How New AI Strategies Are Helping Bridge the Consumer Expectation Gap

To say the quiet part out loud, there’s a widening disconnect in the world of retail today.

One of the most significant challenges, and often blindspots, that retailers face is the delta between how consumers feel about their retail experiences, and how retailers themselves perceive their performance in terms of the retail experience.

Those disconnects can manifest in what signals and benchmarks retailers choose to focus on. In other words, to use the language of statistical analysis, it is a problem of validity in the data retailers are using to measure performance: what retailers are often using to understand their CX performance does not necessarily conform to how their customers feel about their shopping journeys.

To learn more about how consumer expectations and behaviors are evolving—as well as how retailers are innovating through harnessing advancements in AI to better close those gaps—RETHINK Retail partnered with Lily AI to ask over 1,000 North American shoppers in the fashion, home, and

beauty sectors what they thought about their shopping experiences, AI, and how comfortable they are with how both are progressing together to provide personalized retail experiences.

The survey yielded a range of key insights, including what channels shoppers today are focusing on and what tools they prefer to use; when asked which e-commerce technologies they’ve used for shopping, ‘on-site search,’ ‘product reviews,’ and ‘social media shopping’ dominate at 71%, 52%, and 42% respectively, with significant implications for where retailers can focus their efforts—and their tools.

This report will dive into the challenges around closing the retail-consumer expectation gap, as well as the new suite of AI tools that can help do exactly that. We also provide an overview of our survey’s latest findings to help brands and retailers understand what their customers want, how they’re shopping, and how to work best for them.

The Competitive, High-Tech Struggle to Convert Customers

While the retail world has worked diligently to recover from the challenges of the last many years, we’re not out of the woods yet.

The next few years will continue to be hard on consumers, with knock-on effects on their spending habits. The ongoing cost-of-living crisis is likely to continue the broader trends of less-inherently loyal, ‘choosy’ customers who want the best in personalization, quality, convenience, value, and overall experience from the retailers they choose to shop with, highlighting the need to boost CX investment through 2024 and beyond.

Indeed, when it comes to bridging the consumer expectation gap within the customer journey, the aim is to empower that journey in ways that most respond to customer needs, wants, and preferred channels. While optimizing varying search channels is an

70% of customers quit their searches without buying

according to The New York Times

obvious first step towards better meeting consumer expectations, it’s important to enhance consumer product discovery more holistically.

The ongoing cost-of-living crisis is likely to continue the broader trends of less-inherently loyal, ‘choosy’ customers who want the best in personalization, convenience, and overall experience from the retailers they choose to shop with, highlighting the need to boost CX investment through 2024 and beyond.

In general, understanding the complexity of the discovery process for your customers is simply key to capturing market share and remaining competitive. That process often starts—but doesn’t stop—at search. According to The New York Times in September 2023, “An estimated 70% of [customers] quit their searches without buying.” That may already seem like a lot, but in the retail market environment of 2024, improving those numbers even marginally can make a huge difference on your bottom line.

Furthermore, retailers must be looking to optimize aspects of the consumer discovery journey that aren’t strictly tied to search. These include social media (the usual suspects such as TikTok and Instagram, yet

also niche social spaces like Lemon8 and WeAre8) and the role authentic influencers play in promoting new products and brands, as well as inspiring and reigniting micro and macro trends.

Ultimately, the call to continue to invest in CX is not new within the industry. Rather, what changes are the technological advancements that impact market dynamics, such as how the rise of smartphones and social media radically altered consumer shopping habits.

Responding competitively to the vastness of the digital age means increasing operational speed and productivity via efficiency-driving

If you go back to basics, time after time, no matter how far back you look, when you look at customer choice and customer behavior, the single biggest reason a customer will either abandon a cart online or walk into a store and walk out without buying anything will be ‘I couldn’t

find what I was looking for.’”

Vimal Kohli // Chief Analytics Officer at Lily AI

innovations while continuing to enrich the customer experience via deep, data-driven processes and products that reflect personal preferences

More specifically, at a time when so many people access product information and engage in discovery via a wide range of channels, retailers need smarter product discovery tools—to empower the customer journey with the latest AI tools and machine learning algorithms.

