The scope for Generative AI to transform the retail value chain goes beyond forecasting and managing customer demand during major shopping seasons – although those are significant milestones in every organization’s retail calendar. Its real potential lies in tapping into generative capabilities reshaping the entire customer journey.

From sales records to customer preferences, retail brands are data goldmines. By fusing foundational language models with this wealth of information, retailers can harness Generative AI to craft personalized shopping experiences or improve business processes like never before:

• Customer support and assistants through improved LLM-based chatbots
• Intelligent search and summarization for inquiries and sales
• Consistent product descriptions generated through AI
• Synthetic inventory data generation to simulate supply chains
• Streamline the process of product development
• Label generation enhanced accuracy
• Personalized promotions through text and image generation

Building and operationalizing a bespoke solution using foundational AI models requires several components and enablers to be successful. Components for prompt engineering, dialog management, and information indexing are necessary to extract the best out of an LLM. When coupled with various NLP Accelerators such as document parsing, speech-to-text, and text embedding an end-to-end solution can be developed and deployed.

At Tiger Analytics, we’ve worked with various retail clients to elevate retail CX and work productivity and CRM with an AI/ML-driven customer data garden, streamlining and automating targeting models. Here are our observations

Streamlining Product Descriptions for Better Consistency and Cost Savings

Writing product descriptions for the entire catalog of products is a time-intensive and manual activity for most retailers. Add to this, the variations in consistency in terms of tone of writing style across different departments and countries make this a difficult problem to solve. Retailers need to ensure that the descriptions are relevant and concise to facilitate more conversions. They also need to keep the writing consistent across their e-commerce portals, campaigns, and digital content.

Generative AI can make this process smoother while being more cost-efficient. Customized LLMs can be trained to generate automated descriptions based on product attributes and specifications. Content can be standardized to the company’s style and tone for use across media. For retailers with an evolving product portfolio, this becomes a more scalable way to write and maintain product descriptions. Such a solution can be developed by fine-tuning a foundational LLM such as GPT, T5, or BART with annotated product data, product catalogs, and relevant SEO keywords. By incorporating human feedback, the descriptions can be further tailored to specific styles and needs.

Illustration for Consistent Product Description Solution

Customer Support with Better Understanding and Efficiency

The biggest problem with chatbots before LLMs was that they could not converse in natural language. This led to frustrating experiences for users who would eventually give up on using the bot. As many of the bots in the past were not well-linked with human agents, it led to low customer satisfaction and churn.

LLMs are a perfect solution to this problem. Their strength lies in generating natural language conversational text. They are also good at summarizing vast amounts of text into concise and understandable content. To develop a customer assist solution that works, retailers can deploy LLMs in key parts of the process:

1. Converting user speech to text
2. Summarizing the user query
3. Relaying summarized information to the user
4. Helping support agents query large amounts of information and generate concise responses.

LLMs need to be used in conjunction with components such as dialog management and work on top of issues, orders, and product data to deliver contextual responses to user queries. Due to the advanced context retention capabilities of LLMs, conversations can naturally progress with continuity, allowing for in-depth dialogue over an extended interaction and the context of the user’s query can be inferred clearly. This enhances customer interaction dramatically and can make the entire support process both effective and cost-efficient.

Illustration for Customer Assist Solution

Enhanced Sales and Customer Engagement with Virtual Shopping Assistant

Generative AI has the potential to personalize the customer journey across various touchpoints, creating a seamless and engaging experience. Imagine a shopper browsing through an online store, encountering suggestions that not only match their preferences but also anticipate their desires. The assistant doesn’t merely suggest; it understands, learns, and grows with the customer. By leveraging cross-category targeting and Next Best Action (NBA) strategies for existing customers, the assistant becomes a companion in the shopping adventure, guiding with insight and relevance.

Illustration for Virtual Shopping Assistant Solution

Beyond mere navigation and suggestions, the Virtual Shopping Assistant can also be leveraged as a smart chatbot to answer any product-related questions while browsing the website. To bring this vision to life, Generative AI can be customized and fine-tuned using detailed product catalogs, customer interaction data, and behavioral insights. By incorporating human feedback and integrating it with existing systems, the Virtual Shopping Assistant can be molded to reflect the retailer’s brand, tone, and values.

Synthetic Inventory Data Generation Boosting Agility and Insight

Managing inventory data is a complex and time-consuming task for retailers, often fraught with inconsistencies and challenges in scaling. Large Language Models (LLMs) can analyze extensive inventory data, identifying trends and patterns. This allows for the creation of realistic and relevant synthetic data without revealing sensitive information, providing both privacy and comprehensive testing capabilities.

