How to Optimise PDPs to Overcome Choice Fatigue?

Even though the concept of Product Description Pages isn’t new, there have been a lot of changes in consumer buying behaviours, which have left D2C ecommerce brands in contemplation. To battle choice fatigue, how can brands personalise PDPs at scale?

Only 16% of product detail pages are personalised for customers, revealing that personalisation at scale is a continuous struggle for many global brands, Zoovu unveiled. 

The global B2C ecommerce market is continuously burgeoning with changing consumer preferences. Forrester predicts global online sales to surge from $4.4 trillion in 2023 to $6.8 trillion by 2028. Consequently, this wave is constantly prompting brands to invest in various omnichannel strategies for seamless shopping experiences. 

PDPs (Product Description Pages) are one of the driving forces behind making these strategies successful, influencing shoppers to buy more. Even though the concept of PDPs isn’t new, there have been a lot of changes in consumer buying behaviours, which have left D2C ecommerce brands in contemplation. Are they overwhelming consumers with too much information, which isn’t even relevant, leading to choice fatigue? 

What do traditional PDPs from global brands look like? 

Zoovu, an AI-powered discovery platform, conducted an in-depth analysis of over 2000 data points across 125 product detail pages from US and Canadian ecommerce brands in fashion and apparel, consumer electronics, health and beauty, furniture and home appliances, and tools and recreation. 

According to the survey, an average PDP has over 59 pieces of information about the product, including product specifications, images, videos on related products, product variations, and more. Besides that:  

• Over 90% of PDPs have customer reviews, and over 50% have two or more types of social proof. Consequently, brands are fighting hard against the rising wave of returns by experimenting with tactics to get shoppers to make more purchases. 
• Less than 50% of PDPs had a video, whereas over 7.69% of images outnumbered them. However, the scenario was completely different in the case of furniture and home appliances brands, where over 72% of PDPs had a video, or double the average number of videos per page.
• Both product customisation and personalised PDP descriptions came in below 20%, and other personalised elements, like live chat and bundling, occurred on less than 50% of the pages. 

Even though ecommerce brands use detailed PDPs on their websites, they still lack customisation. As a result, there is a high chance that a consumer will leave the website or app without buying a product. 

Where do brands lack, and how does data overload lead to choice paralysis? 

Brands constantly struggle to create result-oriented product description pages. The survey also uncovered that an average of almost 11 product recommendations can cause choice fatigue and cart abandonment. The main reason behind this is the difficulty of bridging the gap between SKUs to provide product recommendations that aren’t generic. 

Based on the analysis, the main concern for online shoppers(42%) is the quality of the products they are considering buying. This is followed by the inability to physically interact with the products(29%) before purchasing. Finally, 20% of shoppers also struggle to find enough information to make an informed decision.

Emphasising consumers’ difficulties before buying a product, Ken Yanhs, CMO at Zoovu, said, “It’s no secret that consumers, with instant access to unlimited information, are feeling increasingly overwhelmed, lost, and frustrated when shopping online. This is why optimising product pages, the last mile of the buying process, is more important than ever.”

How do we personalise PDPs at scale? 

While traditional personalisation methods like live chat struggle with scalability, genAI tools like ChatGPT present a promising alternative. Imagine AI-powered assistants such as Microsoft’s Q&A tool recommending products and answering customer questions in real time. Undoubtedly, this would create a more interactive and personalised experience.

For instance, Bullsai offers a ChatGPT-style LLM app that customises existing product titles, descriptions, attributes and tags to generate different product description versions depending on user personas. 

“Driving personalisation with generative AI is the biggest opportunity for ecommerce brands. To give customers the guidance they are seeking at the scale they need to drive real, sustainable growth,” Yanhs added.

Is incorporating open-source genAI tools enough?

Generic genAI-powered can suffer from hallucinations, bias, and inaccuracies. Brands need to ensure their AI tools are trained on high-quality product data and have clear boundaries for interacting with customers at every point of their buying journey. ML and intelligent virtual assistants can help.

According to a survey from Gartner, At least 25% of all consumer purchases online will be made by machine customers by 2030, i.e. $279 billion in sales. These intelligent virtual assistants handle a significant portion of online purchases, converting traditional filters and search tools to be more needs-based. Consequently, it will help brands to combat choice fatigue, prompting shoppers to buy more. 

Final thoughts, 

Creating the perfect product page is an ongoing challenge. Brands struggle with presenting clear information while showcasing their brand identity across countless pages. This can get messy, and well, customers crave simplicity. By leveraging AI responsibly and adapting to the evolving customer landscape, brands must create a more engaging and efficient shopping experience.