In this article, we’ll dive deep into the best sales analytics tools for Amazon sellers. Whether you’re a seasoned seller or just starting, this guide will help you navigate the options, understand their features, and choose the right tool for your business. Let’s get started!

Why Sales Analytics Tools Are a Game-Changer for Amazon Sellers

Before exploring the tools themselves, it’s essential to understand why sales analytics tools are critical for Amazon sellers.

• Profitability Tracking: Amazon’s marketplace is highly competitive, and profit margins can be razor-thin. Sales analytics tools help you track your profit and loss (P&L) in real time, ensuring you’re not losing money on every sale.
• Inventory Management: Overstocking or understocking can be costly. These tools provide insights into your inventory levels, helping you avoid stockouts or excess inventory.
• Customer Insights: Understanding customer behavior—such as returns, refunds, and reviews—can help you improve your product offerings and customer satisfaction.
• Competitive Edge: By analyzing market trends and competitor performance, you can identify opportunities to outshine your competition.

With these benefits in mind, let’s explore the top sales analytics tools available for Amazon sellers.

1. Helium 10

Helium 10 is one of the most popular tools among Amazon sellers, and for good reason. It’s a comprehensive suite of tools designed to help sellers with everything from product research to keyword optimization and sales analytics.

Key Features

• Profit and Loss Tracking: Helium 10’s analytics dashboard provides a clear view of your revenue, expenses, and net profit.
• Inventory Management: The tool alerts you when your inventory runs low, helping you avoid stockouts.
• Refund and Return Insights: Track refunds and returns to identify problematic products or customer pain points.
• Custom Alerts: Set up alerts for critical business metrics, such as sudden sales drops or changes in competitor pricing.

Pros:

• Comprehensive Tool Suite: This suite offers over 30 tools, including product research, keyword research, listing optimization, and inventory management.
• User-Friendly Design: Intuitive interface with easy-to-follow training resources.
  Free Version Available: Allows users to test the platform before committing.

Cons:

• Overwhelming for Beginners: The extensive feature set can be daunting for new users.
• Customer Support: Some users report slow response times.

Why It Stands Out

Helium 10 is more than just a sales analytics tool—it’s a complete Amazon business management platform. Its robust features make it a favorite among sellers who want an all-in-one solution.

2. Putler

Putler is a comprehensive multi-channel analytics tool that consolidates data from multiple Amazon stores and 17+ other platforms into a single intelligent dashboard. Built for sellers managing complex operations, Putler goes beyond basic reporting to deliver advanced customer intelligence.

Key Features

• Advanced RFM Customer Segmentation: Automatically segments customers into 11 behavioral groups (Champions, Loyal, At-Risk, etc.) with one-click access to customer lists in each category.
• Sales Heatmap: Shows exactly when your customers are most active on your store, helping you time promotions and campaigns for maximum impact.
• Unlimited Filters & Segmentation: Create custom segments using multiple parameters and infinite filtering options to discover hidden opportunities.
• Holiday Season Insights: Detailed analysis of performance during peak periods like Black Friday and Christmas to optimize seasonal strategies.
• 200+ KPIs: Comprehensive metrics including LTV, Churn, MRR, ARR, ARPU across 8 dedicated dashboards.

Pros:

• Multi-Store Consolidation: Handles multiple stores, currencies, and data sources with automatic deduplication and cleaning.
• Advanced Customer Intelligence: Provides deep customer insights, including purchase behavior, lifetime value, and churn, etc.
• Operational Efficiency: Streamlines tasks like refunds (from 5 minutes to 5 seconds) and subscription management.
• Team Collaboration: Share insights with team members with proper access controls.

Cons:

• Limited Inventory Management: Focuses more on analytics and customer insights rather than inventory tracking features.

Why It Stands Out

Putler provides advanced analytics that not only analyzes Amazon but also payment gateways like PayPal and Stripe. You can integrate everything in one place – no need for different tools. Despite its powerful features, it remains user-friendly with intuitive dashboards that make complex data easy to understand. It’s the perfect all-in-one solution for sellers who want comprehensive business intelligence without the complexity.

