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Showing posts with label customer service. Show all posts
Showing posts with label customer service. Show all posts

Friday, November 1, 2024

HaxiTAG PreSale BOT: Build Your Conversions from Customer login

With the rapid advancement of digital technology, businesses face increasing challenges, especially in efficiently converting website visitors into actual customers. Traditional marketing and customer management approaches are becoming cumbersome and costly. To address this challenge, HaxiTAG PreSale BOT was created. This embedded intelligent solution is designed to optimize the conversion process of website visitors. By harnessing the power of LLM (Large Language Models) and Generative AI, HaxiTAG PreSale BOT provides businesses with a robust tool, making customer acquisition and conversion more efficient and precise.

                Image: From Tea Room to Intelligent Bot Reception

1. Challenges of Reaching Potential Customers

In traditional customer management, converting potential customers often involves high costs and complex processes. From initial contact to final conversion, this lengthy process requires significant human and resource investment. If mishandled, the churn rate of potential customers will significantly increase. As a result, businesses are compelled to seek smarter and more efficient solutions to tackle the challenges of customer conversion.

2. Automation and Intelligence Advantages of HaxiTAG PreSale BOT

HaxiTAG PreSale BOT simplifies the pre-sale service process by automatically creating tasks, scheduling professional bots, and incorporating human interaction. Whether during a customer's first visit to the website or during subsequent follow-ups and conversions, HaxiTAG PreSale BOT ensures smooth transitions throughout each stage, preventing customer churn due to delays or miscommunication.

This automated process not only reduces business operating costs but also greatly improves customer satisfaction and brand loyalty. Through in-depth analysis of customer behavior and needs, HaxiTAG PreSale BOT can adjust and optimize touchpoints in real-time, ensuring customers receive the most appropriate service at the most opportune time.

3. End-to-End Digital Transformation and Asset Management

The core value of HaxiTAG PreSale BOT lies in its comprehensive coverage and optimization of the customer journey. Through digitalized and intelligent management, businesses can convert their customer service processes into valuable assets at a low cost, achieving full digital transformation. This intelligent customer engagement approach not only shortens the time between initial contact and conversion but also reduces the risk of customer churn, ensuring that businesses maintain a competitive edge in the market.




4. Future Outlook: The Core Competitiveness of Intelligent Transformation

In the future, as technology continues to evolve and the market environment shifts, HaxiTAG PreSale BOT will become a key competitive edge in business marketing and service, thanks to its efficient conversion capabilities and deep customer insights. For businesses seeking to stay ahead in the digital wave, HaxiTAG PreSale BOT is not just a powerful tool for acquiring potential customers but also a vital instrument for achieving intelligent transformation.

By deeply analyzing customer profiles and building accurate conversion models, HaxiTAG PreSale BOT helps businesses deliver personalized services and experiences at every critical touchpoint in the customer journey, ultimately achieving higher conversion rates and customer loyalty. Whether improving brand image or increasing sales revenue, HaxiTAG PreSale BOT offers businesses an effective solution.

HaxiTAG PreSale BOT is not just an embedded intelligent tool; it features a consultative and service interface for customer access, while the enterprise side benefits from statistical analysis, customizable data, and trackable customer profiles. It represents a new concept in customer management and marketing. By integrating LLM and Generative AI technology into every stage of the customer journey, HaxiTAG PreSale BOT helps businesses optimize and enhance conversion rates from the moment customers log in, securing a competitive advantage in the fierce market landscape.

Related Topic

HaxiTAG Studio: Leading the Future of Intelligent Prediction Tools

HaxiTAG AI Solutions: Opportunities and Challenges in Expanding New Markets

HaxiTAG: Trusted Solutions for LLM and GenAI Applications

From Technology to Value: The Innovative Journey of HaxiTAG Studio AI

HaxiTAG Studio: AI-Driven Future Prediction Tool

HaxiTAG: Enhancing Enterprise Productivity with Intelligent Knowledge Management Solutions

HaxiTAG Studio Provides a Standardized Multi-Modal Data Entry, Simplifying Data Management and Integration Processes

Seamlessly Aligning Enterprise Knowledge with Market Demand Using the HaxiTAG EiKM Intelligent Knowledge Management System

Maximizing Productivity and Insight with HaxiTAG EIKM System

HaxiTAG EIKM System: An Intelligent Journey from Information to Decision-Making



Tuesday, October 15, 2024

Unlocking the Future of Customer Interaction and Market Research: The Transformative Power of HaxiTAG AI for Comprehensive Coverage and Precise Insights

HaxiTAG AI is introducing this groundbreaking new technology into market research, customer support, and customer-facing service interactions. Whether it’s customer support, sales, or customer success teams, every conversation with your customers is an opportunity to understand your business and identify customer needs.

Understanding Customer and Market Challenges

  1. Issues to Explore and Analyze:
    The problems that need to be examined in-depth.

  2. Questions Needing Immediate Research:
    Inquiries from customers that require prompt investigation.

  3. Signals from Daily Operations:
    Routine activities that may reveal underlying issues. While most companies have a general grasp of categories they need to manage, there's often a wealth of untapped information due to human resource limitations.

  4. Listening to Customers:
    Strive to listen to your customers as thoroughly as possible and understand them within your capacity. However, as your company grows and the number of customers increases, daily communication with them may become challenging.

The Scale Problem in Customer and Market Interactions

This issue indeed accompanies success. When the number of customers is manageable, you can typically leverage your staff, sales teams, or customer support teams to gain insights and better guide your company toward greater revenue growth. But as you expand to a size where managing these vast conversations becomes nearly impossible, you’ll realize that much is happening without your awareness.

Traditional Methods of Customer Data Analysis

We believe that every large-scale enterprise is attempting to manually review and conduct small-sample analyses, aiming to collect and evaluate about 5% of conversations. This may involve checking compliance matters, like how agents handle situations, or identifying common themes in these conversations.

Ultimately, this is just sampling, and everyone is dissatisfied because they understand that it’s not a very accurate process. Then you begin involving engineers to write scripts, perform post-analysis, extract data from various customer interaction systems, and conduct lengthy analyses. Eventually, you hope to gain insights that can be tracked in the future.

The Role of Generative AI in Transformation

Next, you enter a stage of building software to look for very specific content in every conversation. But everything is retrospective—events have already occurred, and you were unaware of the signs. This is where generative AI can truly change the process.

