=42)
kmeans.fit(X)

# Generating personalized content for each cluster
clusters = kmeans.labels_
for i, cluster in enumerate(clusters):
    print(f"User {i+1} belongs to cluster {cluster}")
    if cluster == 0:
        print("Suggested content: New phone with the latest technology")
    elif cluster == 1:
        print("Suggested content: New cosmetic product for skin care")
    else:
        print("Suggested content: Exceptional travel offers")
    print("\n")

3. Optimizing Content

AI can help optimize advertising content to increase its effectiveness. By analyzing data from previous campaigns, AI can identify which content elements are most effective and adjust them to achieve better results.

Example: Optimizing Content Using AI

import pandas as pd
from sklearn.linear_model import LogisticRegression

# Example data from previous campaigns
data = {
    'slogan': [
        "New phone with the latest technology",
        "Exceptional travel offers",
        "New cosmetic product for skin care"
    ],
    'click_through_rate': [0.05, 0.03, 0.07],
    'conversion_rate': [0.01, 0.005, 0.02]
}

df = pd.DataFrame(data)

# Text vectorization
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(df['slogan'])
y = df['click_through_rate']

# Training the predictive model
model = LogisticRegression()
model.fit(X, y)

# Predicting the effectiveness of new content
new_slogans = [
    "New phone with the latest technology and long battery life",
    "Exceptional travel offers with low prices",
    "New cosmetic product for skin care with natural ingredients"
]

X_new = vectorizer.transform(new_slogans)
predictions = model.predict(X_new)

for slogan, prediction in zip(new_slogans, predictions):
    print(f"Slogan: {slogan}")
    print(f"Predicted CTR: {prediction}")
    print("\n")

4. Analyzing Results

AI can help analyze the results of advertising campaigns to identify which elements were most effective and how they can be improved in the future. By analyzing data, AI can provide valuable insights that will help optimize future campaigns.

Example: Analyzing Results Using AI

import matplotlib.pyplot as plt

# Example data from an advertising campaign
data = {
    'campaign': ['Campaign 1', 'Campaign 2', 'Campaign 3'],
    'CTR': [0.05, 0.03, 0.07],
    'conversions': [100, 50, 200]
}

df = pd.DataFrame(data)

# Visualizing results
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
plt.bar(df['campaign'], df['CTR'])
plt.title('Click-Through Rate (CTR)')
plt.subplot(1, 2, 2)
plt.bar(df['campaign'], df['conversions'])
plt.title('Number of conversions')
plt.show()

Summary

Artificial intelligence is becoming an essential tool for advertising specialists. AI makes it possible to create more personalized, effective, and cost-efficient advertising campaigns. From generating ideas to optimization and analyzing results, AI offers many possibilities that can significantly improve the effectiveness of advertising campaigns.

Thanks to AI, marketers can focus on the strategic aspects of their campaigns, leaving routine tasks to algorithms. As AI technologies develop, we can expect their role in creating advertising content to become even more significant.