Generative AI in Ecommerce Business: 11 Use Cases and Implementation Path

Retail leaders act like pioneers, diving headfirst into cutting-edge technologies and innovative sales channels to meet the ever-evolving customer needs. The advent of generative AI in ecommerce has given ecom leaders yet another powerful and cost-effective tool to woo loyal buyers, impress would-be customers, and master the three Rs of top-grade ecommerce — serving the right offer at the right time for the right person.

But despite its no-regret efficiency gains and broad appeal, generative AI requires careful navigation. There are still risks to consider, tech capabilities to embrace, and ethical considerations to take heed of.

So here we are, breaking down the latest proven use cases of gen AI in ecommerce, sharing the secrets of how to boost ecommerce sales with generative AI, and outlining an implementation roadmap for faster gen AI wins.

What is generative AI in ecommerce?

In ecommerce, generative AI is a powerful, highly adaptable tool that enables businesses to deliver high-touch customer experience at scale, without sacrificing personalization. The technology makes use of advanced machine learning algorithms and dives into vast amounts of customer, company, and third-party data to churn out tailored product descriptions, personalized responses, and other interactions fine-tuned to each customer.

Unlike traditional AI solutions, generative AI in ecommerce showcases higher adaptability, typified by foundational models like GPT, Claude,  Amazon Titan, and others. Generative AI ecommerce solutions can learn from data without explicit programming, tackle unstructured data, and imitate that empathetic experience customers long for. 

Once powered by generative AI, ecommerce chatbots can support highly tailored interactions based on consumer behavior and previous sessions, classify complex sentiment patterns in customer queries, and provide dynamic, pointblank responses, imitating human-like conversation. Similarly, generative AI technology can support other existing AI applications in ecommerce and make them more receptive to real-time learning.

Benefits of adopting generative AI for your ecommerce business

AI and ecommerce have had a long-lasting relationship way before generative AI tools stepped into the limelight. Generative AI brought a new dimension to this synergy, ushering in a new era of personalized shopping experiences and optimized business processes. In fact, almost 50% of ecommerce leaders are making generative AI a centerpiece technology for their business. So that sends the ecommerce community a very clear signal: there is immense value in gen AI ecommerce strategy.

Redefine customer journey, all of it

Typically, retailers cover only three of the seven steps of the customer journey, leaving the rest of customer interactions unattended. Gen AI can pick up the slack, transforming the entire customer experience and enhancing customer engagement right from the initial stages of the customer experience.

For example, customers can count on a generative AI chatbot to recommend purchase ideas — based on the context, their preferences, and purchase history — and find the items that match their exact needs, all from a single interface. Smart product comparisons, product usage insights, delivery preferences, and the rest of the back-and-forth can also be delegated to conversational gen AI solutions.

Augment your digital back office 

Ecommerce businesses can also implement generative AI solutions to automate time-consuming admin tasks such as inputting product information, sales reporting, staff management, and others. Routine activities are typically prone to errors and inefficiencies, while gen AI can perform them with more precision, acting in your company’s best interests — just like your loyal employees would.

By handing off such tasks to generative AI, companies can reduce operational costs and reallocate their resources to mission-critical areas such as efficient customer service and innovative marketing strategies.

Boost efficiency across the entire retail value chain

Another benefit of using generative AI in ecommerce is its transformative potential, which, once harnessed and backed up by the company’s data, can introduce both quick wins and long-term gains into the retail value chain. 

From handling supplier negotiations to returns management and in-store operations, gen AI tools can streamline processes related to every product function, necessitating relatively few resources to deliver a substantial impact.

Supercharge retail decision making

Retailers often race against the clock, trying to analyze what caused sales dips, what trends to gear up for, and what specific smart moves their competitors made to hit it big with their last marketing campaign. And not all of this data is easy and fast to measure and monitor.

Paired with a robust AI core and a comprehensive data engine, generative AI can deliver whatever insights your ecommerce company needs at the moment and do it in a straightforward, conversational, and actionable way. You can also set gen AI analysis to be proactive or prompt additional analysis in human language.

Key generative AI modalities to be leveraged in ecommerce

According to Precedence Research, the generative AI for ecommerce is anticipated to grow from $833.11 million in 2024 to over $3,519 million by 2034. The growth this rapid can be partly attested to the fact that gen AI is a highly functional smart application that exists across multiple modalities. Gen AI content can be delivered in text, images, videos, audio, and even 3D representations.