Generative AI unlocks the incredible ability to cover 100% of the data. Now, you can use generative AI to discover things you didn’t even know you were looking for, reviewing everything at once, rather than just sampling or seeking known issues.

Practical Examples of AI in Customer Interactions

Here’s a great example: a brief interaction with a random agent handling customer chat. From this customer message, you can identify the reason for the customer’s communication—that’s your intent. Which aspects of our business are truly the root cause of this issue? The router, damaged delivery—perhaps it’s a supply chain issue. You can also gauge emotions, not just of the customer but also of your agent, which may be even more critical.

In the end, through every message, you can extract more in-depth information from a conversation than ever before. This is the service our platform strives to provide.

The Actual Impact of the HaxiTAG AI Platform

Here’s a great example from one of our clients, a wind power operator. One insight we provided was identifying defects in their wind turbine operations and maintenance. Some issues might persist for weeks without IT technical support to uncover them, potentially evolving into bigger problems. But our platform can detect these issues in real-time, significantly increasing the power generation revenue from their operations and maintenance.

The Process Behind AI Technology

How does all this work? It all starts with collecting all these conversations. This part is the non-AI mundane work, where we connect to numerous contact systems, ticket systems, and so forth. We pull all this information in, normalize it, clean it thoroughly, and prepare it for compression and processing by LLM prompts.

We have dozens of pipelines to evaluate these conversations in different ways, all of which can be configured by the user. Our customers can tell us what they care about, what they are searching for, and they actually collaborate with us to craft these prompts. Ultimately, they write the prompts themselves and manage them over time.

The Critical Importance of Accuracy in Enterprise AI

Why is accuracy ultimately the most important? When dealing with enterprise-scale operations, the primary concern is accuracy. There’s significant market concern about accuracy. Can I deploy generative AI to try to understand these conversations and truly trust these insights? When we work with customers, within seven days, we aim to demonstrate these insights to them. From that point forward, we strive to achieve 97% accuracy. However, this requires extensive sampling and trial and error. Ultimately, we seek to build trust with our customers because that will ensure they continue to renew and become long-term clients.

The Role of HaxiTAG AI in AI Implementation

HaxiTAG AI plays a crucial role in helping us achieve this goal. They not only provide our engineering team with a plethora of features and capabilities but also assist wind power domain experts, not IT specialists, in understanding the quality of the code they write through standardized components and interactive experiences. More importantly, our solution engineers and implementation engineers work with customers to debug and ultimately receive positive feedback. Customers tell us, “For certain things, the HaxiTAG AI tool is the go-to tool in this process.”

Conclusion and the Future of Self-Improving AI Systems

HaxiTAG AI has built an infrastructure layer in generative AI programs and LLM-driven large-scale data and knowledge application solutions to enhance the accuracy and reliability of AI applications while significantly lowering the barrier to entry. Our initial vision was to build a self-improving system—a system with LLM applications capable of refining prompts and models, ultimately driving accuracy and enhancing the utility of customer digital transformation.

The vision we are striving to achieve is one where HaxiTAG AI helps you turn your business data into assets, build new competitive advantages, and achieve better growth.

Related Topic

Sunday, October 6, 2024

Optimizing Marketing Precision: Enhancing GTM Strategy with Signal Identification and Attribute Analysis

In modern marketing strategies, the identification and utilization of signals have become critical factors for business success. To make your Go-to-Market (GTM) strategy more intelligent, it is crucial to understand and correctly use signals and attributes. This article will provide an in-depth analysis of signals and their role in marketing strategies, helping readers understand how to optimize signal collection and utilization to enhance the precision and effectiveness of marketing activities.

Definition and Importance of Signals

Signals, simply put, are the behavioral cues that users exhibit during interactions. These cues can help businesses identify potential customers' interests and purchasing tendencies. For example, a user may visit a product's pricing page, sign up for a trial account, or interact with a company's posts on social media. These behaviors not only reveal the user's level of interest in the product but also provide valuable data for the sales and marketing teams, allowing them to adjust marketing strategies to ensure that information is accurately delivered to the target audience.

Attributes: A Deeper Understanding of Users

However, signals alone are not sufficient to paint a complete picture of the user. To gain a more comprehensive understanding, it is necessary to analyze attributes. Attributes refer to the background characteristics of users, such as their job titles, company size, industry, and so on. These attributes help businesses better understand the intent behind the signals. For instance, even if a user exhibits high purchase intent, if their attributes indicate that they are an intern rather than a decision-maker, the business may need to reconsider the allocation of marketing resources. By combining signals and attributes, businesses can more accurately identify target user groups and enhance the precision of their marketing efforts.

Categories of Signals and Data Sources

In the process of identifying signals, the choice of data sources is particularly critical. Typically, signals can be divided into three categories: first-party signals, second-party signals, and third-party signals.

1. First-Party Signals

First-party signals are data directly collected from user behavior by the business, usually coming from the business's own platforms and systems. For example, a user might browse a specific product page on the company website, book a meeting through a CRM system, or submit a service request through a support system. These signals directly reflect the user's interaction with the business's products or services, thus possessing a high degree of authenticity and relevance.

2. Second-Party Signals

Second-party signals are data generated when users interact with the business or its products on other platforms. For example, when a user updates their job information on LinkedIn or submits code in a developer community, these behaviors provide key insights about the user to the business. Although these signals are not as direct as first-party signals, they still offer valuable information about the user's potential needs and intentions.

3. Third-Party Signals

Third-party signals are more macro in nature, typically sourced from external channels such as industry news, job postings, and technical reports. These signals are often used to identify industry trends or competitive dynamics. When combined with first-party and second-party signals, they can help businesses assess the market environment and user needs more comprehensively.

Signals and Intelligent GTM Strategy

In practice, the integration of signals and attributes is key to achieving an intelligent GTM strategy. By identifying and analyzing these signals, businesses can better understand market demands, optimize product positioning, and refine marketing strategies. This data-driven approach not only enhances the effectiveness of marketing activities but also helps businesses gain a competitive edge in a highly competitive market.

Conclusion

The identification and utilization of signals are indispensable elements of modern marketing. By understanding the types of signals and the user attributes behind them, businesses can more precisely target customer groups, thus achieving a more intelligent market strategy. For companies seeking to stand out in the competitive market, mastering this critical capability is essential. This is not just a technical enhancement but also a strategic shift in thinking.

