How AI is Transforming Retail and How to Leverage It

Since ChatGPT disrupted the internet in late 2022, the surge in Artificial Intelligence has begun, leaving us without any doubts that we are experiencing a genuine revolution. Although we are currently facing some confusion about where exactly AI can be applied to make significant improvements, retail, once again, appears to be at the forefront of embracing innovations, with personalization being one of the most prominent areas where AI has made a difference.

The way consumers shop keeps changing, and the pace of these changes has been breakneck in recent years. Customers fluently switch from one channel to another, going back and forth to compare prices and delivery options and read reviews. Foremost, they feel no strings attached when it comes to brands, readily ditching them with zero remorse when they decide that their needs could be better fulfilled elsewhere. They actively seek opportunities to make purchases beyond web search engines.

According to a McKinsey study, nearly half of consumers—and approximately 70 percent of millennials and Gen Zers—state that they rely on social media, celebrities, articles, or blogs for purchase inspiration.

The range of online product purchasing has also changed over time. Initially, it mainly involved hardware, RTV, and AGD. Later, fashion entered the game, and then things progressed rapidly—even groceries, once considered internet-immune, made their way online, with nearly 40 percent of US consumers saying they do at least some of their grocery shopping online.

Online retail has become a vast and cutthroat competitive market, where customers exhibit zero loyalty and zero patience—for page loading times, item deliveries, or corporate "greenwashing." For retailers who have grown accustomed to competing primarily on prices, this shift has been quite a shock.

The main battlefield to win customers and maintain attention is solid UX, which can be a make-or-break for any retail activity. However, it is personalization that seems to be the most promising and demanding area and—at the same time—the most crucial success factor for all UX-oriented efforts.

Why? Because solid UX, perceived as a seamless customer journey, visually pleasant UI, intuitive navigation, and lightning-fast performance, has become a standard, while personalization is like a cherry on top of the cake.

Benefits of Hyper-personalization in Furniture Retail

Personalization and hyper-personalization offer significant advantages across all realms of retail, even in the most demanding ones, such as the furniture industry. By tailoring every aspect of the shopping experience to individual customer preferences through data-driven insights, furniture retailers can align with each customer's unique style, needs, and preferences, fostering a deeper emotional connection to the brand.

For the furniture industry, personalization means customized product recommendations, immersive virtual room visualizations, and personalized messaging. These not only increase the likelihood of successful conversions but also cultivate long-term loyalty as customers recognize the retailer's commitment to meeting their specific requirements.

Challenges in Delivering Personalization

Despite the evident benefits of personalization, marketers agree that incorporating it into their marketing strategy can be difficult. Among the main challenges marketers must face while dealing with it, data-related issues are at the top of the list. 

The Data Challenge

The main problem is not the lack of data, but quite the opposite: an overwhelming amount. The vast sets of unstructured data are difficult to digest and convert into meaningful business insights. To achieve this, the right set of dedicated tools and systems is required to collect data from disconnected sources such as CRMs, ERPs, marketing automation systems, sales platforms, and analytical tools. Subsequently, the data must be cleaned to prepare it for further analysis and eventual incorporation of AI.

Having data under control is the key to assembling a complete and accurate view of any given customer from fragmented pieces scattered across various databases. Only once the data is preprocessed—cleaned, structured, and evaluated in terms of quality—can retailers begin to consider AI.

AI models are only as good as the data that powers them.

Limitations of rule-based recommendation engines

Delivering personalized recommendations is not new, but traditionally, it was based on rule-based systems where human-predefined conditions determined the content served to users. In straightforward scenarios and industries where certain preferences are well-defined, that was sufficient.

However, when rule-based engines must capture complex relationships between users and items, adapt to dynamically changing user behaviors, or handle vast datasets that reflect user preferences, they turn out to be insufficient.

Complex and customizable furniture products

The challenges related to personalization increase as the complexity of the products we attempt to sell grows. When a product offers numerous customizable options, creating a unique version for each person becomes more difficult and requires additional time and effort.

While it is possible, it must involve visualization tools that transparently communicate possibilities and costs, along with effective production management.

However, achieving this balance can be challenging; striking the right equilibrium between personalization and complexity may disrupt the customer journey. 