Yet, as with other major tech advancements in retail’s past aimed at addressing long-standing challenges, the key to success lies in timely and effective implementation

Furthermore, companies want to make sure they leverage these new technologies responsibly: argues Kirat Anand, Chief Community Officer with RETHINK Retail, “Socially responsible values and ethics resonate with shareholders and employee communities alike, yielding an increase in market cap and talent retention.”

Navigating all of this to find the right AI applications for your business—and the right partner to guide your

Navigating all of this to find the right AI applications for your business—and the right partner to guide your way—depends on clearly knowing your challenges, goals, customers, and the sophistication of the tools at hand.

Japandi

way—depends on clearly knowing your challenges, goals, customers, and the sophistication of the tools at hand

Given the incredible buzz around generative AI (GenAI) in particular, however, retailers seeking solutions should bear in mind the range of other AI applications that can complement GenAI or that may be better suited to certain tasks altogether. That includes proven AI technologies such as computer vision, machine learning, and deep learning. These proven AI technologies have been operating within the industry for decades and feature a wide range of qualitative applications with differing specialties

Notes Sasha Wallinger, Founder & CEO of Blockchain Style Lab, “One of the most common misconceptions around AI is that it is new. Artificial Intelligence technologies have been in use for 20+ years, fueling search engines like Google, speech integration like Siri, and a variety of chatbots in marketing and communications.” Wallinger goes on to argue that while advancements such as OpenAI’s SORA and ChatGPT are bringing unprecedented attention to AI, it is critical to keep a more balanced view.

Returning to the need for retailers to take a multifaceted approach to optimizing how their customers can discover and search for the products that best suit their needs, the incredible marketing success of micro-trends such as #barbiecore (one of the better-known in recent memory) highlights the power of those social media trends.

Micro-trends are defined by their transience, with other recent micro-trends in the fashion and beauty world including ‘Old-Money Aesthetic’ and ‘TomatoGirl Summer.’ Equally important to consider are macro-trends, or trends that are more stable than their microtrend counterparts. In the fashion and home worlds, these can include enduring trends shoppers invest in for multiple seasons; Quiet Luxury and Modern Farmhouse are two recent examples.

It’s understandable if the retail manager today can feel that all of that is a lot to absorb and implement, but it is here that AI can step in by monitoring trends on a moment-to-moment basis, connecting customer language (i.e., what they truly care about) with the products that best drive conversions and generate loyalty.

In other words, with the right AI tools, companies will be that much better at analyzing relevant trends across wide swathes of data, increasing opportunities to be part of the next wave.

Argues Alicia Esposito, Vice President of Content with Retail TouchPoints, the greatest opportunity for AI in this arena is indeed ‘contextual commerce’:

“Consumers are looking beyond the basic product recommendations and service interactions; they’re looking for rich and contextual content and product inspiration that aligns with what they want, need, and what others like them want and need too. They want high-quality, curated content that can inspire them further in their discovery and shopping journey.”

It’s here we can again turn to our survey to peer into what exactly consumers want, what they prioritize, and how retailers can leverage the right solutions to help close the gap between what customers care about and how retailers understand–and respond–to those priorities.

How Customers Shop and What They Want in 2024

The majority of respondents were born between 1965 and 1996 (69%), while 21% were born before 1965 and 11% after 1996.

When asked for their go-to methods for product discovery and inspiration (with multiple answers permitted), a clear majority indicated ‘a search engine like Google’ (59%) or a digital ‘marketplace, like Amazon’ (52%). Other high performers were search bars on a retailer’s site and on-site category browsing at 44% and 41% respectively, while searching on social media came in at 27%—a growing trend.

‘On-site search,’ ‘product reviews,’ and ‘social media shopping’ dominate the eCommerce technologies respondents most reported using at 71%, 52%, and 42% respectively. Interestingly, ‘chatbots/virtual assistants,’ ‘augmented reality,’ and ‘virtual shopping events/streaming’ all came in at around 15%, categories that largely didn’t even exist a decade ago.