As an expert in GenAI-driven intelligent industry application, HaxiTAG studio is helping businesses redefine the value of knowledge assets. By deeply integrating cutting-edge AI technology with business applications, HaxiTAG not only enhances organizational productivity but also stands out in the competitive market. As more companies recognize the strategic importance of intelligent knowledge management, HaxiTAG is becoming a key force in driving innovation in this field. In the knowledge economy era, HaxiTAG, with its advanced EiKM system, is creating an intelligent, digital knowledge management ecosystem, helping organizations seize opportunities and achieve sustained growth amidst digital transformation.

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Thursday, August 8, 2024

Five Applications of HaxiTAG's studio in Enterprise Data Analysis

In today's rapidly evolving field of artificial intelligence, large language models (LLM) and generative artificial intelligence (GenAI) are bringing profound changes to various industries. As a comprehensive enterprise-grade LLM GenAI solution integrating AIGC workflows and privatized data fine-tuning, HaxiTAG's studio is at the forefront of this technological revolution. This article will combine the core functions and technical advantages of HaxiTAG's studio to deeply analyze its five major application scenarios in enterprise data analysis: data exploration, data visualization, predictive analysis using synthetic data, data augmentation, and data processing.

1. Data Exploration

Data exploration is the first step in enterprise data analysis, determining the direction and depth of subsequent analysis. HaxiTAG's studio, with its highly scalable task pipeline framework, allows enterprises to easily conduct data exploration. Its AI model center offers flexible model access components, enabling data scientists to quickly query and analyze data. The adapters and KGM components allow users to interact with the system via natural language, significantly simplifying the data querying process.

For example, with HaxiTAG's studio, enterprises can build intelligent chatbots that utilize natural language processing technology to achieve real-time data queries and visualization. Such conversational data exploration not only improves efficiency but also enhances user experience, making data exploration more intuitive and accessible.

2. Data Visualization

Data visualization is the key process of transforming complex data into easily understandable graphical representations. HaxiTAG's studio, through its advanced AI capabilities and multimodal information integration functions, can automatically generate high-quality visual charts, helping enterprises quickly identify and understand patterns and trends in the data. Its RAG technology solution allows the system to generate more accurate and relevant visual content based on retrieved information.

For example, in the financial sector, HaxiTAG's studio can automatically generate market trend charts, risk assessment charts, and more, helping analysts intuitively understand market dynamics and potential risks. Such automated visualization saves considerable human effort and time while enhancing the accuracy and depth of data analysis.

3. Predictive Analysis Using Synthetic Data

Predictive analysis is an important tool for strategic planning and decision-making in enterprises. HaxiTAG's studio improves predictive model training by generating high-quality synthetic data to fill gaps in datasets, providing more balanced and diverse datasets. Its training data annotation tool system ensures the high quality and accuracy of the synthetic data.

For instance, in the healthcare sector, HaxiTAG's studio can generate synthetic patient data to train disease prediction models. Such synthetic data enriches the training datasets and helps models better identify and handle rare conditions and diverse patient profiles, thus improving the accuracy and reliability of predictions.

4. Data Augmentation

Data augmentation is a critical technique for enhancing model performance by increasing the diversity and quantity of the dataset. HaxiTAG's studio leverages its generative AI capabilities to generate realistic synthetic data, filling gaps in the dataset and ensuring the model is trained on more complete and varied data. This data augmentation method not only improves the generalization ability of models but also enhances their adaptability to different application scenarios.

For example, in the e-commerce sector, HaxiTAG's studio can generate diverse user behavior data, helping enterprises build more precise recommendation systems and marketing strategies. Through data augmentation, enterprises can better understand and predict user needs, thereby improving user satisfaction and sales performance.

5. Data Processing

Data processing involves transforming raw data into a structured format suitable for analysis. HaxiTAG's studio, through its automated data cleaning and processing functions, can efficiently identify and correct errors, inconsistencies, and missing values in datasets, ensuring data quality and reliability. This is crucial for accurate analysis and decision-making.

For example, in the financial sector, HaxiTAG's studio can automatically detect and correct anomalies in transaction records, ensuring data accuracy and consistency. This not only simplifies the data processing workflow but also enhances the accuracy and credibility of data analysis, providing a solid foundation for enterprise decision-making.

HaxiTAG's studio Basic Introduction

HaxiTAG's studio is a leading enterprise-grade LLM GenAI solution designed to provide comprehensive AI application support through the integration of AIGC (Artificial Intelligence Generated Content) workflows and privatized data fine-tuning. The platform features a highly scalable task pipeline framework, flexible AI model center, adapters, and Knowledge Graph Management (KGM) components, and advanced Retrieval-Augmented Generation (RAG) technology. These core functions enable HaxiTAG's studio to help enterprises efficiently process and analyze data, generate high-quality synthetic data, automate data processing workflows, and provide powerful data visualization and exploration tools. Its privatized deployment scheme ensures the security and privacy of enterprise data, making it an ideal choice for digital transformation and intelligent decision-making.

Conclusion

The application of HaxiTAG's studio in enterprise data analysis not only enhances data quality and analysis efficiency but also provides strong support for enterprise decision-making through its flexible architecture, advanced AI capabilities, and diverse application scenarios. As technology continues to evolve, HaxiTAG's studio will play a greater role in the field of data analysis, bringing more innovation and growth opportunities. By fully leveraging the power of generative AI, enterprises will be better equipped to meet challenges, seize opportunities, and achieve digital transformation and sustainable development.

