Now, picture this instead: the data pulls itself together, relevant risks are flagged in real time, and the report drafts itself. You simply review and hit send.

This kind of seamless orchestration and automation has become a reality with the right tools and platforms. By connecting disparate data, automating workflows, and embedding intelligence into decision-making, organizations can reduce complexity and improve productivity.

At Tiger Analytics, we focus on three foundational pillars when building an analytics solution that is optimized and streamlined for efficiency and effectiveness:

• Reliable data
• Creating extensive data lakes with new governance processes
• Setting up connectors across data applications
• Strong analytics engine
• Managing and maintaining the diverse dependencies as the solution evolves
• Improvements to the framework (Agentic or otherwise) or dependencies
• Efficient implementation
• Identifying the right set of tools/framework dependencies to onboard
• Identifying internal/external data dependencies
• Building orchestration and governance on those dependencies

Over the years, we used this foundation to build accelerators and SOPs, which connect business needs with domain context and technical knowledge, for faster and smoother implementation.

Agentic AI platforms like Google Agentspace follow the same principles to help users across the enterprise quickly access information from various sources, summarize and understand the data, and take action with the help of AI agents. They provide the flexibility to integrate custom accelerators and SOPs, along with the structure needed to set up custom governance frameworks that ensure integrity throughout the decision-making process.

In this blog, we explore regulatory compliance in retail banking and demand forecasting in retail, and the role AI agents can play in reducing risk and improving outcomes.

Use case 1: Automated regulatory reporting and compliance monitoring in retail banking

Retail banks navigate a maze of compliance and regulatory requirements such as Basel III, GDPR, and more. As of 2023, 88% of global companies said GDPR compliance alone costs their organization more than $1 million annually, while 40% spend more than $10 million. These regulations demand extreme diligence with a focus on transparency, accuracy, and timeliness.

Traditionally, banks rely on multiple systems to collect and report data:

• ERP systems for financials
• CRM tools for customer data
• Core banking systems for transaction histories
• Email communication for policy updates and legal notices

Manually piecing together fragmented data is a time-consuming and error-prone process that may expose the organization to compliance risks. Here’s where we believe Agentic AI can add value:

• Seamless data integration: Platforms such as Agentspace integrate data from various sources, including email communications, using prebuilt 2P and 3P connectors for easy aggregation and standardization of data as required for regulatory reports. This eliminates the need for manual data entry.
• Real-time compliance monitoring: Custom AI agents can be built and orchestrated to continuously monitor and analyze compliance-related data against industry standards, such as Basel III and GDPR for up-to-date reports. For example, Jira/Salesforce can be seamlessly connected with Agentspace applications through prebuilt connectors, and processes can be tracked to ensure proper compliance with policy
• Timely report generation and submission: AI agents can help automate the process of preparing and submitting Basel III liquidity reports, capital adequacy reports, and GDPR compliance documents, saving time and improving efficiency.
• Audit-ready reporting: Integrating prebuilt 2P and 3P connectors with enterprise solutions ensures every data point, action, and process is tracked, so organizations are always prepared for external audits. These comprehensive audit trails also provide much-needed transparency, thereby reducing the risk of penalties due to non-compliance.

Automating regulatory reporting helps retail banks reduce manual effort, cut down on compliance costs, and meet reporting deadlines more efficiently. Real-time monitoring and built-in validation minimize risk exposure while keeping pace with evolving regulations. In addition, the process becomes fully traceable and audit-ready by design.

Use case 2: Integrated demand forecasting & inventory management in retail

With fluctuating market conditions, changing consumer demands, and growing competition, retailers are finding new opportunities to improve operations by leveraging AI. According to a 2024 Deloitte report, 6 in 10 retail buyers in the US said that AI-enabled tools enhanced demand forecasting and inventory management. As expectations for hyperpersonalized experiences and seamless omnichannel shopping grow, AI can help retailers remain agile and respond effectively.