With LLMs, retailers can gain control over the data generation process, enabling augmentation and diverse scenario creation. By fine-tuning LLMs with actual inventory data and incorporating human feedback, retailers can craft a system that aligns with their unique requirements. Generative AI’s ability to produce synthetic inventory data is not just a technological advancement; it’s a strategic asset that empowers retailers to be more agile, insightful, and effective.

Illustration for Synthetic Inventory Data Solution

Quick and Market-Aligned Product Generation

In the realm of retail, manual product development is a time-consuming and resource-intensive process. The challenges extend from heavy reliance on manual efforts by designers and stakeholders to the uncertainty in market success due to fluctuating customer demand, competition, and trends. The future state of product generation, however, offers transformative possibilities. By automating concept creation, design exploration, and prototyping, retailers can accelerate product development. This shift towards data-driven decision-making and key metrics identification further refines design choices and mitigates market risk.

Illustration for Product Generation Solution

The journey from concept to product can be streamlined through AI-driven stages such as generating product concepts, evaluating, refining, and iterating designs, and prototyping and testing. By leveraging customer data and market insights, retailers can create products that truly resonate with their audience. The ability to fine-tune the development process with actual market insights and human feedback aligns product creation with customer demand. This empowers retailers to be more innovative, efficient, and aligned with the ever-changing market landscape.

Generating Labels with Enhanced Accuracy, Brand Consistency, and Compliance

In the current retail landscape, generating labels is a labor-intensive process, marked by time-consuming efforts from graphic designers and product managers. Limited customization, error-prone procedures, and numerous iterations not only hinder efficiency but also pose risks to accuracy and compliance. This complexity impacts both time and flexibility, making label design a challenging task.

The future, however, presents an exciting transformation. Leveraging AI for rapid iterations, customization, and consistency opens doors to significant time and resource savings. The ability to offer scalability for large catalogs, ensure accuracy, maintain brand consistency, and comply with regulations is more than an efficiency gain; it’s a strategic advantage. By automating the design process and focusing on the creative aspects of label design, retailers can elevate their brand’s identity and engage with their audience in a more meaningful way.

Illustration for Generating Labels Solution

Personalized Promotion for Enhanced Customer Engagement

Creating personalized promotions has traditionally been a manual, error-prone process. Manual analysis and segmentation of customer data can lead to limited insights, inefficient promotion design, and static promotions that lack relevance. The challenges in uncovering subtle customer preferences make it difficult to deliver truly personalized experiences.

The future state of personalized promotion, driven by AI, offers a transformative approach. Automated customer segmentation, real-time personalization, and adaptive promotions bring accuracy and dynamism. This shift not only improves efficiency and maximizes ROI but also ensures a seamless and cohesive customer experience throughout the shopping journey. By focusing on real-time insights and multichannel personalization, retailers can connect with customers in more meaningful ways, enhancing engagement and loyalty.

Illustration for Personalized Promotion Solution

The emergence of Generative AI in retail signals a transformative era, offering immense potential to enhance every aspect of the retail value cycle. From creating more engaging customer experiences to optimizing supply chain management, the applications are vast and varied. Retail leaders who leverage these technologies can significantly improve operational efficiencies, personalize customer interactions, and stay agile in a dynamically evolving market. By harnessing the power of Generative AI, retailers are not just adapting to current trends; they are actively shaping the future of retail, paving the way for innovative approaches and sustainable growth in an increasingly digital world. Now is a pivotal moment for industry leaders to explore and invest in these advanced capabilities, ensuring they remain at the forefront of retail innovation and excellence.

The post Beyond Bargains: 7 Powerful Ways Retail Leaders Can Use Generative AI to Level Up Their Retail Value Cycle appeared first on Tiger Analytics.

Manual Inspection:

This ensures new defects are captured accurately, but it is not scalable. Also, human error around misrepresentation of defects is possible.

Precision Measurements:

These utilize statistical process control and scale well, but are tuned to trigger alerts only on well-studied (known) error types – they’re not effective for novel defects.

Machine Vision Systems:

When trained to detect certain defect types, they scale the best to detect defects on products as soon as they leave the assembly line. But, they’ve traditionally struggled to detect unknown defects.

