- How does generative AI build upon the legacy of AI in ecommerce?
- Key generative AI modalities to be leveraged in ecommerce
- Deploying generative AI in ecommerce: consumer perspective
- Leading generative AI use cases in ecommerce: business perspective
- Leveraging generative AI for ecommerce takes dedicated effort
- Don’t get caught in the hype, focus on the gains
It has always been challenging for retailers to consistently grow and defend market share, let alone to deliver groundbreaking technology. But lately, generative AI in ecommerce has given ecom leaders more capacity to innovate at a minimal cost.
In the hyper-competitive eсommerce landscape, gen AI enables today’s retailers and brands to seamlessly and automatically serve their customers needs at scale. The best of their use cases offer insight into all-time highs across personalization, engagement, conversion rates, revenue and efficiency. Let’s explore the latest proven use cases, for the sake of approvable budgets and doable timelines.
How does generative AI build upon the legacy of AI in ecommerce?
AI and ecommerce have had a long-lasting relationship way before generative AI tools stepped into the limelight. Personalized product recommendations, dynamic pricing, live support, and demand forecasting are just a few of the multiple capabilities artificial intelligence has ushered into the ecommerce industry. However, most of these AI commerce applications are based on predefined rules, lacking versatility and cross-task adaptability.
Unlike traditional AI ecommerce solutions, generative AI, typified by foundational models like ChatGPT, showcases higher adaptability. It can learn from data without explicit programming, excels at tackling unstructured data, and can imitate that personal touch customers expect from retailers.
Let’s see how well non-generative AI and generative AI perform when applying them to ecommerce chatbots.
|Basis of distinction
|Limited ability to learn and evolve, needs profound retraining for new types of cases.
|Learns and adapts over time through continuous learning.
|Provides consistent, but generic responses.
|Provides dynamic, pointblank responses, imitating human-like conversation.
|Functions according to predefined rules and classifies the sentiment as negative, positive, or neutral
|Able to identify and classify complex sentiment patterns in customer queries.
|Provides scripted responses.
|Offers highly tailored interactions based on customer behavior and previous interactions.
As we see from the table, traditional artificial intelligence does the job when it comes to automating customer support. However, gen AI takes it a step further and brings more personalization into the use case. Similarly, generative AI tools can support other existing AI applications in ecommerce and make them more receptive to real-time learning.
Key generative AI modalities to be leveraged in ecommerce
According to DataHorizzon Research, the generative AI for ecommerce market is anticipated to grow from $4.2 billion in 2022 to $18.2 billion by 2032. 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.
|– Product descriptions
– Personalized marketing and sales collateral
– Messaging and notifications
|– Customer service and support
– Personalized online shopping journey
|– Personalized product search
– Product recommendations
|– Inventory and supply chain management
– Fraud detection
– Sentiment analysis
– Social listening
– Customer and market analysis
– New product analysis
|– Product images and ads generation
|– Voice search
– Voice-based product recommendations
– Voice-activated shopping carts
– Soundtrack generation for marketing purposes
|– Video generation for marketing purposes
– Video product description
– Product tutorials and manuals
|– 3D product catalogs
– Virtual fashion design
– New 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.
Deploying generative AI in ecommerce: consumer perspective
There is nothing more effective than offering a one-of-a-kind experience for an influx of customers. So, no wonder over 60% of retail organizations employ artificial intelligence to improve customer interactions, with 40% dedicating teams and budgets to the technology.
Just like its parent technology, generative AI accounts for many applications that upgrade online shopping experience on ecommerce platforms and beyond.
- Personalized product visualization
Customization 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.
- 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, gen 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.
- Human-like chatbots
Traditional 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.
Generative AI can also supplement digital agents with natural language processing capabilities that enable the bots to process natural human language inputs (voice or text) and serve up empathetic outputs for after-sales support and issue resolution.
- Product discovery and search personalization
In product discovery, generative AI can analyze user preferences, behavior, and past customer purchase history to offer personalized recommendations. In 2023, over 50% of retailers applied gen 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 smart assistant will make recommendations. Gen AI powered tools can also interpret uploaded images and process short video clips.
Leading generative AI use cases in ecommerce: business perspective
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. 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.
2. Market research
When testing the waters of new markets and customer cohorts, retailers 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, retailers 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.
3. Planning for promotions and campaigns
Generative AI technology can also supercharge sales and marketing campaigns of retailers with highly personalized customer loyalty programs and discount structures. Smart 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.
4. 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 gen AI — one particularly valuable endgame — is its analytical capabilities. By analyzing and deriving insights from customer data, gen AI tools can tell retailers what advertiser categories to draw to their RMNs.
Within the network, gen 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.
5. 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.
6. 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 retailers 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.
7. 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 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.
Don’t miss a chance to uncover new opportunities with gen AI
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 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.
Don’t get caught in the hype, focus on the gains
The potential of generative AI in ecommerce is infinite, stretching from more efficient 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 gen 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.
Let’s begin your gen AI journey together
In ecommerce, generative AI has taken over a lot of repetitive tasks, including customer support, product descriptions, and ad generation. The technology also assists retailers in forecasting demand, maintaining optimal inventory levels, and providing novel customer experiences.
According to Precedence Research, the global artificial intelligence in ecommerce market size is expected to achieve $22.60 billion by 2032. We can assume that artificial intelligence will gain even more traction in the industry, ushering in new, competitive capabilities.
Artificial intelligence elevates the customer experience, improves the accuracy of sales forecasts, amplifies marketing with personalized messaging, automates follow-up abandoned cart inquiries, and more.
Artificial intelligence improves lead retargeting, enhances customer experience, optimizes pricing strategies, and enables hyper-personalization in marketing. This increases conversion, leading to more sales.
Before adopting AI models, retailers need to make sure models are compliant with applicable regulations and have enough training data to prevent model bias.