TAGS

HaxiTAG's studio, enterprise data analysis, large language models, generative AI, data exploration tools, automated data visualization, synthetic data for predictions, data augmentation techniques, AI-driven data processing, digital transformation solutions, RAG technology in AI, financial data analysis, e-commerce data analysis, healthcare predictive models, intelligent chatbots for data, AI model management, multimodal information integration, data cleaning automation, scalable AI task pipeline, privacy-focused AI deployment

Related topic:

The Navigator of AI: The Role of Large Language Models in Human Knowledge Journeys
The Key Role of Knowledge Management in Enterprises and the Breakthrough Solution HaxiTAG EiKM
Unveiling the Future of UI Design and Development through Generative AI and Machine Learning Advancements
Unlocking Enterprise Intelligence: HaxiTAG Smart Solutions Empowering Knowledge Management Innovation
HaxiTAG ESG Solution: Unlocking Sustainable Development and Corporate Social Responsibility
Organizational Culture and Knowledge Sharing: The Key to Building a Learning Organization
HaxiTAG EiKM System: The Ultimate Strategy for Accelerating Enterprise Knowledge Management and Innovation

Sunday, July 7, 2024

HaxiTAG Studio: Leading the Future of Intelligent Prediction Tools

Overview

In the modern business environment, data-driven decision-making has become a key factor for corporate success. HaxiTAG Studio is an innovative AI tool that analyzes existing company data to provide predictions on customer retention rates and product demand. This functionality not only helps companies with strategic planning but also allows them to stay ahead of the competition. This article will explore in detail the features, applications, and advantages of HaxiTAG Studio in market research and customer insights.

Core Functions of HaxiTAG Studio

  1. Customer Retention Prediction

    HaxiTAG Studio predicts future customer retention rates by analyzing historical data. This functionality helps companies to formulate more effective customer retention strategies, reducing customer churn and increasing Customer Lifetime Value (CLV).

  2. Product Demand Prediction

    By analyzing sales data and market trends, HaxiTAG Studio can forecast future product demand. This assists companies in making more accurate decisions regarding inventory management and production planning, avoiding issues of overstocking or stockouts.

  3. No Need for Professional Data Analysis Skills

    Designed to be user-friendly, HaxiTAG Studio requires no complex data analysis skills. Its intuitive interface and clear reports enable business managers to quickly understand and apply data analysis results.

Application Scenarios

  1. Strategic Planning

    Companies can utilize HaxiTAG Studio's predictive results for long-term strategic planning. For example, based on customer retention predictions, companies can design more targeted customer care programs to enhance customer satisfaction and loyalty.

  2. Supply Chain Management

    Accurate product demand predictions enable companies to optimize supply chain management, ensuring the right amount of products are available at the right time. This not only reduces inventory costs but also improves market responsiveness.

  3. Marketing

    HaxiTAG Studio helps companies conduct market segmentation and customer profiling, designing more effective marketing campaigns and enhancing the precision and ROI of advertising investments.

Market Research and Customer Insights

HaxiTAG Studio excels in market research and customer insights. Its robust data analysis capabilities help companies gain a deeper understanding of market dynamics and customer needs, providing solid data support for market decisions.

  1. Market Research

    By analyzing market data, HaxiTAG Studio helps companies identify market trends and potential opportunities. This is particularly important for new product development and market entry strategies.

  2. Customer Insights

    HaxiTAG Studio analyzes customer behavior data to help companies understand customer preferences and purchasing habits, providing essential information for customer segmentation and personalized marketing.

Conclusion

HaxiTAG Studio is a powerful and easy-to-use intelligent prediction tool. It assists companies in strategic planning, supply chain management, and marketing, while also offering significant support in market research and customer insights. For companies looking to leverage data-driven decision-making and maintain a competitive edge, HaxiTAG Studio is undoubtedly an ideal choice.

TAGS

HaxiTAG Studio predictions, data-driven decision-making, customer retention strategy, product demand forecasting, supply chain optimization, market segmentation tool, customer profiling insights, AI for strategic planning, marketing campaign effectiveness, intelligent prediction tools.

Friday, June 28, 2024

The Application of AI in Market Research: Enhancing Efficiency and Accuracy

Market research can be time-consuming and labor-intensive. This is why 48% of marketers use generative AI for research. AI not only automates tedious tasks but also provides deep insights, helping businesses better understand market trends and customer needs. This article will explore the specific applications and advantages of generative AI in market research.

How AI is Transforming Market Research

Automating Tedious Tasks

Traditional market research often requires manually sending out surveys and organizing numerous responses, which is not only time-consuming but also prone to errors. Generative AI can automate these tasks, from sending surveys to organizing data, greatly improving efficiency. This allows researchers to devote more time and energy to analysis and decision-making instead of being bogged down by repetitive tasks.

Analyzing Sentiments and Opinions on Social Media

Generative AI can understand the sentiments and opinions hidden behind words by analyzing social media posts, comments, and surveys. Through sentiment analysis, businesses can better grasp customers' true feelings and attitudes, thereby making more targeted market strategies. For example, AI can analyze customer reviews of a product to help businesses understand its strengths and weaknesses, leading to improvements and optimization.

Discovering Hidden Trends and Patterns

AI's strength lies in its ability to scan vast amounts of data and identify trends and patterns that human researchers might miss. Through data mining and pattern recognition, AI can help businesses uncover potential market opportunities and risks. For instance, by analyzing sales data, AI can identify peak sales periods for certain products, aiding in the development of more effective promotional strategies.

Predicting Customer Behavior and Market Trends

Generative AI can predict potential customer behaviors and future market trends by analyzing data. This enables businesses to make more informed decisions regarding new product development, market promotion activities, and optimal resource allocation. Predictive analysis allows companies to anticipate market changes and maintain a competitive edge.

Personalizing Surveys

AI can also create personalized surveys automatically based on different customer groups. This not only improves the relevance and effectiveness of surveys but also saves a lot of time. With personalized surveys, businesses can obtain more accurate and valuable customer feedback, better meeting customer needs.

Specific Applications of Generative AI in Market Research

HaxiTAG's Customer Insights and Market Research Center

HaxiTAG offers powerful customer feedback software that helps businesses run surveys and understand customer shopping habits. The platform provides a large number of ready-made survey templates, enabling businesses to quickly grasp customer habits and preferences, and thereby formulate more effective market strategies. Additionally, HaxiTAG supports sentiment analysis and trend prediction, helping businesses delve deeper into customer needs and market changes.

Generative AI is revolutionizing the efficiency and accuracy of market research. By automating tedious tasks, analyzing sentiments and opinions, discovering hidden trends and patterns, predicting customer behavior and market trends, and personalizing surveys, AI provides businesses with powerful market insights. As AI technology continues to advance, market research will become increasingly intelligent and efficient. Businesses should fully leverage these AI tools to maintain a competitive edge in the market.

