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Computational Photography

How Marketers Use AI With Image Analysis

How Marketers Use AI With Image Analysis

With more than 3 billion images uploaded online every day, you will never be able to go through that amount of information manually.
However, this data can provide valuable insights into customer behavior, current trends, competitors’ capabilities, etc.

With these objectives in mind, computational photography offers a more advanced approach to existing research tools and analytics platforms. Let’s see how you can incorporate a broader dataset into your decision-making process.


Definition of Computational Photography

The emerging field of computational photography is yet to set its precise definition. A generally accepted interpretation of the term was defined by Marc Levoy, professor of computer science at Stanford University and leading computer graphics researcher. According to him, computational photography is a set of methods to “enhance or extend the capabilities of digital photography”.

In Levoy’s talk on the new techniques in computational photography, he outlined various uses and entrepreneurial capacity of this technology, ranging from image analysis & enhancement to computational cameras. Currently, the most well-known examples are techniques used in most recent smartphones and some standalone cameras. These include autofocus, high dynamic range imaging (HDR), vibration reduction, and shot bracketing.

Even though the boundaries of what constitutes computational photography are unclear, there is one thing we can say with certainty. In terms of taking and storing digital images, smartphone use is far more common than film, DSLR, or mirrorless cameras.

As people are taking more pictures, they are contributing to a growing body of data that brands and organizations can use. Image analysis can be put into practice to learn more about people taking them and apply this information for marketing purposes. Let’s see how these opportunities can be realized.


How Does AI Bring Value to Marketers?

The global AI market is rapidly growing, and the quality and quantity of data that machines can process is improving.

A notable example is the Asia-Pacific region – it is projected to have a 35.95% compound annual growth rate (CAGR) due to an increase in AI investments and a heightened demand for data analysis. But what does it mean for businesses?

From a marketing standpoint, image analysis, with the help of AI, is a versatile tool for multiple purposes. CVisionLab can help you understand its applications and identify which ones are most fitting for your purposes.

In the context of market research and social media, image analysis is the approach to learn more about visual content online. AI makes it possible to recognize and extract features of the image; for example, curvature, color, and texture.

Let’s say you are a brand specializing in producing and distributing roofing materials. You need to select locations in nearby regions to design logistics networks for the next several years.
For a marketer, the most important factors to analyze include knowing and understanding:

  • Target audience;
  • Customer and their behavior;
  • Competition and information environment;
  • Routes to potential customers.

Suppose you have an image database for the regions under consideration. You can analyze this data and map your audience by breaking down the database into the following parameters:

How Marketers Use AI for Roof Analysis


  • Decrepit roofs
  • Old-style roofs
  • Modern soft roofing
  • Modern hard roofing
  • Roofing of high-rise facilities and more

It should also be easy to further segment these categories – for example, where roofing works are likely to be needed in the near future. It will allow you to gather and visualize these objects based on proximity to each other. If there is a location with multiple objects that may need roofing works, you can organize the logistics with this information in mind, open a new store in the area, prorate insurance policy for an agency etc.

Additionally, you can compare the results with work locations for the distant future. Or you can extract whatever data you need on competitors, such as the service life of their roofing materials.

Overall, the technology does not limit you in terms of its applications and industries where it can be used. There is potential for computational photography in entertainment, medical imaging, industrial inspection, insurance, etc.

The overarching theme is that marketing experts can obtain large volumes of visual data and process it. The amount of useful information from such an analysis is growing, which will provide a competitive edge. Image analysis offers an alternative approach to marketing research and focus groups by using data not in a straightforward way.


How Can We Turn AI into Competitive Advantage?

The most limiting factor in AI and computer vision industries is that research and solutions are mostly focused on specific case uses. Plus, regulators are imposing conditions restricting the processing of personal data. In this regard, any user information becomes particularly valuable.

Large companies that set the pace and support emerging technologies have the ability to outperform rivals and provide their services to other companies. An eminent example is McKinsey Global Institute conducting research in commercial, public, and social sectors and influencing developments of entire industries.

There is no denying that businesses can benefit from understanding their customers’ needs and desires through collecting data. But the question of what form it will take and what to do with it is better addressed by various outsourcing companies with Big Data departments. However, unlike us, even these companies are not always able to present a targeted optimization solution.

Why Data Is Key to Business Success

Big Data requires equally significant intelligence and resources. Before a model can quickly and accurately draw conclusions, it needs to be taught based on thousands of images. Then the tool will be able to identify, segment, and classify data.

But this effort is well worth it. Manually selecting and matching images based on specific criteria is almost unreasonable contrasted with what a neural network can achieve. Now there are a plethora of various datasets: there are images that can be found through search engines, social media, and mapping services, frames taken from video sharing platforms, etc.

The matter of data collection should preferably be resolved ahead of time. However, by consulting with machine learning specialists when dealing with the first datasets, you ensure the work is done correctly from the very beginning. The type and quality of data we gather and input into the model will affect the subsequent outcome. If we use data gathered from an outdated dataset, results may not be accurate. For example, there might be a recent event that impacts demand in a particular market. Therefore, you will need a relevant dataset for precise analysis and a correct conclusion.

Collected data and data analysis can help you answer research questions and meet your objectives.
Contact us, and we will address your questions and assumptions. We will help you take advantage of Big Data and reach the most profitable outcome.




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