AI-Powered Personalization Solutions

AI-powered personalization solutions, well before the advent of ChatGPT, took the lead in delivering tailored experiences to customers. They primarily harnessed Machine Learning models but also employed Computer Vision and Natural Language Processing to cater to individual preferences and needs, especially in the domain of complex products such as customized furniture.

Here are the most crucial AI technologies used in driving personalization:

Natural Language Processing (NLP)
NLP enables communication between humans and machines using natural language. With NLP, AI can understand, interpret, and generate responses to customer questions. NLP powers chatbots and intelligent assistants, making a significant impact on enhancing customer service.

Computer Vision
Computer Vision (or Machine Vision) allows AI to analyze and interpret images and videos. In the realm of customer experience, AI employing Computer Vision can:

• Recognize customers' faces
• Identify products
• Analyze users' behavior in stores
• Provide interactive visual experiences

Machine Learning 
Machine Learning is a technology that empowers AI systems to learn from vast amounts of data. With ML, AI can analyze and understand patterns, offering valuable insights. ML algorithms can provide personalized recommendations, offers, and content. They can also predict customer behavior, aiding companies in optimizing services and marketing.

Recommendation systems based on AI

Machine learning-based recommendation systems are pushing traditional rules-based systems back, and companies are racing to create the perfect mix of AI and data to provide users with a pitch-perfect tool that saves time and increases conversion rates.

The effort is undoubtedly worth taking. According to Datatechvibe, Cencosud, the third-largest listed retail company in Latin America, has started using a 'machine learning (ML) based recommendation system,' which resulted in a 600% increase in click-through rates and a nearly 26% increase in average order value compared to their prior non-ML driven approach.

Building an effective AI-fueled recommendation engine, however, is not an easy task. It requires collecting relevant data about users and products, preprocessing it to ensure it is clean and ready for analysis, choosing an ML algorithm or/and deep learning techniques such as neural networks, training the algorithm with historical data, and evaluating and optimizing if needed.

There are no off-the-shelf solutions, but even considering the cost and time, AI-powered engines have proven themselves as a future-proof investment. Machine learning algorithms improve over time, in contrast to traditional, rule-based systems that assume user preferences remain the same.

Virtual Furniture Configurators With Realistic 3D Renderings

Furniture brands have been a laggard in delivering immersive digital experiences to their customers, but recently they are catching up quickly. Many brands had begun developing 3D configurators or augmented reality solutions to provide clients with the ability to see items as they would actually arrive, enhanced with individual customization options. 

By offering a tangible preview of the product, virtual configurators reduce the risk of disappointment upon delivery, enhance customer engagement, streamline the design and selection process, and reduce the likelihood of product returns or exchanges due to mismatched expectations.

Chatbots provide personalized customer support

And last but not least–chatbots. This is by far the most common use of AI in retail. These tools, designed to converse with customers and provide them with the most accurate solutions, became one of the hottest trends in marketing years ago. However, with the rise of Generative AI, their impact has become even more significant.

Chatbots can deliver hyper-personalized content and offerings tailored to individual customer behavior, personas, and purchase history. Additionally, AI can enhance sales effectiveness and performance by offloading and automating many mundane sales activities, thereby freeing up the capacity to spend more time with customers and prospective clients

By 2024, consumer retail spending via chatbots worldwide will reach $142 billion—up from just $2.8 billion in 2019, according to Juniper Research.

The common denominator of all these activities is personalization, and Generative AI capacities in that area surpass the ones traditional AI has. In the sales process, Gen AI extends support beyond initial engagement, assisting throughout, from proposal to deal closure.

Serving as a virtual assistant, it offers customized recommendations, reminders, and feedback, boosting engagement and conversions, and during deal progression, Gen AI provides real-time negotiation guidance and predictive insights from historical transaction data, customer behavior, and competitive pricing analysis. 

Post-sale Gen AI supports onboarding and retention. It delivers personalized training content for new customers, with chatbots providing immediate assistance.

Leveraging Data and Analytics

AI, both generative and 'traditional', is only as good as the data that feeds the algorithms. This is why companies should thoroughly consider their strategy before implementing specific solutions. 

