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.

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.

How it works

Today’s users – whether customers or employees – expect products, applications, and services to be intuitive to their demands. According to research, 4.48 billion people are currently using social media worldwide and approximately 7.74 billion are connected to IoT devices.

From blogs, tweets, mentions, and weblogs to sensors, iBeacons, and smart hangers/bins/racks, there’s a massive treasure trove of data that when accessed and interpreted can help decode customer preferences and deliver personalized experiences.

Personalization already forms a significant metric in serving relevant content to customers. And the business impact of recommended content is pretty evident. Here’s what the data says:

• 80% of Netflix viewer activity is driven by personalized content from the Netflix Recommendation Engine – helping save over $1 billion per year
• 38% of click-through rates on Google News are from recommended links
• 28% of people would like to buy more music, if they find what they like

In the retail space, recommender engines scour through customers’ likes, searches, and shopping carts to curate personalized recommendations based on historical data and shopping behavior. Using Artificial Intelligence and Machine Learning models, the system provides insights into buying intent, shopping preferences, details on why customers switch, what they’ll buy next, and what factors lead them to recommend a brand.

Since most retail organizations already have a recommender engine of some sort, why aren’t all of them able to harness it effectively to derive powerful customer insights?

Here are a few challenges:

• Lack of a unified customer record across all channels
• Global distribution online – at home and across stores
• No “golden record” for analytics on customer buying behavior across all channels
• Data repositories on website traffic, POS transactions, and in-home services exist in silos
• No analytics dashboard across all types of data
• Limited ability for targeted marketing to specific segments

At Tiger Analytics, we adopt a modular approach and develop solutions that help in effectively building powerful customer insights.

The Approach:

1. Customer segmentation & Baseline LTV
2. Mapping Strategic intents with target customers
3. Personalized treatment -Offer, cadence channel
4. Beyond the core -expand the offer base, new intents
5. Customer Data Platform (CDP)
6. ML Orchestration and Integration
7. Self-service Platform & BI
8. Experimentation & Value Measurement

How building a 360-degree Customer View in GCP can solve this problem

GCP provides several data analytics design patterns, which can be leveraged across industry segments to build analytics solutions like recommendation systems and many more.

GCP delivers targeted marketing and data storage savings and:

• Provides a golden record of data insights for targeted, customized marketing
• On-premises data warehouse offload to GCP BigQuery (BQ)
• Unified 360° view for recommendations of similar products
• Analytics dashboard joins clickstream w/ transactional data
• Summary data stored in BigQuery, can be queried with web apps
• Data warehouse offload to BigQuery – saving millions in recurring expenses
• Neo4j AuraDB fully managed graph database for real-time graph-based recommender system

– Insights from social media activity
– Purchase history across all channels

• Data: Clickstream, unstructured, and structured

Weaving together different data sources for a 360-degree Customer View



360-degree Customer – Graph Schema

Here’s a graphical representation of customer information using concepts of graph theory. Recommendations can be generated in real-time since retrieval and search happen quickly.

The circles indicate nodes representing customers and activities. The ratings/reviews given by the customer to the services are represented as edges that demonstrate the relationship between users and activities. Each node and relation may contain properties to store further details of the data.



360-degree Customer View using GCP BQ and Neo4j

Google Data Cloud provides a portfolio of solutions to facilitate the implementation of these elements of data strategy – right from collecting the data to getting real-time actionable business insights using Google Cloud-native tools, open source, third-party products, and solutions available on the Google marketplace.

Here’s a detailed technical view of the proposed GCP Data Platform architecture:



Going Phygital with the Recommendations and Personalization

According to industry sources, retailers across the world are going back to their offline store formats. In fact, the data shows that in the United States, retailers have so far this year announced 4,432 store openings, compared with 1,954 closings (due to Covid and the pandemic) resulting in a net of 2,478 openings. As the fight over the customer’s wallet continues, brick-and-mortar retailers can steal a page from their online counterparts’ sales strategy by using 360-degree Customer View to gain valuable insights into customer behavior and equip their customer-facing teams with the right information to engage their customers, develop trusted relationships, and achieve positive outcomes like solving customer problems and up-selling/cross-selling products.

In fact, at Tiger Analytics, we’ve used various customer insights generated through GCP to help a major online retailer improve cross-selling, effectively harness micro-data on shopper location, and thus curate personalized shopping experiences – real-time, customized promotions, etc. The historical data we’ve gathered has even provided useful insight into in-store design.

The 360-degree Customer View provides clarity on not only what the customer wants, but also what the retailer needs: improving customer retention, increasing lifetime value, and overall customer service efficiency.