TAGS: 

Know Your Transaction technology, financial compliance solutions, anti-money laundering technology, real-time transaction analysis, dynamic risk modeling, multi-source data integration, intelligent transaction analysis, KYT solution for AML, FATF travel rule compliance, Counter-Terrorist Financing technology

Related topic:
Analysis of HaxiTAG Studio's KYT Technical Solution
Enhancing Encrypted Finance Compliance and Risk Management with HaxiTAG Studio
Generative Artificial Intelligence in the Financial Services Industry: Applications and Prospects
Application of HaxiTAG AI in Anti-Money Laundering (AML)
HaxiTAG Studio: Revolutionizing Financial Risk Control and AML Solutions
Analysis of HaxiTAG Studio's KYT Technical Solution
Enhancing Encrypted Finance Compliance and Risk Management with HaxiTAG Studio

Tuesday, June 25, 2024

Expanding Your Business with Intelligent Automation: New Paths and Methods

In an era of continuous technological innovation, many businesses find themselves falling behind the pace of market development. The current business environment demands not only traditional programming and hard coding methods but also the adoption of advanced technologies, such as GPT engine-driven intelligent language models (LLMs) and the integration of enterprise privatized knowledge, to achieve comprehensive automation. This new path and method offer unprecedented opportunities for businesses, helping them stand out in a fiercely competitive market.

Combining GPT Engine-Driven LLM Intelligence with Enterprise Knowledge

The GPT (Generative Pre-trained Transformer) engine-driven LLM represents the forefront of modern artificial intelligence technology. By pre-training on large amounts of data, it can understand and generate natural language text. This capability makes LLMs highly applicable across various fields, particularly in business automation.

Enterprise privatized knowledge refers to the proprietary information and data accumulated within an organization, including business processes, customer data, market strategies, and more. This knowledge is crucial for a company’s operations and decision-making. By combining the GPT engine-driven LLM with enterprise privatized knowledge, businesses can implement highly intelligent automation solutions. For instance, automated customer service systems can respond to customer inquiries in real-time, enhancing customer satisfaction and loyalty; intelligent data analysis tools can help businesses identify market trends and develop more effective marketing strategies.

HaxiTAG’s Innovative Solutions

HaxiTAG is a leading company dedicated to integrating LLM, GenAI (Generative Artificial Intelligence), and automation technologies. By partnering with other companies, HaxiTAG provides comprehensive and reliable automation solutions, significantly reducing the hassle and complexity of introducing AI language model technology.

HaxiTAG’s expert team possesses deep technical backgrounds and rich industry experience, enabling them to tailor solutions to meet the unique needs of businesses, ensuring that these technologies truly add value. Their services include not only technical implementation but also comprehensive managed services, ensuring businesses have no worries during the technological upgrade process.

Advantages of New Paths and Methods

  1. Increased Efficiency and Productivity: Automation allows businesses to significantly reduce manual operations, increasing work efficiency and productivity. For example, automated process management systems can monitor and optimize business processes in real-time, reducing human errors and time wastage.

  2. Enhanced Decision-Making Capability: Intelligent data analysis tools help businesses delve into data value, providing accurate market insights and predictive support, enabling companies to make more informed decisions.

  3. Improved Customer Experience: Automated customer service systems provide 24/7 real-time support, quickly responding to customer needs and enhancing customer satisfaction and loyalty.

  4. Reduced Operational Costs: Through automation, businesses can lower labor costs and operational expenses, improving overall profitability.

Conclusion

In today’s fiercely competitive business environment, continuous innovation is essential for maintaining a competitive edge. Utilizing GPT engine-driven LLM intelligence combined with enterprise privatized knowledge to achieve comprehensive automation is a necessary trend for future business development. HaxiTAG offers comprehensive and reliable automation solutions, helping businesses seamlessly tackle technological upgrade challenges, providing strong support for innovation and growth. By adopting this new path and method, businesses can significantly enhance efficiency, improve decision-making capabilities, enhance customer experiences, and ultimately achieve sustainable business growth.

The application of this new path and method not only helps businesses stand out in a fiercely competitive market but also drives the development of the entire industry, bringing more innovation and opportunities. In this era of constant technological transformation, businesses must continually adapt and innovate to achieve long-term development and success.

TAGS

Intelligent automation solutions, GPT engine-driven LLM applications, business automation with AI, enterprise privatized knowledge integration, HaxiTAG AI services, automated customer service systems, intelligent data analysis tools, AI-driven business growth strategies, automation in competitive markets, enhancing efficiency with AI

Related topic:

Revolutionizing Market Research with HaxiTAG AI

Developing LLM-based GenAI Applications: Addressing Four Key Challenges to Overcome Limitations

Optimizing Enterprise AI Applications: Insights from HaxiTAG Collaboration and Gartner Survey on Key Challenges and Solutions
GPT Search: A Revolutionary Gateway to Information, fan's OpenAI and Google's battle on social media

Strategies and Challenges in AI and ESG Reporting for Enterprises: A Case Study of HaxiTAG
HaxiTAG ESG Solutions: Best Practices Guide for ESG Reporting
Impact of Data Privacy and Compliance on HaxiTAG ESG System

Sunday, June 23, 2024

Automated Email Campaigns: How AI Enhances Email Marketing Efficiency

Email marketing automation has been around for some time, but advancements in artificial intelligence (AI) have significantly improved the efficiency and effectiveness of email marketing. AI can help businesses write more engaging emails and gain a deeper understanding of subscriber preferences, thereby optimizing email content and sending strategies. This article will explore the applications and advantages of AI in email marketing automation.

Applications of AI in Email Automation

Auto-Responses

AI can generate automatic email responses based on preset rules or templates. This allows businesses to quickly respond to customers even during busy periods while maintaining a professional image. For example, when a customer sends an inquiry, AI can immediately reply with an acknowledgment and provide initial solutions or next steps.

Email Sorting and Categorization

AI can learn a user's work habits and preferences, automatically categorizing emails into different folders such as "To Do," "Spam," or "Important." This automated sorting saves users time on manual email organization and enhances work efficiency.

Intelligent Reply Suggestions

When composing emails, AI can provide intelligent reply suggestions based on the context. This not only improves work efficiency but also ensures the accuracy of the responses. For instance, when replying to a customer complaint, AI can suggest appropriate apology phrases and solution recommendations.

Language Translation and Proofreading

In multilingual work environments, AI can quickly translate emails from one language to another and perform spell and grammar checks, avoiding errors and enhancing professional image. This is especially crucial for multinational companies to ensure seamless communication with international clients.

