Automatic Ad Generation From Product Description Using Artificial Intelligence

Can AI Generate Ad From Product Description

Yes, AI can generate ads from product descriptions. By analyzing the product description, an AI system can generate relevant ad copy that highlights the product’s features and benefits, and appeals to the target audience.

AI-powered ad generators use natural language processing (NLP) and machine learning algorithms to analyze the text and generate ad copy. The system can also consider factors such as the target audience, brand voice, and advertising goals when creating the ad.

Some AI-powered ad generators can even generate multiple ad variations, allowing advertisers to test which ad performs the best. This can be helpful to optimize ad campaigns and improve their effectiveness.

OpenAI Ad Generation

OpenAI has been working on natural language processing and consistently developing industry revolutionary tools. Have you heard about ChatGPT? It is the best chatbot available today. Learn about it from the complete guide on using OpenAI ChatGPT.

Python Code

Below is the python code for how OpenAI can be used to generate ads from product descriptions. The only prerequisite is to generate your OpenAI API key which can be done easily.

Related Read: OpenAI Completion API

import openai
openai.api_key = API Key
response=openai.Completion.create(
    model="text-davinci-003",
    prompt="Write creative ad for the following product to run on Facebook aimed at parents:\n\nProduct: chatbot to support food delivery.",
    temperature=0.5,
    max_tokens=100,
    top_p=1.0,
    frequency_penalty=0.0,
    presence_penalty=0.0
)
response
<OpenAIObject text_completion id=cmpl-6OsQh3Yqu5JOiqhYOaqW7Ls6UdKFX at 0x7f3cf39686d0> JSON: {
“choices”: [
    {
        “finish_reason”: “length”,
        “index”: 0,
        “logprobs”: null,
        “text”: “\n\nAre you a busy parent who wants to make sure your family is eating healthy meals? Look no further! Our chatbot for food delivery is here to help! With our chatbot, you can easily order healthy meals for your family with the click of a button. Plus, our chatbot is available 24/7, so you can order when it’s convenient for you. Don’t let the hassle of meal planning get in the way of feeding your family nutritious meals. Try our chatbot
    }
],
        “created”: 1671387119,
        “id”: “cmpl-6OsQh3Yqu5JOiqhYOaqW7Ls6UdKFX”,
        “model”: “text-davinci-003”,
        “object”: “text_completion”,
        “usage”:
            {
                 “completion_tokens”: 100,
                 “prompt_tokens”: 28,
                 “total_tokens”: 128
            }
}
response["choices"][0]["text"]

Output:

Are you a busy parent who wants to make sure your family is eating healthy meals? Look no further! Our chatbot for food delivery is here to help! With our chatbot, you can easily order healthy meals for your family with the click of a button. Plus, our chatbot is available 24/7, so you can order when it’s convenient for you. Don’t let the hassle of meal planning get in the way of feeding your family nutritious meals. Try our chatbot.

OpenAI Completion Parameters

Below is a detailed description of all the parameters used in the above code:

1. model 

  • Id of the model to be used.

2. prompt

  • Text description of the SQL query to be generated.

3. temperature

  • It defines the amount of risk the model will take.
  • Higher the temperature value, the more the risk.
  • More risk helps in creating more creative applications.
  • Low risk will create applications with well-defined answers.

4. max_tokens

  • The maximum number of tokens to be generated in the completion.

5. top_p

  • The model considers the result of the tokens with top_p probability mass.
  • For example, top_p=0.2 means the model will only consider the tokens comprising the top 20% probability mass.

6. frequency_penalty

  • Value lies between -2 and +2.
  • A positive value decreases the model’s likelihood to repeat the same line.
  • Positive values penalize new tokens based on their existing frequency in the text.

7. presence_penalty

  • The value lies between -2 and +2.
  • A positive value increases the model’s likelihood to talk about a new topic.

8. stop

  • The returned text will not contain a stop sequence.
  • The maximum length of the sequence can be 4.

How Automatic AD Generation can be Useful?

Automatic AD generation can be very useful. Below are a few examples:

  1. Time-Saving: Automatic AD generation can save a lot of time and effort that would otherwise be spent on manually creating advertisements. By using automated tools, businesses can quickly create high-quality ads without the need for a lot of manual input.
  2. Cost-Effective: Automatic AD generation can be cost-effective, as it eliminates the need for hiring additional personnel to create ads. This can be particularly beneficial for small businesses that may not have the resources to hire a dedicated advertising team.
  3. Increased Efficiency: Automated tools can help businesses quickly generate a large number of ads that are tailored to specific target audiences. This can help businesses reach more potential customers with less effort.
  4. Improved Targeting: Automated AD generation tools can use data analytics to identify the most effective targeting strategies for specific audiences. This can help businesses create more effective ads that reach the right people at the right time.
  5. Consistency: Automated AD generation tools can help ensure that ads are consistent in terms of messaging, branding, and design. This can help build brand recognition and increase the effectiveness of advertising campaigns.

End Notes

This was another example of how mind-blowing the applications of Artificial Intelligence are. You can learn about more such use cases from my previous articles like:

I hope you find my articles useful. Feel free to give your feedback in the comment box below.

Happy Learning 🙂