When asked how they shop for new trends, the answers saw a relatively even spread, with ‘perusing trusted retailers’ online stores,’ ‘search engines,’ ‘social media ads, posts, or profiles,’ and ‘trends in brick-and-mortar stores’ all coming in at 33-45% of respondents. Common ‘frustrations’ were similarly distributed, with ‘retailers stocking similar items, but not exactly what I’m looking for,’ ‘products not meeting expectations upon delivery,’ and ‘out of stocks’ at 41%, 38%, and 28% respectively.

These questions tell key aspects of the story retailers need to know: that when it comes to discovery for both products and trends, digital modalities rule the roost, with even more advanced methodologies (such as AR) gaining significant traction in recent years. A retailer that can accurately and reliably get customers to the specific product they want via smarter responses to inputs and predictive algorithms is going to see more conversions than their competition, period.

Yet, when examining the data, it is important to keep in mind that to ‘hyperfocus’ on search bars

would be to miss the whole, emergent picture. As demonstrated, surprising numbers of respondents— even within a dataset that is primarily middle-aged— are ‘casually browsing’ or leveraging social media networks to seek what they want (or don’t yet know they want).

That’s the difference between ‘window shopping’ and targeted searches, and to maximize conversions, you want both experiences to be working efficiently. This distinction is also evident in searches for branded versus non-branded categories, such as (e.g.) “Adidas Superstar Sneakers” and “white tennis shoes.” The respective stages of the shopping journey for these sets of customers offer meaningful insights for marketing strategists.

When asked whether they were familiar with GenAI, 63% replied ‘Yes,’ which may be striking in light of how much discussion has centered on the technology for over a year (and important to keep in mind when thinking about the comfort level some customers may have with the technology).

59% of respondents indicated a search engine like Google was their go-to method for product discovery and inspiration

according to survey conducted by RETHINK Retail and Lily AI

A retailer that can accurately and reliably get customers to the specific product they want via smarter responses to inputs and predictive algorithms is going to see more conversions than their competition, period.

When asked whether they’d be more or less likely to shop with a retailer using AI, 25% indicated ‘more likely,’ 24% indicated ‘less likely,’ and 35% said they were unsure (while 15% noted that it ‘didn’t matter’ to them). That indicates a pretty clear split as of the time of the survey, as does it demonstrate that many are still ‘feeling out’ this newer generation of AI technology.

That sense of wariness can be seen in how respondents felt about whether AI could make it quicker and easier to find the right products, 52% answered ‘yes’ to some degree, while the most significant single response was ‘not sure’ at 35% (the remainder answered ‘no’ to some degree at 13%).

This trend of either uncertainty and/or fairly clear splits between ‘yes’ and ‘no’ was found with other questions as well, such as whether ‘respondents believe AI would make them feel more confident about their choices’ or ‘respondents would be more likely to purchase if an online store uses AI to offer personalized recommendations.’

Additionally, when asked if they ever abandoned an online shopping session due to ‘discomfort or distrust’ related to the use of AI, a majority (nearly 50%) indicated ‘yes.’

It’s because of these consumer sentiments that retailers must partner with expert AI solution

providers that are trustworthy and reliable to enhance retail experiences and build trust, loyalty, and credibility with consumers. Keeping with the theme that implementation is everything, customers must feel that the responsible, ethically sound use of AI technologies is a priority for brands they seek to trust.

When asked about the online shopping areas in which they’d most appreciate AI-driven personalization, respondents indicated nearly every area: from ‘more detailed product descriptions’ to ‘product recommendations based on my shopping history’ to ‘personalized discounts’ and ‘virtual try-on features,’ between 37% and 46% of respondents indicated enthusiasm.

Finally, when asked if they were comfortable with sharing some personal information such as “shopping preferences, sizes, or style interests,” a clear majority chose either ‘very likely’ or ‘likely’ at a combined 66%—indicating that customers continue to be generally more comfortable with targeted information being leveraged to better serve them.

Overall, the survey results indicate that the respondents feel positive about AI and its potential for even greater personalization. While some people are still ‘feeling out’ the technology, a strong majority are clear on the need for retailers to use it transparently and responsibly. This ‘cautious enthusiasm’ tracks with how consumers have

In which of the following online shopping areas would you appreciate more AI-driven personalization?