Email Summaries and Highlights

AI can extract key information from lengthy emails to generate summaries or highlights, helping users quickly grasp the email content and save reading time. For busy managers, AI-generated summaries can provide quick insights into meeting notes or project updates.

Personalized Reminders and Notifications

AI can offer personalized reminders and notifications based on the user's schedule and email content. For example, sending reminders before important meetings or notifying users promptly when receiving important emails. This personalized service helps users better manage their time and tasks.

Enhancing Email Marketing Effectiveness

- Analyzing User Data to Send Targeted Messages

By analyzing historical data and user behavior, AI can help businesses send targeted email content to increase open and click-through rates. Personalized marketing messages can be tailored for different user groups, ensuring the content matches the audience's needs.

- Predicting Optimal Send Times

AI can predict the optimal send times based on user behavior patterns, ensuring that emails arrive in the inbox when recipients are most likely to open and engage with them. This significantly boosts email open and click-through rates.

Automated A/B Testing

AI can automate A/B testing of various email elements (such as subject lines, CTAs, and design layouts) to determine the best versions, optimizing future marketing campaigns. Continuous optimization enhances email performance and increases conversion rates.

- Avoiding Spam Triggers

AI can analyze email content to flag potential spam triggers, helping businesses avoid landing in the dreaded spam folder. This ensures that marketing emails reach the target audience's inbox.

- Re-engagement Campaigns

AI can identify users likely to unsubscribe and create personalized re-engagement campaigns to win them back. These campaigns can offer special promotions or tailored content to recapture users' attention and interest.

Conclusion

AI is revolutionizing the effectiveness of email marketing. Through auto-responses, intelligent categorization, reply suggestions, language translation, email summaries, and personalized notifications, AI significantly enhances email handling efficiency. Additionally, by analyzing user data, predicting optimal send times, automating A/B testing, and running re-engagement campaigns, AI helps businesses optimize email content and strategies, improving open rates, click-through rates, and customer satisfaction. As AI technology continues to evolve, email marketing will become increasingly intelligent and efficient. Businesses should fully leverage these AI tools to maintain a competitive edge in the market.

Using HaxiTAG to create compelling emails can help write attention-grabbing subject lines, product descriptions, and even entire email drafts, attracting subscribers to click and engage with your content.

TAGS:

AI email marketing automation, automated email campaigns, AI-generated email responses, email sorting with AI, intelligent email reply suggestions, AI email translation and proofreading, personalized email reminders, targeted email marketing, optimal email send times, AI re-engagement campaigns

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Saturday, June 22, 2024

Analyzing Customer Behavior: How HaxiTAG Transforms the Customer Journey

In today's data-driven business environment, understanding customer behavior, analyzing customer profiles, and exploring potential markets and opportunities have become crucial for business success. The advancements in artificial intelligence (AI), particularly LLM and GenAI technologies, have made it possible to analyze large volumes of customer data, helping businesses better understand customer needs and behaviors. This article will explore how the HaxiTAG system leverages AI to build customer behavior analysis, customer profiling, and market research, and demonstrate how to use these insights to optimize the customer journey.

The Role of AI in Customer Behavior Analysis

Comprehensive Data Analysis

AI excels at sifting through vast amounts of customer data, including website visits, app interactions, social media activities, purchase histories, and email clicks. By synthesizing this data, AI can map out the various touchpoints customers interact with throughout the purchasing process. This allows businesses to identify any obstacles or friction points in the customer journey and make the necessary optimizations.

Identifying Purchase Patterns

AI can not only analyze single purchase behaviors but also identify customers' purchasing histories to uncover which products are frequently bought together. This information is invaluable for creating targeted upsell and cross-sell campaigns, thereby increasing the average order value. For instance, if a customer frequently buys coffee beans and filters together with a coffee machine, businesses can recommend these related products at the right time, boosting sales.

Powerful Analytical Tools

Customer Journey Analysis Based on LLM and GenAI

A standout tool is the customer journey analysis based on LLM and GenAI. This tool provides cross-channel (online and offline) customer journey insights and features data connectivity and unlimited customer data collection capabilities. With such an advanced analytical tool, businesses can instantly gain contextually relevant insights to better understand customer behavior and make timely marketing decisions.

Customer Behavior Targeting Tools and Customer Profiling Based on LLM and GenAI

Another professional tip is to use customer behavior targeting tools and customer profiling based on LLM and GenAI. These tools allow businesses to create segmented lists based on personas and engagement levels, helping identify and respond to high-intent behaviors such as website visits, email interactions, and form submissions. By doing so, businesses can interact more effectively with customers, enhancing satisfaction and loyalty.

Future Trends and Impact

As AI technology continues to advance, customer behavior analysis will become more precise and efficient. Businesses will be able to obtain real-time customer behavior data and make immediate decisions based on this data. In the future, AI will not only be a data analysis tool but also an essential assistant in strategic decision-making, helping businesses stand out in competitive markets.

Conclusion

Artificial intelligence is revolutionizing customer behavior analysis. Through comprehensive data analysis, identifying purchase patterns, and utilizing powerful analytical tools, AI helps businesses optimize the customer journey, increase sales, and improve customer satisfaction. As AI technology continues to evolve, businesses will be able to understand and meet customer needs more precisely, gaining a competitive edge in the market. Companies should fully leverage these AI tools to continuously optimize the customer experience and maintain their leading position in the competition.

TAGS:

Customer behavior analysis, AI customer journey, HaxiTAG system, LLM and GenAI in marketing, personalized customer profiling, AI market research tools, customer data insights, AI-driven sales strategies, optimizing customer experience, advanced customer targeting

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Tuesday, June 4, 2024

Revolutionizing Market Research with HaxiTAG AI

Discover how HaxiTAG AI is revolutionizing market research by providing real-time, actionable insights into customer behavior and market trends. Learn about its advantages and future trends in AI-powered data analysis.Explore the transformative power of HaxiTAG AI in market research, from analyzing customer behavior to predicting trends, and see how it can help your business stay ahead of the competition.

How Traditional Market Research Falls Short

Market research has long been the cornerstone of business strategy, guiding decisions and shaping success. Traditionally, companies have relied on methods such as surveys, focus groups, and direct customer feedback to gather insights. However, these conventional approaches come with their own set of challenges.