3. Jungle Scout

Jungle Scout is another heavyweight in the Amazon seller community. Known for its product research capabilities, Jungle Scout also offers powerful sales analytics features.

Key Features

• Sales Dashboard: Monitor your sales performance, including revenue, units sold, and profit margins.
• Refund and Return Tracking: Identify trends in customer returns and refunds to improve product quality.
• Inventory Alerts: Receive notifications when your inventory levels are low or when it’s time to reorder.
• Tutorials and Support: Jungle Scout provides extensive tutorials and customer support to help you make the most of the tool.

Pros:

• Accurate Product Research: Known for precise data in product research and sales estimates.
• User-Friendly Interface: Simplifies the process for beginners.
• Supplier Database: Helps in finding reliable suppliers.

Cons:

• Limited Features Compared to Competitors: Offers fewer tools than Helium 10.
• Higher Pricing: Some users find it relatively expensive.

Why It Stands Out

Jungle Scout’s user-friendly interface and focus on education make it an excellent choice for beginners. Its sales analytics features are straightforward yet powerful, making it easy to track your business performance.

4. Sellerboard

If your primary focus is profitability, Sellerboard is your tool. Designed specifically for profit analytics, Sellerboard helps you understand your actual profit margins after accounting for all expenses.

Key Features

• Profit and Loss Reporting: Sellerboard provides detailed P&L reports, including Amazon fees, advertising, and shipping expenses.
• Inventory Insights: Track your inventory turnover rate and identify slow-moving products.
• Customizable Dashboards: Tailor your dashboard to focus on the metrics that matter most to your business.
• Free Trial: Sellerboard offers an extended two-month free trial, making it easy to test the tool before committing.

Pros:

• Detailed Profit Analytics: Provides real-time profit calculations, including all expenses.
  Affordable Pricing: Starts at $19 per month.
• Inventory Management: Helps track stock levels and manage inventory efficiently.

Cons:

• Limited Features: Focuses primarily on profit analytics, lacking broader tools like product research.
• User Interface: Some users find the interface less intuitive.

Why It Stands Out

Sellerboard’s laser focus on profitability sets it apart from other tools. It’s ideal for sellers who want to dig deep into their financials and optimize their profit margins.

5. Sellerise

Sellerise is a relatively new player in the Amazon sales analytics space, but it’s already making waves. Created by eight-figure Amazon seller Dima Kubrak, Sellerise is designed to help sellers streamline their operations and maximize profits.

Key Features

• Sales Analytics: Track your sales performance, including revenue, profit, and expenses.
• Advertising Insights: Analyze your Amazon PPC campaigns to optimize your ad spend.
• Inventory Management: Monitor your inventory levels and receive alerts when it’s time to reorder.
• Free Trial and Discounts: Sellerise offers users a free trial and a recurring 10% discount.

Pros:

• Comprehensive Operations Management: Offers tools for inventory management, keyword tracking, and review monitoring.
• User-Friendly Mobile App: Allows for business management on the go.
• Affordable Pricing: Starter plans begin at $17.99 per month.

Cons:

• No Product Research Tools: Lacks features for finding new products to sell.
• Limited Integrations: Fewer integrations compared to competitors.

Why It Stands Out

Sellerise’s unique selling point is its creator’s firsthand experience as a successful Amazon seller. This tool is built with real-world insights, making it highly practical and effective.

6. ZonGuru

ZonGuru is another versatile tool that offers a range of features for Amazon sellers, including sales analytics.

Key Features

• Sales Tracking: Monitor your sales performance and identify trends over time.
• Keyword Research: ZonGuru’s keyword research tools help optimize your product listings for better visibility.
• Inventory Alerts: Stay on top of your inventory levels with timely alerts.
• Free Trial and Discounts: ZonGuru offers a free trial and special discounts for new users.

Pros:

• All-in-One Platform: Provides tools for product research, keyword tracking, and listing optimization.
• User-Friendly Interface: Designed to be intuitive and easy to navigate.
• Affordable Pricing: Plans start at $49 per month.