PRODUCT RECOMMENDATIONS BASED ON MY INTERESTS AND PAST PURCHASES

MORE DETAILED PRODUCT DESCRIPTIONS

VIRTUAL TRY-ON FEATURES FOR CLOTHES, ACCESSORIES, OR MAKEUP

STREAMLINED AND SIMPLIFIED CHECKOUT PROCESS

PERSONALIZED DISCOUNTS AND OFFERS

CUSTOMER SERVICE CHATBOTS FOR IMMEDIATE ASSISTANCE

NONE

OTHER

Source: RETHINK Retail 2024 Consumer Study

Imagine you’re shopping for a piece of clothing, but you don’t have a specific product you want to purchase. What’s your go-to method for product discovery and inspiration?

SEARCH ON A SEARCH ENGINE, LIKE GOOGLE

SEARCH ON SOCIAL MEDIA OR TIKTOK FOR PRODUCTS

USE A SEARCH BAR ON A RETAILER’S SITE

CASUALLY BROWSE CATEGORIES ON A RETAILER’S SITE

SEARCH ON A MARKETPLACE, LIKE AMAZON

TALK WITH A CHATBOT ON A RETAILER’S SITE

ASK FRIENDS OR FAMILY FOR IDEAS

OTHER

Source: RETHINK Retail 2024 Consumer Study

oversize neutral color women’s tops

accepted data-centric technologies into their lives in the past.

As time marches forward, the normative status of AI will progress as well. What ‘normativity’ means in the context of AI is how accepted it is by the general public as an ‘everyday technology,’ how that public perceives and tolerates its risks, and to what extent AI becomes part of broader policy discussions that may affect retailers.

Those policy discussions include the ongoing debate around how to define available and permissions training data for today’s Large Language Models

(LLMs). Recently, the New York Times’ well-publicized lawsuit against OpenAI centers on the copyright debate stirring at the heart of these emerging AI applications.

Yet, it also helps to highlight the way industry-specific models can help to provide better results tailored to retailer’s needs. For example, when working with a partner like Lily AI, proprietary data sets can be leveraged that are specific to fashion, home, and beauty retailers in particular, helping to clarify the question of what the AI is leveraging to do its job while making its outputs more reliably relevant.

How AI Empowers Customers to Identify and Reach Their Needs

We can see from the survey data that retailers must keep in mind that while the most publicly visible AI technologies, generative AI and LLMs, form a major part of the future of commerce, they are still emergent and rapidly evolving technologies that require significant trust-building (not unlike robotics and mobile location-based services).

The world of ‘proven AI,’ such as computer vision, machine learning, deep learning, and natural language processing, has a long history of innovation and efficiency gains in the industry to date. Regardless, retailers should be looking at how to make significant investments into AI now; brands that invest in digital customer engagement today are seeing figures such as a 90% increase in revenue. Yet, that brings us back to the problem of what AI solution to use for what function—and who can help you figure it out.

Regardless, retailers should be looking at how to make significant investments into AI now; brands that invest in digital customer engagement today are seeing figures such as a 90% increase in revenue.

That gap in the vernacular that the consumer is using to describe products and the vernacular that a merchant is using to describe products, that gap is what Lily AI is solving.”

Purva Gupta // Co-Founder and CEO of Lily AI

Proposed Product Title:

Hydrating Multi-Protection Anti-Aging Moisturizer with SPF-15

For many retailers, the top ‘function’ is personalization, be it marketing and advertising or on their website. While some effective product recommendations are driven in part by Personal Identifiable Information (PII) and consumer segment info, they can also be driven by retailer-specific data and a combined analysis of shopper engagements, effective branding, and more to guide brands on how to capitalize on their unique consumers’ preferences.

These include behavioral cues, search term analysis, products clicked, and other deeply granular product attributes useful to the kinds of ‘inferential marketing’ AI is only going to continue to master, all while helping IT teams keep things safer for consumer privacy and data security than was previously possible.

It’s within both more traditional personalization metrics and those subtler, less traditional, yet no less impactful indicators that technology providers such as Lily AI are leveraging modern AI tools to help retailers thrive.

Proposed Product Description:

Refresh, soothe, and protect dry skin with this lightweight moisturizer. Expertly crafted for normal to dry skin types, experience the benefit of 24-hour moisture.