Traditional demand forecasting in retail is often based solely on historical sales data and fails to account for external factors such as weather, economic conditions, or cultural trends that could impact consumer behavior. As a result, retailers can face challenges managing inventory across both online and brick-and-mortar stores, leading to overstocking, stockouts, or lost sales. Here’s how AI agents can help:

• External factor integration: In addition to integrating data sources spread across the business and every touchpoint, AI agents also enable integration of external data sources such as weather forecasts, local events, trends on social media platforms, etc. This provides retailers with a holistic view of inventory and demand, and helps proactively adjust inventory levels based on real-time external conditions.
• AI-driven demand forecasting: Machine learning algorithms analyze historical sales data, customer preferences, weather patterns, economic conditions, and social media trends to predict demand with high accuracy across multiple product categories and geographic regions.
• Real-time inventory optimization: AI agents help retailers track inventory across both online and offline channels and automatically adjust stock levels based on demand forecasts. For example, if a popular product is selling faster than expected in an online store, agents can trigger automatic inventory replenishment from physical stores or external suppliers. They also enable cross-channel inventory synchronization so products are available where customers want to buy them.

With AI-driven demand forecasting enhancing forecast accuracy, retailers can reduce instances of stockouts and overstocking. This optimization lowers storage and stockholding costs and improves customer satisfaction, ensuring the right products are available at the right time. Real-time data collection and analysis help retailers make faster, more informed decisions, boosting agility and driving better business performance.

In summary

Any analytical solution is only as strong as the underlying data. That’s why every large-scale analytics transformation must begin with a robust data infrastructure and quality control. As businesses adopt large language models and Agentic frameworks, the focus shifts to ease of adoption and driving measurable outcomes at scale. To remain competitive, companies must automate complex processes and connect fragmented data sources. Platforms like Agentspace, with its multi-agent architecture, help facilitate efficient data flow, adaptive learning, and improved decision-making.

Governance is crucial throughout the deployment, operation, and scaling of AI agents. A structured approach that combines ‘human-in-the-loop’ oversight and clear operational guardrails ensures integrity and compliance of agents and agentic frameworks, aligning them with organizational objectives while maintaining ethical standards.

References

https://www.pwc.com/us/en/services/consulting/cybersecurity-risk-regulatory/library/privacy-reset.html
https://www2.deloitte.com/us/en/insights/industry/retail-distribution/retail-distribution-industry-outlook.html

The post Connecting the Dots: How Agentic AI Can Help Build Smarter Compliance and Forecasting Pipelines appeared first on Tiger Analytics.

The beauty industry today is embracing cutting-edge technology to stay ahead of microtrends and streamline product development, all while improving efficiency and innovation in an increasingly fast-paced market. Brand loyalty is no longer solely dictated by legacy brands; “digital-first” challengers are capitalizing on changing consumer preferences and behaviors. Global Industry Cosmetics Magazine found that 6.2% of beauty sales now come from social selling platforms, with TikTok alone capturing 2.6% of the market. Nearly 41% of all beauty and personal care product sales now happen online, according to NielsenIQ’s Global State of Beauty 2025 report.

Virtual try-on apps, AI/ML-based product recommendations, smart applicators for hair and skin products – the beauty industry is testing, scaling, and rapidly deploying solutions to satisfy consumers who demand personalized experiences that cater to their unique needs. Based on our observations, and conversations with leaders in beauty & cosmetics retailing, we found that to thrive in this dynamic landscape, there is a need to move beyond traditional customer segmentation and delve deeper into customer product affinity.

What is customer product affinity embedding?

Imagine a complex map where customers and products are not locations but points in a multidimensional space. Customer product affinity embedding uses advanced machine learning algorithms to analyze vast amounts of data – everything from purchase history and browsing behavior to customer reviews and social media interactions. Upon processing this data, the algorithms create a map where customers (those who have opted-in, and anonymized, of course) and products are positioned based on the strength of their relationship, with proximity reflecting the degree of relevance, preference, and engagement between them. In short, it helps capture the essence of customer preferences in a mathematical representation.

This approach provides businesses with a deeper understanding of customer-product affinities. At Tiger Analytics, we partnered with a leading beauty retailer to design a system that captures the true essence of customer preferences by focusing on both customer and product nuances. It begins with a harmonized product taxonomy and sanitized product attributes, and incorporates curated customer data from transactions, interactions, and browsing behavior. Together, these elements create an accurate and comprehensive view of customer affinities, allowing businesses to tailor strategies with greater precision.

How does customer product affinity embedding transform business decisions?