Below, we’ve listed the key modalities of generative AI ecommerce and the specific use cases it targets in the industry.

Modality	Application	Use case
Text	Content production	– Product descriptions – Personalized marketing and sales collateral – Messaging and notifications
	Chatbots	– Ecommerce customer service tasks and support – Personalized online shopping journey
	Search	– Personalized product search – Product recommendations
	Analysis	– Supply chain and inventory management – Fraud detection – Sentiment analysis – Social listening – Customer and market analysis – New product analysis
Image	Image generator	– Product images and ads generation
Audio	Voicebots	– Voice search – Voice-based product recommendations – Voice-activated shopping carts – Soundtrack generation for marketing purposes
Video	Video creation	– Video generation for marketing purposes – Video product description – Product tutorials and manuals
3D representation	Product representation	– 3D product catalogs – Virtual fashion design – New product design
	Product design	– Turning text descriptions into 3D product models

Although multi-functional, a gen AI model’s application layer fine-tunes it to complete a specific task. 

11 generative AI use cases in ecommerce

To increase margin and customer satisfaction levels, retailers stretch themselves thin across numerous tasks. Inventory glut, complex supply chains, and an exploding number of distribution channels and customer touch points top the list of pain points. But now, they can leave business complexity and tedium to generative AI in ecommerce.

1. Personalized product visualization

Hyper-personalization is the new status quo for the ecommerce industry, with the majority of customers favoring a curated shopping experience on ecommerce websites. Generative AI lives up to the demand and allows customers to customize and play with the styles, colors, and fabrics of the products.

For example, Stitch Fix, an online personal styling service, relies on text-to-image generation to visualize an article of clothing based on a consumer’s preferences, size, budget, and style. Sephora and Ulta Beauty are also using generative AI to develop personalized skincare products.

2. Virtual try-ons

Around 19% of US beauty consumers say that virtual product try-ons would help them feel more confident purchasing products digitally. Generative AI brings the visual try-on experience to each screen, allowing consumers to create realistic representations of clothes and other products. 

Unlike traditional virtual try-on tools, generative AI applications make the fitting experience more mindful of a body shape, skin tone, and personal style. And that’s exactly what Google has done lately. The tech giant pushed out a new virtual try-on feature that demonstrates how clothes look on real models with different hair types, body types, skin types, ethnicities, and sizes.

Style transfer technique, which is another gen AI offshoot, is also helpful in transforming the customer’s vision into a realistic piece of clothing or product. In simple words, this technique allows the customer to take two images — a self-portrait and a style reference image — and blend them together to try on a new style.

3. Human-like chatbots

Traditional ecommerce chatbots and virtual assistants guide customers through basic linear flows but have a hard time thinking outside the predefined boundaries. By leveraging the power of generative AI chatbots, retailers can swap generic responses for human-like interactions and provide accurate 24/7 support to users, thus boosting customer service efficiency.  

Generative AI can also supplement digital agents with natural language processing capabilities, enabling the bots to process natural language inputs (voice or text) and serve up empathetic outputs for after-sales support and issue resolution.

4. Product discovery and search personalization

In product discovery, generative AI can analyze user preferences, behavior, and past customer purchase history to offer personalized product suggestions. In 2023, over 50% of retailers applied generative AI to curate personalized product bundles. On the same line, gen AI tools can anticipate user preferences and search intent, reducing search time to a couple of clicks. 

They also make products more discoverable by improving tagging accuracy and enabling more intuitive, conversational search. Instead of browsing hundreds of product names, customers can describe what they are looking for in their own words, and the virtual assistant will make recommendations. Generative AI powered tools can also interpret uploaded images and process short video clips.

5. Content generation assistant

Creating accurate product content for thousands of SKUs is not for the faint-hearted. That’s why gen AI-generated content was among the first use cases that picked up steam in ecommerce. Gen AI powered solutions have simplified content production for product descriptions, listings, and even tailored promotional materials. For example, Amazon has debuted generative AI tools to help sellers write product descriptions at scale. 

And the capabilities of artificial intelligence do not end there. Heinz has used generative AI to create images for advertising, while Shoplazza, an ecommerce website builder, has implemented gen AI models to transform mannequin models into real models. Whatever it is, artificial intelligence reduces the cost of content creation and streamlines manual tasks.

