AI in Retail

Decoding Shopper Intent With AI For A Seamless Customer Experience4 min read

March 14, 2022   |   3 min read
ai for customer experience

Decoding Shopper Intent With AI For A Seamless Customer Experience4 min read

Reading Time: 3 minutes

Before understanding how to use AI for customer experience management in e- retail, let’s look at the current scenario of online shopping. The global eCommerce market is growing at the rate of $5.55 trillion. Given the competitive landscape, the average time spent by shoppers on a website is only 3.49 minutes. Understanding shopper intent becomes crucial and keywords don’t necessarily give the full picture. Cracking shopper intent is key to retaining them on the site for longer as well.

Retailers should understand shopper intent and behavioral patterns to 

  • Optimize website experience with better recommendations that are relevant to the shopper and thus accelerate product discovery. 
  • Gain a competitive advantage by offering personalized experiences at every step of the shoppers’ journey.
  • Offer a seamless customer experience, which in turn, increases customer retention rate.

Here’s how brands can achieve this with AI to improve customer experience

Create Rich Shopper Profiles

There is a wide range of attributes through which we can understand a shopper’s preferences. Understanding shopper profiles will get retailers closer to decoding their true intent behind a search for a product. For example, using AI a fashion retailer can recognize the colors a customer likes, understand her style, and then make her shopping journey seamless. AI considers different factors when building a profile through a comprehensive understanding of every individual shopper.

88% view a brand’s products as having higher quality if they feel like the brand is listening to their needs and are willing to share their data for a better shopping experience. uses real-time and historic behavioral data to generate rich shopper profiles. This works for both known and anonymous customers as we capture visual affinities (Eg-color, pattern, shape), and non-visual affinities (Eg- brands, categories, price). However, creating shopper profiles isn’t always enough.

Invest in Retail Personalization

While we understand the importance of creating shopper profiles, a retailer should also be updated with the current lifestyle changes of a shopper. Sounds complicated? Here is an example. Natalie is getting married in 3 months and is searching for wedding dresses on her favorite site which has a personalization engine powering recommendations. But now, she still sees recommendations of wedding dresses even after visiting the site to search for regular clothes multiple times a full year after her wedding.

These recommendations are bound to turn her away from the site instead of retaining a loyal shopper. Instead, a site using’s personalization engine will understand that she was shopping for an occasion and not show recommendations of wedding dresses after she purchases one. 

The personalization platform needs to be sensitive to fluctuations in browsing and spending patterns due to life events. This boils down to understanding the true intent of a given shopper.

Furthermore, many retailers have found that around 80% of online catalogs remain undiscovered. Recommendations lead them to relevant products, making it easier and faster to discover products they are most likely to purchase.

Ecommerce retailers should ideally focus on personalized recommendations at every touchpoint of the shopper’s journey. These recommendation strategies need to be unique depending on the page it appears i.e. Homepage, Category Page, Product Detail Page, etc. For example, in a category page, a personalized sorting according to every shopper’s profile—helps shoppers discover relevant products faster.

Make Most of Product Data

Product data is often inaccurate, inconsistent, or inadequate to power shopper experiences across channels. Granular product data can aid in product discovery, increasing AoV and decreasing cart abandonment rates. Our AI-powered data management solutions extract detailed product attributes from images, text, and videos through NLP, OCR, text extraction, and video processing. This allows easier and faster product discovery for the shopper.

With retailers realized a 51% increase in catalog accuracy for better personalized search and product discovery. 

Moreover, when shoppers are taken to the “No Results” pages often, they tend to lose track of the shopping experience offered. So it is important to provide alternatives when an exact match isn’t found. This can be automated with AI to deliver the ideal customer experience by featuring “Next Best” product recommendations that can provide shoppers with relevant alternatives to what they were looking for.

68% of shoppers are unlikely to return to a website or store that doesn’t provide a satisfactory customer experience. –Forrester  

On the whole, improving product discovery with the foundation of rich product and shopper data is fundamental for eCommerce growth and a seamless customer experience. helps shoppers find products relevant to them and convert faster with dynamic personalization across channels.

ABOUT THE AUTHOR engineers bespoke AI transformation roadmaps for enterprises across industries. Retailers to resellers, auto-extracting data from files to extrapolating fashion styles, 150+ conglomerates in five continents recruit How can we help yours?