Customer product affinity embedding enhances decision-making by capturing the multidimensional interactions between business efforts, customer activities, product characteristics, and broader macroeconomic conditions. Unlike conventional machine learning approaches, which typically focus on solving one problem at a time and require custom feature engineering, this method integrates diverse business signals. Traditional approaches often isolate and aggregate these signals, but they fail to explain the overall variance or underlying business causality, limiting their effectiveness.

Caption: Matrix factorization foundation for affinity matrix

By incorporating deeper insights into customer preferences and behaviors into business strategy, beauty retailers can unlock greater efficiency, relevance, and personalization across various touchpoints. Below are a few ways customer affinity embedding can bring tangible advantages:

• Hyper-Personalized Recommendations: Take, for instance, suggesting a hydrating toner to someone with a high affinity for high-coverage foundations, thereby providing them with relevant products that match their needs and preferences.
• Smart Product Substitutions: Sarah, a regular purchaser of the ‘Sunset Shimmer’ eyeshadow palette, gets a notification suggesting a substitute — ‘Ocean Breeze’, a cool-toned palette with hues she may also enjoy — when her favorite product is out of stock.
• Store Inventory Optimizations: Optimizing inventory levels by predicting demand based on customer affinity. Businesses can avoid stockouts for high-affinity products and minimize dead stock for low-affinity ones, leading to reduced costs and improved customer satisfaction.
• Personalized Search: Traditional search relies on keywords and filters. However, these methods often miss the nuances of the customer’s intent. For example, a search for “foundation” might be someone seeking full coverage, or someone wanting a lightweight, dewy finish. Affinity embedding helps bridge this gap, ensuring more relevant search results.
• Targeted Marketing Campaigns: Consider targeting millennials with a strong affinity for Korean beauty with social media campaigns showcasing the latest K-beauty trends.
• Data-Driven Product Development: If a significant customer segment shows a high affinity for vegan beauty products, but limited options are available, the brand can proactively develop a high-quality vegan makeup line to fill that gap in the market.
• Personalized Buying Journey: Picture a customer searching for false eyelashes on the app, and then being recommended complementary items like glue and party essentials. Additionally, the system can suggest popular shades previously chosen by customers with similar preferences, creating a seamless and personalized shopping experience.

These are just a few examples of how customer affinity embedding can enhance customer engagement and improve the overall shopping experience. Other use cases, such as Trip Mission Basket Builder, Dynamic Pricing/Discounting, and Subscription Box Optimization further demonstrate how this technology can revolutionize customer satisfaction and business efficiency.

Real-world impact of customer affinity embedding on sales and engagement

Customer affinity embedding is a multi-step process that converts customer data points into a mathematical representation that captures the strength of a customer’s relationship with various products.

Caption: Functional architecture

The same embedding features can be transformed into affinity ranks, which serve as inputs for downstream ML models to generate personalized recommendations and provide insights such as:

• Product Similarity
• Customer Similarity
• Customer Affinity to Products
• Product Substitutions

Through our collaboration, the beauty retailer experienced a 4.5% increase in repeat purchases over a 12-month period. Additionally, the brand saw a 3.5% average boost in customer engagement scores within the fashion category, and a 7.8% rise in app usage. The company’s ROI for marketing campaigns also improved, with a 23-basis-point increase across digital channels.

Today, the real question isn’t just ‘what does the customer want?’ – it’s ‘how can we truly understand and deliver it?’

Understanding customer needs isn’t just about analyzing past behaviors, but rather predicting intent and adapting in real time. Customers don’t always explicitly state their preferences. Their choices are shaped by trends, context, and discovery. The challenge for brands is to move from reactive insights to proactive personalization, ensuring that every recommendation, search result, and marketing touchpoint feels intuitive rather than intrusive.

Customer product affinity embedding brings brands closer to the customer by placing the consumer at the heart of every decision. With data-driven customer understanding, brands can build deeper and more personalized connections, driving loyalty and growth.

References:

https://shop.nielseniq.com/product/global-state-of-beauty-2025/
https://www.gcimagazine.com/brands-products/skin-care/news/22916897/2024s-global-beauty-sales-are-powered-by-an-ecommerce-social-selling-boom/

The post AI in Beauty: Decoding Customer Preferences with the Power of Affinity Embeddings appeared first on Tiger Analytics.