Firstly, the sheer volume of data that needs to be processed can be overwhelming. Collecting, sorting, and analyzing data manually is not only time-consuming but also prone to human error. Additionally, traditional methods often fail to capture real-time data, resulting in outdated or irrelevant insights by the time they are analyzed. Lastly, there's the issue of respondent bias – the data gathered may not always reflect genuine consumer behavior or sentiment.

In a rapidly evolving market landscape, these limitations can significantly hinder a company's ability to stay ahead of trends and meet customer needs effectively. This is where AI-powered tools like HaxiTAG AI come into play, offering a transformative solution to these age-old problems.

The Edge of HaxiTAG AI in Market Data Analysis

Enter HaxiTAG AI – a cutting-edge tool designed to revolutionize market research by leveraging the power of artificial intelligence. Unlike traditional methods, HaxiTAG AI can process vast amounts of data in real-time, providing businesses with up-to-the-minute insights that are both accurate and actionable.

One of the primary advantages of HaxiTAG AI is its ability to integrate and analyze diverse data sources. Whether it's social media trends, customer reviews, sales data, or web analytics, HaxiTAG AI consolidates this information to offer a comprehensive view of the market. This holistic approach not only saves time but also ensures that the insights derived are rich and multifaceted.

Moreover, HaxiTAG AI employs advanced machine learning algorithms to identify patterns and trends that might be invisible to the human eye. This predictive capability is crucial for businesses looking to anticipate market shifts and adapt their strategies accordingly. By continuously learning and improving from new data, HaxiTAG AI ensures that companies are always equipped with the latest insights.

Analyzing Customer Behavior and Predicting Trends

Understanding customer behavior is at the heart of effective market research. HaxiTAG AI excels in this area by offering deep insights into customer preferences, buying habits, and sentiment. By analyzing social media interactions, product reviews, and customer feedback, HaxiTAG AI can paint a detailed picture of what drives consumer decisions.

For example, a company might discover through HaxiTAG AI that a particular demographic shows a growing interest in eco-friendly products. This insight can inform product development, marketing strategies, and even corporate social responsibility initiatives. Furthermore, by tracking these behaviors over time, HaxiTAG AI can help predict future trends, allowing businesses to stay one step ahead of the competition.

In addition to analyzing past and present data, HaxiTAG AI can also simulate various market scenarios. This capability enables businesses to test different strategies and predict their potential outcomes before making significant investments. Whether it's launching a new product, entering a new market, or adjusting pricing strategies, HaxiTAG AI provides the foresight needed to make informed decisions.
Case Study: Successful Market Research Applications

To illustrate the transformative power of HaxiTAG AI, let's look at a real-world application. Consider a retail company looking to expand its product line based on emerging consumer trends. Using traditional methods, this process could take months of surveys and focus groups, with no guarantee of accurate results.

With HaxiTAG AI, the company can analyze data from various sources – including social media, customer reviews, and sales trends – to identify which products are gaining popularity. Within weeks, they can pinpoint not only what products are in demand but also the specific features and attributes that customers value most. Armed with this knowledge, the company can swiftly bring new products to market, tailored precisely to consumer preferences.

Another example is a service-based business aiming to enhance customer satisfaction. By leveraging HaxiTAG AI, they can monitor customer feedback in real-time, identify recurring issues, and address them proactively. This not only improves customer satisfaction but also builds loyalty and trust.

Future Trends in Market Research

The future of market research is undoubtedly intertwined with advancements in AI technology. As tools like HaxiTAG AI continue to evolve, we can expect even more sophisticated capabilities in data analysis and trend prediction. For instance, natural language processing (NLP) advancements will enable deeper understanding and analysis of unstructured data, such as open-ended survey responses and social media posts.

Moreover, the integration of AI with other emerging technologies, such as the Internet of Things (IoT) and blockchain, will open new avenues for market research. IoT devices can provide real-time data on product usage and customer behavior, while blockchain can ensure the security and integrity of data collected.

In addition, we can anticipate a shift towards more personalized market research. As AI systems become more adept at understanding individual consumer preferences, businesses will be able to tailor their offerings with unprecedented precision. This level of personalization will not only enhance customer satisfaction but also drive higher conversion rates and loyalty.

Wrapping Up: The AI-Powered Future of Market Research

In conclusion, the landscape of market research is being dramatically reshaped by AI technologies like HaxiTAG AI. Traditional methods, while still valuable, are no match for the speed, accuracy, and depth of insights provided by AI-powered tools. By integrating diverse data sources, identifying hidden patterns, and predicting future trends, HaxiTAG AI empowers businesses to make informed, strategic decisions with confidence.

As we look to the future, it's clear that the role of AI in market research will only continue to grow. Companies that embrace these advancements will be well-positioned to navigate the ever-changing market landscape, stay ahead of competitors, and meet the evolving needs of their customers.

For businesses looking to stay competitive and innovative, adopting AI tools like HaxiTAG AI is not just an option – it's a necessity.

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AI-powered market research, HaxiTAG AI advantages, customer behavior insights, predictive analytics tools, market trend forecasting, real-time data analysis, AI in business strategy, transforming market research, data-driven decision-making, advanced machine learning for market research

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Saturday, June 1, 2024

How Artificial Intelligence Enhances Sales Efficiency and Drives Business Growth

In today's highly competitive market environment, companies are increasingly focusing on various means to improve sales efficiency and conversion rates. The rapid development of artificial intelligence (AI) technology has brought unprecedented changes to the sales field. This article explores the application of AI in sales and its specific role in enhancing sales efficiency and driving business growth.

Creating Personalized Email Drafts:

Using AI tools like ChatGPT, sales teams can quickly create personalized email drafts. AI can analyze potential customers' interests and needs, generating email content that better fits their requirements. This not only saves time but also increases the open and response rates of the emails. This personalized approach, compared to fully automated outbound strategies, has tripled response rates, further proving that AI-driven teams can sometimes outperform standalone AI.

Identifying Potential Customer Traits and Patterns:

AI technology can analyze big data to identify common traits and patterns among potential customers, such as shared educational backgrounds or industry experiences. This information helps sales teams more accurately target potential customers, increasing the success rate of sales. Moreover, this data-driven decision-making process is more scientific and efficient than traditional experiential judgments.