Cons:

• Overwhelming for Beginners: Many tools can be daunting for new users.
• Short Free Trial: Offers only a 7-day free trial, which may not be sufficient for thorough testing.

Why It Stands Out

ZonGuru’s niche-focused approach makes it an excellent choice for sellers who want to combine sales analytics with other essential features like keyword research and listing optimization.

How to Choose the Right Tool for Your Business

Choosing the right sales analytics tool can feel overwhelming with so many options available. Here are some tips to help you make the best decision:

• Identify Your Needs: Are you primarily focused on profitability, inventory management, or customer insights? Different tools excel in other areas.
• Consider Your Budget: While most tools offer free trials, their pricing plans vary. Make sure to choose a tool that fits your budget.
• Test Multiple Tools: Use free trials to test different tools and see which works best for your business.
• Check for Integrations: If you’re already using other tools (e.g., for advertising or product research), ensure your chosen analytics tool integrates seamlessly.

Final Thoughts

In the competitive world of Amazon selling, knowledge is power. Sales analytics tools provide the insights you need to make informed decisions, optimize your operations, and stay ahead of the competition. Whether you choose Helium 10, Jungle Scout, Sellerboard, Sellerise, or ZonGuru, each tool offers unique features to help you succeed.

Remember, the best tool for you depends on your specific needs and goals. Take the time to explore your options, test different tools, and choose the one that aligns with your business strategy.

The post Top 5 best sales analytics tools for Amazon sellers appeared first on RoboticsBiz.

Exploring Sales Data Analysis

Sales analytics involves examining data related to product performance in the market. This includes metrics such as the number of sales made, conversion rates, and customer engagement in purchasing goods or services, whether online or offline.

Examining these factors allows companies to make informed decisions about their marketing strategies and inventory management and effectively meet customer demands. Amazon product sales analytics provide insights into how various aspects influence sales outcomes.

Boosting Product Ranking

One of the main advantages of using analytics tools is improving product rankings. Higher rankings boost visibility on the platform, leading to increased exposure to potential buyers. Sales analytics tools help identify the factors contributing to better rankings, such as search engine optimization, customer reviews, and managing price. Concentrating on these areas can help businesses enhance their product placement, making it easier for customers to discover their offerings.

Exploring the Current Market Trends

Staying current with market trends is essential for maintaining a competitive edge in business operations. Data analysis tools offer insights into consumer behavior and preferences. Companies can align themselves with consumers’ evolving needs by monitoring these trends and adjusting products accordingly.

This proactive strategy achieved through Amazon product sales analytics helps businesses remain relevant in a changing market landscape. Understanding customers’ desires allows brands to refine their approaches and position themselves for success.

Improving the Customer Experience

Improving the customer experience is a key benefit of using analytics tools in business operations. Examining customer feedback and purchasing patterns can help businesses identify areas for enhancement. This might involve updating product information or improving customer service to address complaints. In doing so, companies can create a shopping experience that builds loyalty and encourages repeat purchases.

Fine-tuning Advertising Approaches

Advertising is essential for businesses to thrive and expand their market reach by attracting new customers. Tools like analytics provide valuable insights into effective advertising methods that produce positive results. Analyzing click-through and conversion rates and overall ad performance, companies can help companies refine their advertising strategies and use their marketing budgets more effectively to maximize returns. Understanding which ad campaigns resonate well with customers enables brands to allocate resources wisely and effectively boost sales figures.

Optimizing Inventory Control

Effective inventory management meets customer needs by utilizing analytical tools to monitor stock levels and predict demand while efficiently overseeing supply chains. Studying purchasing trends can help companies prevent shortages and excess inventory, increase customer satisfaction, and reduce storage costs. Inventory management ensures that products are available to customers when needed, ultimately enhancing their overall shopping experience.

