How can I make creative decisions when I don't have enough data
There are millions of decisions you need to make when starting a business that are too small to test, but are too risky to make without data.
Memetic Analysis
GPT-4V can be used to automate qualitative analysis and labeling of images so you get a wider picture of what creative choices your competitors made...More
I know it's frustrating that we don't have enough data to make these decisions, but memetic analysis can really help us out here. \nBy analyzing and tagging images of coffee shop interiors, we can uncover valuable insights that will guide our design choices. \nThis process will allow us to understand the trends in the industry and make informed decisions that resonate with our target audience. \nSo, let's dive in and see what we can discover through memetic analysis!
This course is a work of fiction. Unless otherwise indicated, all the names, characters, businesses, data, places, events and incidents in this course are either the product of the author's imagination or used in a fictitious manner. Any resemblance to actual persons, living or dead, or actual events is purely coincidental.
Memetic Analysis: Uncovering Insights with AI and Vision
In today's digital age, understanding consumer preferences and market trends is crucial for businesses to stay competitive. This is especially true for industries like coffee shops, where interior design and ambiance play a significant role in attracting customers. But how can businesses gain insights into their competitors' designs and make informed decisions about their own establishments?
One innovative approach is using memetic analysis, a method that combines artificial intelligence (AI) and computer vision to analyze images and identify patterns. This technique can help businesses gain valuable insights into the typical interiors of their competitors and make data-driven decisions about their own establishments.
The first step in conducting a memetic analysis is to gather a large dataset of images from various coffee shops. Manually analyzing these images can be time-consuming and inefficient. However, with the advancements in AI and computer vision, it is now possible to automatically tag these images using machine learning algorithms.
To scrape a large number of images from Google, a Google image scraper tool can be used. This tool automates the process of collecting images based on specific keywords, such as "coffee shop interiors." Once the images are downloaded, they can be analyzed using the AI-powered Google Cloud Vision API.
The analysis begins with inductive coding, where two different images are randomly selected and compared. The images are converted into base64 encoding and sent to the API for analysis. The prompt asks the AI to compare the two coffee shops using inductive coding and identify similar and different features and design elements. The response from the API provides valuable insights into the shared and unique attributes of the two coffee shops.
The next step is deductive coding, which involves analyzing each image against a set of predefined labels. This step is more time-consuming and expensive, but it provides a comprehensive understanding of the design elements present in each coffee shop. To speed up the process, asynchronous coding can be utilized, allowing multiple API calls to be processed simultaneously.
The results of the deductive coding are stored in a dictionary, which includes the labels and corresponding coffee shop images. These labels can be customized based on the specific design elements or features that the business wants to analyze, such as natural light, wooden elements, or suspended lighting.
Once the analysis is complete, businesses can gain valuable insights into the design choices of their competitors. By comparing the frequency of different labels across the dataset, businesses can identify popular trends and common design elements. For example, if a high percentage of coffee shop images include patrons,
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