When shoppers visit an e-commerce site, images are the natural substitute for the tactile in-store experience of seeing and touching a product. 

So it stands to reason that poor quality imagery can significantly impact sales, potentially leading to customers searching elsewhere and buying off rivals. 

“Visual content today has a really big impact on shoppers,” explains Eli Orkin, CMO and founding member of Vizit, the first and only way to predict, measure, optimize and monitor e-commerce content’s effectiveness.

“Ninety-three per cent of shoppers will say the product image is the key piece of that online shopping experience. Another great is that 76 per cent of shoppers are compelled to click because of the image.”

Vizit is a startup visual AI company and visual intelligence platform that allows brands to see content exactly as consumers do, and reveal the images and designs that motivate shopper audiences to view and buy your products – and those that don’t.

“Today, the number one reason for conversion loss is bad content. We built Vizit because there was no way at scale to understand or measure if your images are working for or against you, and [whether or not] you were losing sales conversion,” shared Orkin.

“We can measure, understand, optimize and monitor the effectiveness of brand imagery. For example, on product detail pages, which images work best, and which discourage viewers from staying on the page.” 

Orkin says that ineffective content can cost a retailer up to 40 per cent of conversions and in a study of 40,000 product detail pages conducted with Amazon last year, Vizit found brands could lose 30 per cent of sales if a certain percentage of images on their product pages were not optimized. 

“The flip side we’ve seen is that when you have imagery that is top notch or the best in class in a category, you can achieve huge gains. We see double- and triple-digit gains in sales conversion rates.” 

Vizit’s AI works by learning from millions of organic interactions that consumer audiences have with visuals. “We don’t ask any questions, there are no surveys. We learn from visual data and interaction data to be able to then simulate those preferences and cover them. All of our platforms are underpinned by what we call audience lens technology. It’s like you’re looking through the eyes of your target consumer, and brands will use that at scale to quantify the impact of their images.”

The platform will rate every image a brand has online from 0 to 100 and then allows you to hone in on the exact product experiences and e-commerce listings that may need image optimization or changes to improve results with consumers. 

The growing impact of AI

Orkin observes that the retail sector is fast approaching a critical inflection point in the adoption of AI technology, like Vizit’s. Brands and retailers – regardless of size – are actively seeking out case studies and benchmarking against competitors to better understand how AI is being deployed across the industry. As the ability to measure tangible business outcomes such as sales and conversion improvements becomes more sophisticated, the value of AI is becoming increasingly clear.

According to Orkin, the perception of AI among brand leaders has shifted significantly. What was once considered a “nice to have” or experimental tool is now being integrated into business operations. 

“The focus is moving to where AI can be most productively implemented in businesses by aligning solutions to problems, such as low conversion or cart abandonment, investing in education and making sure cross-functionally that every team understands why the company is investing in an AI solution or an approach.”

Businesses are now prioritizing cross-functional education to ensure teams understand not just the “how” but the “why” behind AI adoption. Implementation strategies increasingly include built-in feedback loops to measure performance and refine solutions over time.

However, Orkin cautions that results are not always immediate. While AI operates in real-time, business outcomes can take weeks to materialize due to the complexity of data inputs and market variables. Unrealistic expectations around timing can hinder success, especially when organizations underestimate the broader operational impact AI may have across different departments.

“Some teams will expect to see a result on one day because it’s AI, because it’s real-time, and that’s just not the case. Sometimes it’s weeks, and there are variables and data we need to account for.”

To mitigate risk, Orkin advocates for a test-and-learn approach. “Start small,” he advised. “Pilot targeted initiatives before rolling them out across a full product range or portfolio. Learn what works and scale from there.”