6. Market research

When testing the waters of new markets and customer cohorts, ecommerce businesses need to comb through vast amounts of data to inform their strategies. Social media platforms, customer insights, competitors’ ecommerce companies, and other valuable data have to be taken into account and made sense of.

Here’s how generative AI can help with analysis-related tasks in ecommerce:

• Market intelligence — gen AI can help simulate market scenarios, produce synthetic data to fill data gaps, and forecast customer responses based on historical data. 
• Information summarization — instead of spending months on research, ecommerce brands can employ AI tools to read and analyze existing material.
• Novel market and customer segmentation or product opportunities — gen AI algorithms can uncover untapped market and customer segments as well as identify new product niches within the target market. 

7. Planning for promotions and marketing campaigns

Generative AI technology can also supercharge sales and marketing campaigns of retailers with highly personalized customer loyalty programs and discount structures. Smart AI algorithms analyze customer and reference data to create tailored rewards and incentives cut out for individual customer preferences. 

Moreover, gen AI-based tools can get to the bottom of EPoS data and transactional information to unearth actionable insights on sales trends. This information can then underpin promotional efforts, inform price strategies, or set the direction for production processes according to the expected demand.

8. Boosting retail media networks

Selling media to advertisers is one of retail’s biggest new trends that has given birth to retail media networks or RMNs. RMNs help brands advertise in places naturally inhabited by consumers, while retailers driving the network get to unlock a new revenue stream. 

Retail media networks rely on loyalty and transaction data to sell ad inventory to third-party brands. So the endgame for RMNs’ use of generative AI — one particularly valuable endgame — is its analytical capabilities. By analyzing and deriving insights from customer data, gen AI tools can tell ecommerce businesses what advertiser categories to draw to their RMNs.

Within the network, generative AI tools can help advertisers tie together and optimize their ad spend. Generative artificial intelligence can also analyze the best-performing offerings of advertisers, match them to relevant consumers, and generate campaign configurations to replicate ad success. It’s a win-win for both: advertisers get the bang for the buck, while retailers get to generate more RMN revenue.

9. Supply chain and inventory management

Out of all industries, retail supply chains are the most dynamic due to ever-evolving customer demand, a large number of products, and rapid product life cycles. Generative AI adds simplicity to supply chain management by taking over the analytics inherent in the process.

Gen AI tools can analyze sales information and demand trends to make demand predictions, calculate safety stock levels, and identify slow-moving stock. These tools can also assist gen AI ecommerce businesses in:

• Running what-if scenarios to get prepared for supply chain disruptions and fluctuations in demand
• Evaluating suppliers by analyzing financial reports, performance metrics, and other data 
• Optimizing logistics routes by analyzing warehouse locations, transport links, and demand patterns
• Improving last-mile delivery by selecting the right delivery or pickup routes based on traffic conditions, weather, and other data.

10. Gen AI-driven pricing

Generative AI models support AI-driven tools in identifying the optimal path to a retailer’s sustainable financial health. To do that, AI tools perform price simulations where they create various market scenarios based on historical data, competitors’ behavior, and potential future events. Price simulations also allow ecommerce businesses to locate key-value categories and items in their portfolio, refine their pricing strategy, and spot implicit cross-dependencies between products.

Demand-based pricing is another ecommerce area where generative AI shines. Here, ecommerce owners can model demand curves based on various influencing factors such as seasonality, economic factors, and other variables to optimize pricing during spikes or slowdowns in demand.

11. Fraud detection

Generative AI and ecommerce make a powerful combo when it comes to anomaly detection. Traditional methods of fraud detection often rely on predefined rules or models, which may not capture the ever-evolving nature of fraud techniques. Conversely, generative AI stays adaptive to new fraud patterns by constantly vacuuming up consumer data and analyzing it with past customer interactions.

By understanding genuine past customer behavior patterns and previously detected fraud patterns, generative AI can simulate fraudulent activities and train machine learning systems to detect and counteract them. Paired with a conversational interface, generative AI can also notify fraud engineers about risk flow and give reliable recommendations on what to do next.

In our journey with AI, we’ve seen it redefine ecommerce to now challenging the fundamentals of commerce itself, across multiple brand types and industries. Innovation isn’t a buzz word for our clients, it’s tangible, so trust me when I say the early results are positive and in some cases staggering — it’s a game-changer.

— Chad West, Managing Director USA, *instinctools

Leveraging generative AI for ecommerce takes dedicated effort

The promise of generative AI is enticing. But it can only deliver on its promise if implemented with your unique business strategy, needs, and constraints accounted for.