Approach to Detecting Novel Defects

Recent advances in deep learning have now enabled us to detect such novel defects in real-time. In this article, we outline a new-age approach to detect novel defects in products. Our approach differs from a typical deep learning model development framework in the following stages:

1. Data Collection

Any product has precisely defined dimensions with specified tolerances. Any deviation from this needs to be highlighted as a new defect. To accurately model the specifications, we generate synthetic data to train models. We model the product in a 3D modeling software and programmatically subject the images to a breadth of variations that are typically observed on the production line. We test the effect of each class of data synthesis against a real-world validation set to ensure generalization.

2. Model Selection & Training

Commonly-used best-in-class deep learning algorithms struggle to generalize from synthetic to real-world images. We made significant configuration changes (e.g., introducing normalization, modifying filter configurations, pre-training filters) to achieve that.

We used a Generator–Discriminator (Generative Adversarial Network) structure to discriminate defects from a typical specification. The generator looks to synthesize new images based on a seed, and the discriminator tries to distinguish real from the synthetic images. On training, the generator creates realistic orientations of the product when fed with seeds. And the discriminator generates a confidence score capable of accurately quantifying membership to the training set.

While making determinations on the production line, we traverse a randomly initiated seed vector down the steepest descent to arrive at the minimum in discriminatory error space. This gives us the orientation and form of synthetic image closest to the one seen on the production line.

3. Validation

We carefully curated real-world images for validation purposes. We used the performance measures captured in each set to make model configuration changes, and also training set enhancements.

Example

To illustrate the working of the model, we model a bottle cap as a semi-capsule with a notch using a 3D modeling tool. We subject this shape to a constant illumination and rotate it about a randomly chosen plane to create a large training sample. See Fig 1.

Fig 1. Creating Synthetic Data

To test the performance of the solution, we created two defective samples. One that had a bulge on top and the second with a shortened notch size. The case with a bulge on top is easier to spot manually, while a good solution would be able to capture the second as well accurately. See Fig 2.

Fig. 2. Defective Products

We do not consider surface and texture defects as part of this solution, as specialized procedures exist to capture defects in such cases.

Option 1

The first step towards identifying novel defects is to create a representation of the object. A volume of “True” images in this representational space would be the no-defect set, while cases that lie away from this set would be the defective ones.

To build a representation of the image, we used a UNET. The UNET, in this case, does not employ cross-connections, ensuring that the image descriptor completely describes the relevant components of the image. The convolution layers used a 5X5 filter with a Leaky ReLU and Batch normalization. See Fig 3.

Fig. 3. Architecture of UNET utilized

Training the anomaly detection involves propagating reconstruction error to learn filter weights and separately training a self-organizing map (SOM) on the image description. The SOM learns representations seen during the training phase and recreates the object nearest to the one passed at detection time. See Fig 4.

Fig. 4. Architecture of Anomaly Detection using the UNET

We observed two issues with this approach:

1. Learning was hindered in the UNET, so, this approach would not accurately recreate the image as gradients diminished through the filter bank layers. While this may be alleviated through residual connections, we needed a more robust technique.

2. A self-organizing map or other density-based clustering methods introduce additional training and detection time without necessarily improving novelty detection ability.

Fig. 5. Results from UNET detection on defect. (Left to Right) Defective input image – Areas reconstructed confidently by the network – Reconstruction error on the defective image

Option 2

An alternative approach is to train the same UNET employing a generator-discriminator framework. The generator helps map points in the image descriptor space to images that mimic real training images. The discriminator trains the encoding filter banks to suppress features uncharacteristic in training images. The two training signals, working in conjunction, effectively train the network to create a representative image descriptor and recreate based on a seen training image.

Fig. 6. GAN training: Generator and Discriminator work in tandem to sensitize filter banks to features characteristic in real images

Fig. 7. GAN Reconstruction error

Compared with training a UNET end to end, the GAN can reconstruct the real image with greater ability and alleviates the need for a separate clustering mechanism to locate the nearest descriptor. In the GAN, this search is replaced with elegant gradient descent.

Conclusion

We used manually inspected product samples to measure model performance across broad defect classes. The model performed accurately in identifying novel defect types and post-processing enables localizing the affected component in the assembly. This ability hinges around the number of gradient descent iterations that are performed at detection time, as discussed in the novelty identification stage in our solution section above.

On an average, the model is able to identify a defective bottle cap and localize the affected sub-component in 0.5 seconds, while a good product took 0.2 seconds to pass through using a TensorFlow backend on a GTX 1080 Ti GPU. Scaling this to run real-time would need computation parallelism.

To know more or see a demo, write to info@tigeranalytics.com.

The post Detecting Unknown Defects: Leveraging GANs in Product Quality appeared first on Tiger Analytics.