Typically, this groundwork becomes the main challenge businesses struggle with, as many of them still operate on legacy systems that are connected by 'duct tape'. This results in bottlenecks and fragmented data flow, which ultimately hinders the ability to integrate AI effectively. 

Data preprocessing, cleaning, transforming, and integrating are among the first and crucial steps in embarking on the AI path.

Collecting omnichannel customer data into integrated databases

Enhancing omnichannel strategy with AI-driven personalization follows a similar path. Its initial step involves gathering information about customer interactions and behaviors from various communication channels, such as social media, websites, mobile apps, email, and more, and then consolidating this data into a unified and interconnected database.

The objective is to create a comprehensive and holistic view of each customer's interactions and preferences across diverse touchpoints. This integrated data empowers businesses to gain a deeper understanding of their customers, personalize their experiences, and make informed decisions to enhance marketing, sales, and customer service strategies.

Identifying insights through customer analytics

Once data is gathered in a unified environment, and ready to further process there is a time for incorporating specific tools, solutions, and techniques to address a given issue. 

Using statistical techniques and data mining methods, machine learning, or predictive analytic platforms it is easier to automatically segment the target audience and uncover insights and patterns within the specific cluster. Then, businesses can apply the derived insights to personalize marketing messages, offers, and interactions with customers.

What metrics should be used to quantify personalization impact?

Evaluating the impact of personalization in retail might appear challenging initially, but the tried-and-true marketing metrics can be effectively employed for this purpose.

Depending on the specific question, the impact of personalization can be measured by comparing conversion rates or click-through rates (CTR) between personalized and non-personalized experiences; and monitoring average order value (AOV) to determine whether personalized experiences result in increased spending per transaction compared to generic experiences; analyzing revenue per user to assess the average revenue generated from users who experienced personalization versus those who did not; evaluating customer lifetime value (CLV), and more.

The choice of metrics should be in line with the specific business objectives and goals that have been set for the personalization initiatives.

Case Studies of Furniture Retailers Successfully Implementing AI

Mobilia

The positive impact of implementing an AI-fueled chatbot on customer experience was proven by Mobilia. This furniture retailer experienced a challenging surge in both sales and customer service inquiries through their online store. To address this, Mobilia decided to utilize retail-specific conversational AI, which automated 83% of the total 14,758 customer service requests received through chat.

IKEA

IKEA chose to train call center employees as interior design advisors, enhancing its home improvement services. The company's goal was to delegate routine customer inquiries to an AI bot named Billie, streamlining operations. By embracing AI-driven automation to handle standard queries, IKEA's call center team focused on addressing more complex service requests and engaging with customers through live chat, ultimately enhancing the overall customer experience.

CITY Furniture

CITY Furniture implemented AI features like Camera Search, a Shop Similar recommendation carousel, and the Discovery Button, allowing shoppers to upload images and instantly discover similar products from CITY's inventory. Clicking on product images leads to result pages featuring similar items, enhancing the shopping experience. The introduction of these tools has significantly increased conversion rates, resulting in a 5.27X boost, along with a 26.3% increase in average order value (AOV). Moreover, the average revenue per user has impressively grown by 440%.

Key Takeaways and Next Steps

Artificial Intelligence is a true game-changer, not only in retail, but retail seems to be one of the most receptive industries to embrace the potential of AI in everyday business. The reason is obvious. Retail companies long ago began blending the online and offline worlds to provide customers with a unified, consistent experience regardless of the sales channels they use. As a result, they have managed to collect vast amounts of data ready for monetization.

And that's a significant advantage. With modern eCommerce systems consisting of robust backend platforms capable of swiftly processing huge datasets and frontend applications reaching clients across all touchpoints, employing Artificial Intelligence and Machine Learning to streamline processes through personalization or predictive analytics is fully feasible. Additionally, there are no concerns about data misuse as customers consent to the collection and use of their data in sales processes. Receiving personalized offers that align with their needs speeds up their purchasing, providing them with benefits.

However, the challenge can be in creating a comprehensive AI strategy that goes beyond a specific use case, even as beneficial as personalization. Companies considering AI implementation, including in retail, should align their business and AI strategies to ensure that implemented solutions cover their foundations.

AI-powered personalization should act as the catalyst for the business strategy, aligning with the same key performance indicators (KPIs) crafted to incentivize growth and competitive advantage.