Get ready for generative AI transformation

A convincing, measurable business case is the foundation for any AI-based adoption. Your business case should define a target application of generative AI to a specific business challenge and the outcomes to measure its effectiveness. Also, your business case will give you a better understanding of the data needed to train the model and the technical expertise required to set the AI infrastructure in place.

Choose the right model

The choice of a foundational model depends on your use case, the type and quality of your data, and the limitations of your infrastructure. The technologies powering the model also differ based on your requirements. You might consider implementing Generative Adversarial Networks (GANs) models for image generation, while models like GPT are more suitable for text-only applications.

Mind that gen AI tools are not compliant with industry-specific regulations automatically from the onset, so it’s imperative to identify the right training and deployment method to keep your customer data, historical sales data, and other data safe.

Train, evaluate, and fine-tune the model

The training process begins after collecting and preprocessing data. A lot of back-and-forth  identifies the optimal model architecture, hyperparameters, and training algorithm to achieve stable and safe performance. Once the model is trained, you should consistently evaluate its performance and fine-tune the model based on periodic test results.

Deploy and monitor

When the model is up and running, it’s time to make it a part of your ecommerce architecture.  

Based on your objectives, you may need to deploy it to a cloud-based service, create a dedicated user interface, or integrate the documents and knowledge databases of your ecommerce business with the model. Once the model is deployed, it needs regular performance monitoring to make sure it lives up to expectations.

Maintain and improve

AI-based models are only as good as the data powering them. Therefore, make sure to refine data patterns to prevent model drifting and update the model as and when necessary. In some cases, your model may need retraining, in other times, new monitoring processes may keep your model up to date. As your ecommerce business grows, be sure to scale the model accordingly.

Keep in mind that AI adoption success goes beyond the pilot. You need to embrace a mature and calibrated practice supported by tailored tactics and hands-on advice from an experienced vendor.

Challenges to clear before gen AI implementation

To see value from generative AI solutions, ecommerce businesses have to take heed of the following considerations.

Data quality and bias

Your gen AI application is only as good as your data. To generate accurate responses, drift your decision-making in the right direction, and automate processes, generative AI requires a rich palette of internal and external data. And not just any data, but the one that’s accurate, diverse, and representative of real-world scenarios and customer needs.

If any of these boxes are left unchecked, your generative AI solution can perpetuate biases and churn out irrelevant outputs, hurting customer experience and harming your bottom line.

Plus, the more high-quality your data is, the stronger the model’s ability to learn complex patterns, make accurate predictions, and evolve.

Along with traditional data sources, we also recommend retailers locating unstructured data sources that set them apart from other retailers. Establishing clear and consistent metadata tagging standards will help the team distill relevant data for specific AI models. 

Scale-ready adoption environment

You may already have a tech estate ready for gen AI adoption, but unless it’s scalable, you will end up with costly gen AI replications that will also be hard to upsize. The optimal gen AI architecture for retailers is function-agnostic and market-agnostic, meaning it can augment various business functions across different markets.

Techwise, an agile generative AI architecture requires the implementation of modular components that will allow for easy LLM swapping and scaling.

Data security and ethical considerations

As generative AI applications thrive on customer data, you must make sure they do it in a secure and responsible way. This includes adherence to applicable data privacy regulations such as GDPR, CCPA, PCI DSS, CalFIPA, and others. Most of these regulations mandate stringent requirements on data security and privacy, including:

• Minimizing the amount of customer data collected
• Reporting data breaches within a specific time frame
• Explicitly stating how personal data is collected and processed
• Maintaining records of processing activities, and others.

Along with regulation-induced security measures, your gen AI applications must also have tech-enabled security to keep customer data safe. Data encryption at rest and in transit, role-based access controls, network security, and other data security safeguards are non-negotiable if you want your gen AI solutions to be ethically sound and good at maintaining customer trust.

Don’t get caught in the hype, focus on the gains

The potential of generative AI in ecommerce is infinite, stretching from more efficient e commerce sales processes to unparalleled customer experiences and beyond. Instead of getting carried away with the persuasive abilities of AI technologies, retailers should rely on a specific business case to spearhead their gen AI journey.

The right choice of a generative AI model, infrastructure readiness, and dedicated human expertise cracks the code of adoption and makes generative AI a low-risk investment for ecommerce businesses and commerce media professionals.

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