AI helps brands tap into customer voices to transform customer experiences

While customer experiences take many forms, brands primarily shape them through their products and retail networks. In a highly competitive digital world, brands scramble to differentiate themselves and capture consumer attention by focusing on great products and exceptional in-store service. When customers have consistently positive experiences, they’re more likely to be loyal and reward brands with the repeat and referral business they need to sustain long-term success. The widespread availability of customer data and AI tools equips brands with the deep insights required to ensure products and shopping experiences align with evolving consumer palates and preferences.

With so many online forums and social media sites, customers can easily tell us what they really want and care about—without being asked. This applies to feedback from both their purchase and product experiences. Though such information is abundant, extracting meaningful information from unstructured text in a time-efficient manner poses a significant challenge. Traditional pipelines have lacked the depth of operational data we need to drive key business decisions. Excessive noise in the final output has led to more ambiguity than certainty in decision-making. 

Recent, profound advances in AI have helped reduce these limitations. Specifically, within generative AI, large language models have become a revelation for their ability to understand, analyze and summarize free text in a way that resembles what a human would do. We now have many avenues to leverage these models.

Use case: An Asia-Pacific-based retailer used AI to generate real-time insights from customer feedback across hundreds of stores in its home country. The company learned what customers thought about interactions with personnel, as well as of the retail store’s ambiance and appearance. Hierarchies helped contextualize atmospherics-related responses with indicators such as temperature, scent, noise, music and lighting. Opinions on store display factors could be quantified across various functional and aesthetic elements such as layout, floor space, assortment and product presentation.
 

Use case: A leading U.S.-based airline sought to enhance the user experience on its mobile app and website. The airline’s primary focus was on evaluating both positive and negative traveler interactions facilitated by its digital platforms. A thorough assessment of the platform was conducted in comparison to key competitors.

Consumers readily share detailed insights on challenges with products they purchase. Most of this information stems from product-attribute-level details related to taste, color, packaging, ingredients such as sugar and consumption occasions. The ability to consume this information in its proper form generates amazing power for any brand because it pinpoints the root problems that lead to consumer dissatisfaction.

Use case: A global food company focused on ready-to-eat products identified multiple demand occasions such as family nights out and small house parties to tailor its offerings by size, packaging and flavors.
 

Use case: A retailer conducted a study based on internal Net Promoter Score data to identify assortment challenges for lower-value items as a key driver of consumer dissatisfaction. Solving for assortment availability and out-of-stock situations resulted in better consumer perception and a sales uptick by three percentage points. 

Most consumer-facing industries—especially food and beverage and fashion—must stay ahead of quick-changing trends. Access to near real-time data addresses this challenge by identifying emerging market patterns and giving brands time to react. This level of access adds information on primary channel preferences, which in a digital-savvy world can help companies create a better omnichannel marketing experience for customers. Innovation is derived from anticipating opportunities in the existing market. Currently, both consumer packaged goods manufacturers and retailers realize the need for faster decision-making based on customer feedback that can shape their product roadmap through analyses such as product concept innovation, next best action, market entry strategy and white space analysis.

Use case: A culinary company identified growing consumer interest in adventurous, home-based gastronomic experiences. The company introduced line-item extensions with new, unique flavors and varieties on their key brands.
 

Use case: A consumer brand focusing on protein products, including protein bars and ready-to-drink protein shakes, analyzed the protein category and underlying segments. The white space opportunities included better flavors such as mint chocolate, better texture and a sweetener option other than cane sugar, opening the door to potential new product line extensions.
 

Use case: A U.S.-based frozen foods manufacturer wanted to launch a new concept with specificity across ingredients, nutrition, price point and brand. The manufacturer moved on from leveraging traditional, qualitative research-based approaches to predict demand among novel products and prioritize profiles with the highest potential for launch success and growth in the category.

Traditional research methods come with pitfalls such as sample size limitations, complex experiment designs and leading questions. An AI-based approach gives distinct advantages such as data velocity, unfiltered product reviews and organic feedback, as this approach uses data sources that capture unaided customer expression. What customers do is more relevant than what they say. That’s why AI-based methods can enhance (and not necessarily replace) traditional research, making their execution more complementary than disruptive.