Sources:

https://www.kdnuggets.com/2015/10/big-data-recommendation-systems-change-lives.html

https://www.cnbc.com/2022/08/02/mall-owners-retailers-still-opening-stores-despite-recession-fears.html

https://umaine.edu/undiscoveredmaine/small-business/resources/marketing-for-small-business/social-media-tools/social-media-statistics-details/

https://www.lighthouselabs.ca/en/blog/how-netflix-uses-data-to-optimize-their-product

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]]> https://www.tigeranalytics.com/perspectives/blog/private-equity-firms-facing-data-analytics-paradox/ Thu, 02 Dec 2021 16:42:44 +0000 https://www.tigeranalytics.com/?p=6732 Data analytics is crucial for Private Equity (PE) firms to navigate a diverse client portfolio and complex data. Despite challenges such as data overflow and outdated strategies, a data-driven approach enables better decision-making, transparent valuation, and optimized investment opportunities, ensuring competitiveness in a dynamic market.

The post From Awareness to Action: Private Equity’s Quest for Data-Driven Growth appeared first on Tiger Analytics.

]]> Data has become the lifeblood of many industries as they unlock the immense potential to make smarter decisions. From retail and insurance to manufacturing and healthcare, companies are leveraging the power of big data and analytics to personalize and scale their products and services while unearthing new market opportunities. However, it has been proven that when the volume of data is high, and the touchpoints are unsynchronized, it becomes difficult to transform raw information into insightful business intelligence. Through this blog series, we will take an in-depth look at why data analytics continues to be an elusive growth strategy for Private Equity firms and how this can be changed.

State of the Private Equity (PE) industry

For starters, Private Equity (PE) firms have to work twice as hard to make sense of their data before turning them into actionable insights. This is because their client portfolios are often diverse, as is the data – spread across different industries and geographies, which limits the reusability of frameworks and processes. Furthermore, each client may have its own unique reporting format, which leads to information overflow.

Other data analytics-related challenges that PE firms have to overcome include:

• No reliable sources and poor understanding of non-traditional data
• Archaic and ineffective data management strategy
• Inability to make optimal use of various data assets
• Absence of analytics-focused functions, resources, and tools

These challenges offer a clear indication of why the adoption of data analytics in the PE industry has been low – compared to others. According to a recent study conducted by KPMG, only a few PE firms are currently exploring big data and analytics as a viable strategy, with “70% of surveyed firms still in the awareness-raising stage.”

Why PE firms need to incubate a data-first mindset

So, considering these herculean challenges, why is a data analytics strategy the need of the hour for Private Equity firms? After all, according to Gartner, “By 2025, more than 75% of venture capital (VC) and early-stage investor executive reviews will be informed using artificial intelligence (AI) and data analytics.”

First, it’s important to understand that as technology continues to skyrocket, a tremendous amount of relevant data is generated and gathered around the clock. And without leveraging data to unearth correlations and trends, they can only rely on half-truths and gut instincts. For instance, such outdated strategies can mislead firms regarding where their portfolio companies can reduce operating costs. Hence, the lack of a data analytics strategy means they can no longer remain competitive in today’s dynamic investment world.

Plus, stakeholders expect more transparency and visibility into the valuation processes. So, Private Equity firms are already under pressure to break down innovation barriers and enable seamless access and utilization of their data assets to build a decision-making culture based on actionable insights. They can also proactively identify good investment opportunities, which can significantly help grow revenue while optimizing the bandwidth of their teams by focusing on the right opportunities.

Some of the other benefits for PE firms are:

• Enriched company valuation models
• Enhanced portfolio monitoring
• Reduced dependency on financial data
• Pipeline monitoring and timely access for key event triggers
• Stronger due diligence processes

Final thoughts

The emergence of data analytics as a game-changer for Private Equity firms has caused some to adopt piecemeal solutions – hoping that it could reap low-hanging fruits. However, this could prove to be hugely ineffective because it would further decentralize the availability of data, which has been this industry’s biggest problem in the first place.

In reality, the key is for Private Equity firms to rethink how they collect data and what they can do with it – from the ground up. There’s no doubt that only by building a data-led master strategy can they make a difference in how they make key investment decisions and successfully navigate a hyper-competitive landscape.

We hope that we helped you understand the current data challenges Private Equity firms face while adopting a data analytics strategy and why it’s still a competitive differentiator. Stay tuned for the next blog in the series, in which we will shed light on how Private Equity firms can overcome these challenges.

The post From Awareness to Action: Private Equity’s Quest for Data-Driven Growth appeared first on Tiger Analytics.

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