AI-Enhanced Human Performance Audio Solutions:

In telemarketing, AI technology plays a significant role. By redesigning the phone dialing system and standardizing communication processes, AI can greatly enhance sales efficiency. Specifically, AI can increase sales efficiency by six times, productize objection handling during calls, and feedback into CRM, channel generation, and product roadmap. This not only improves the success rate of individual calls but also brings more insights and improvement suggestions to the entire organization.

Combining with CRM and Sales Intelligence Tools:

The integration of AI with CRM and sales intelligence tools allows sales teams to make more informed decisions regarding performance visibility, guidance, and recruitment. AI can analyze sales representatives' performance data to identify strengths and weaknesses, assisting management in targeted training and guidance. Additionally, AI can optimize the recruitment process, ensuring new sales representatives can quickly adapt and perform to their maximum potential. By saving significant time from offline meetings and commuting, sales teams can focus more on uncovering opportunities, customizing emails, and collaborating with marketing and product departments to refine the Go-To-Market (GTM) strategy.

Comprehensive Impact on Sales Efficiency:

The shift from outbound and growth operations to sales cannot overstate the role of AI in enhancing sales efficiency. By fully integrating AI technology, companies can achieve automation, personalization, and intelligence in their sales processes. AI helps sales teams respond to customer needs faster, provide higher-quality customer service, and ultimately drive sales growth and business expansion.

Conclusion:

AI technology is changing the game in the sales industry. Through personalized email draft creation, identifying potential customer traits and patterns, AI-enhanced human performance audio solutions, and integration with CRM and sales intelligence tools, companies can significantly improve sales efficiency and conversion rates. As technology continues to develop, the application of AI in sales will become more widespread and in-depth, helping companies stay ahead in the fierce market competition.

TAGS:

AI in Sales Efficiency, AI for Business Growth, Personalized Email Drafts with AI, AI Customer Insights, AI-enhanced Sales Solutions, AI and CRM Integration, Generative AI for Sales, Improving Sales Conversion Rates with AI, AI Sales Intelligence Tools, Advanced AI Applications in Sales

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Wednesday, April 24, 2024

Application of Artificial Intelligence in the Financial Industry: Frontier Areas, Key Applications, and Implementation Steps

HaxiTAG as an expert in the field of artificial intelligence (AI), based on practical case studies and research into market demands and business scenarios within the financial industry, I possess a deep understanding of AI applications in finance:

1. Frontier Areas of AI in the Financial Industry

The traditional applications of AI in finance, such as credit scoring, personalized financial products, and risk management, have expanded in recent years towards broader and deeper areas. Here are some frontier application areas:

- Regulatory Technology (RegTech): AI aids financial institutions in automating regulatory reporting and compliance reviews, reducing regulatory costs, and improving compliance efficiency.

- Financial Data Analytics: AI analyzes vast financial data sets to extract key insights used for financial decision-making, risk management, and market forecasting.

- AI Investment Research: AI assists analysts in stock research, identifying potential investment opportunities, and assessing investment risks.

- Intelligent Risk Control: AI constructs intelligent risk control models to monitor transaction activities in real-time, identifying suspicious behavior and effectively preventing financial fraud and money laundering risks.

- Financial Open Platforms: AI empowers financial open platforms by providing financial technology services including data, algorithms, and models, fostering collaboration and innovation among financial institutions.

2. Key Applications of AI in the Financial Industry

In addition to the mentioned applications like credit scoring, personalized financial products, and risk management, AI in the financial industry encompasses several key applications:

- Intelligent Customer Service: AI-driven chatbots provide 24/7 customer support, answering common queries, handling routine tasks, and offering personalized service recommendations based on customer needs.

- AI-Powered Marketing: AI analyzes customer data to identify target customer groups and formulates precise marketing strategies, enhancing marketing efficiency and customer conversion rates.

- Automated Trading: AI analyzes market data to identify trading opportunities and executes automated trading strategies, enhancing trading efficiency and returns.

- Intelligent Risk Control: AI constructs intelligent risk control models to monitor transaction activities in real-time, identifying suspicious behavior and effectively preventing financial fraud and money laundering risks.

- AI Investment Research: AI assists analysts in stock research, identifying potential investment opportunities, and assessing investment risks.

3. Implementation Steps of AI in the Financial Industry

To successfully apply AI technology, financial institutions need to follow these steps:

- Establish a Clear AI Strategy: Define the goals, scope, and expected benefits of AI applications, and formulate corresponding implementation plans.

- Build Data Foundations: Collect and integrate high-quality data to provide the necessary foundation for AI model training and application.

- Select Suitable AI Technologies: Choose appropriate AI algorithms and models based on specific application scenarios.

- Develop an AI Talent Pool: Cultivate and recruit AI professionals to ensure the successful implementation and execution of AI projects.

- Establish Effective AI Governance: Develop AI ethics standards and risk management measures to ensure the compliant and secure application of AI technology.

Artificial intelligence is profoundly transforming the financial industry, presenting new development opportunities and challenges for financial institutions. Financial institutions should embrace AI technology actively, continuously innovate applications, and enhance competitiveness, risk management, and customer experience.

Additional Recommendations

- Financial institutions should strengthen collaboration with technology companies and universities to advance research and facilitate the transformation of AI applications in the financial industry.

- Regulatory bodies should establish robust AI regulatory systems to promote the standardized development and application of AI technology.

- Financial institutions should prioritize AI ethics and social responsibility to ensure fair, just, and trustworthy AI applications.

HaxiTAG and his partners, through the practice of a large number of case-based efficiency gains, have gained some insights that we believe that with the continuous development and refinement of AI technology, it will play a more significant role in the financial industry, driving transformation and upgrading of financial services to support the real economy.

Key Point Q&A:

1. How has AI expanded beyond traditional applications like credit scoring and personalized financial products in the financial industry?

AI has extended into frontier areas such as RegTech, financial data analytics, AI investment research, intelligent risk control, and financial open platforms.

2. What are some key applications of AI-driven technology in financial institutions apart from risk management and customer service?

Key applications include intelligent marketing, automated trading, and AI-powered investment research.

3. What are the essential steps that financial institutions should follow to successfully implement AI technology?

Financial institutions should establish a clear AI strategy, build robust data foundations, select suitable AI technologies, develop an AI talent pool, and establish effective AI governance measures.