• Broad Spectrum Sunscreen: Defend against harmful UV rays

• Glycerin and Fruit Extracts: Ensure deep hydration and a refreshing feel

• Dermatologist Tested: Suitable for all, minimizing pore blockage

Lily AI is a vertical AI exclusively built for retail. They use a combination of generative AI, computer vision, natural language processing, machine learning, and deep learning to “inject consumer-centric language throughout the retail ecosystem” to deliver upwards of 9-figure revenue lift for retail clients. Specifically, their ‘Product Attribution’ and ‘Product Content Generation’ services inject human-centered language throughout the retail technology ecosystem to elevate and enhance the shopping experience.

Applications range from the aforementioned site search, Google Sponsored Ads and Product Listings, product recommendations, product title and description content generation and, critically, assortment planning and demand forecasting. Interoperable with eCommerce, marketplace, and product management platforms, Lily AI maximizes existing technology investments through improved product attribution and descriptions, enhanced discovery, and higher customer conversion.

Lily AI also emphasizes the need to work to optimize multiple search types rather than solely site search. As our survey demonstrates, customers are leaning on off-site search modalities (e.g. Google) to find products even more so than on-site. Optimizing for both scenarios helps to round out an AI solution’s capability to enhance the entire product discovery process

Other key forms of search include ‘Exploratory Search,’ or the kind of ‘casual browsing’ noted above, wherein customers aren’t certain of what they want (or are simply engaged in shopping as a general activity that they enjoy) and are more likely to be focused on broad categories rather than specific products.

Lily AI, like other growing players in the AI space, has helped its customers to see significant success via its smart, built-for-retail, and customer-centric applications of AI technologies to meet retailers’ goals, including global brands such as J.Crew and Bloomingdale’s. In the case of thredUP, sell-through was increased by 15% via enriched product data

CLOSURES

OCCASION

VAMP

COLOR

TOE STYLES

DRESSING STYLE

MICROTREND

FUNCTIONALITY

EMBELLISHMENTS

I think where Lily has separated themselves is their ability to describe products in more nuanced customerfacing attributes. They’re not indecipherable, machine only. They’re very easy to use in everyday business.

I think the power of using a common language for product attribution that Lily can help drive will unlock many benefits for companies as they think about transitioning from an internal retail language to a product language that is common to the customer.”

SVP of Digital at a Global Footwear Brand

The real magic here is leveraging AI as a tool to augment human capability and allowing employees to focus on more complex tasks that require creativity, critical thinking, and emotional intelligence. AI ultimately allows many of us to do more faster and achieve new heights personally and professionally.”

Ahmed Naiem // President and Chief Revenue Officer at Lily AI

generated by Lily AI. For another multi-brand retailer, Lily AI’s recommendations were able to provide greater accuracy at scale, “driving 7-8 digit revenue lift.”

In summation, while GenAI and LLMs are younger technologies that people are still exploring, they are critical investments for competitive retailers along with all of the other tried-and-true applications AI presents today.

By providing a detailed look at their technologies and proofs of value to potential retailers, AI providers can deliver a more balanced view of the technology, so retailers can make smarter decisions on solutions that will drive their businesses forward. Retailers must get the implementation of new technologies right—particularly ones customers are still ‘feeling out’—in order to build trust with customers

In other words, while there are myriad challenges and unknowns during this period of fast-paced innovation and invention, there are even greater opportunities, but it is only by finding the right tools—or the right retail-savvy partners—that retailers adrift amongst rapidly evolving technologies can understand how to best ride out the sea change that is AI in retail.

Yet, no matter the tools used, it bears re-emphasis that the focus for retailers should remain on creating closer, lasting bonds with consumers that are informed first and foremost by an accurate understanding of what those people want, what their true priorities are, and how retailers can best meet them.

By always starting there, true personalization—and a truer understanding of what your customers expect from their shopping journeys—can be achieved with the tools best tailored for the job.

The focus for retailers should remain on creating closer, lasting bonds with consumers that are informed first and foremost by an accurate understanding of what those people want, what their true priorities are, and how retailers can best meet them.