Establishing Customer Loyalty

Data analysis tools are essential for building customer loyalty to a brand. By understanding consumer preferences and behaviors, companies can develop marketing strategies that resonate with individuals. Personalized promotions and targeted messaging create a sense of connection with customers. Establishing strong relationships can lead to increased loyalty and ongoing patronage. Leveraging analytics tools allows brands to identify the key factors that drive customer loyalty, allowing them to focus on strategies that strengthen these connections.

Summary

Using Amazon’s tools to analyze product sales and boost brand exposure is essential in an online marketplace. These resources provide data on sales trends, market dynamics, and consumer habits. Understanding these aspects helps companies make informed decisions to improve product visibility, refine marketing strategies, and enhance inventory management. Ultimately, leveraging analytics tools helps brands stay competitive, foster customer trust, and achieve growth.

The post How Amazon product sales analytics tools improve brand visibility appeared first on RoboticsBiz.

This not only helps in offering personalized services but also enhances security and overall satisfaction. Below, we’ll explore five key ways big data analytics is enhancing the fintech consumer experience.

Improve Risk Evaluation

There are many risks in the fintech sector that consumers and operators need to consider. Throughout the years, Big Data analytics have significantly improved risk evaluation in fintech. This includes assessing the credit scores of customers who are applying for a loan.

Fintech firms need to oversee their risk exposure. They can do this by assessing their customers’ credit scores, especially if the consumer is acquiring services from the fintech industry.

The provider can determine an individual’s danger to the organization immediately by examining their credit score. Moreover, the company can also advise the customer on how they can maintain or improve their credit scores.

For instance, they can explain to a client that it is difficult to get 700 credit score with collections. If someone is offering to assist and guarantee such an offer, then this would likely raise alarms.

Big data analytics assist companies in analyzing a wide range of factors, including clients’ spending habits, income patterns, and even social behavior. This broader view allows for more accurate and fair assessments of creditworthiness.

Big data analytics enable fintech companies to provide tailored services and fairer credit evaluations, which benefit both the provider and the client.

Improve Security

Security is a top concern in fintech, and big data analytics is crucial in enhancing it. With the growing volume of online transactions, fintech companies must protect consumer data from fraud and cyber threats.

Big data analytics helps by constantly monitoring many transactions in real time, identifying suspicious patterns or anomalies that could indicate fraudulent activity. Fintech companies can detect irregularities quickly and prevent unauthorized access or fraudulent transactions by analyzing user behavior, such as spending patterns and log-in habits.

In the continuously evolving digital world, it’s vital to have a continuous threat detection approach to secure every transaction within the organization. Big data analytics is a platform many fintech companies use to have continuous threat detection and provide their consumers peace of mind in their current and future transactions.

Enhancing Consumer Profiling Insight

Big data analytics has transformed how fintech companies understand and profile their customers. Instead of relying on basic financial information, fintech firms can now gather and analyze a wide range of data points, including spending habits, social media activity, and transaction histories.

Organizations can also easily understand their customers by segmenting their needs, wants, and expectations. As a result, fintech companies can create customized services for their clients according to the data they gather.

Fintech companies can enhance consumer profiling using big data analytics to provide a more relevant and satisfying experience to their consumers. Moreover, they can offer solutions that align with customers’ financial behaviors and goals. This not only improves customer satisfaction but also strengthens brand quality.

Customizing Services Through Chatbots

The importance of chatbots, especially in the fintech sector, is increasing. They are a substitute for talking to human agents in real-time. Chatbots allowed fintech companies to save over 7.3 billion dollars in operation costs last year.

One of the main objectives of chatbots is to provide human-like support to consumers, providing vital information, answering queries, and supporting them in dealing with challenges by giving personalized advice and receiving complaints.

Big data analytics is crucial in improving the fintech customer experience by providing customized service through chatbots. Fintech businesses are able to create chatbots that offer highly tailored and relevant interactions by analyzing large volumes of client data, including transaction histories, preferences, and behavior.

Additionally, chatbots powered by big data can adapt in real-time, learning from each interaction to improve future conversations. The use of data-driven chatbots not only improves customer satisfaction by offering faster and more accurate support but also enhances customer loyalty.

Forecasting Upcoming Market Trends

Fintech companies can use big data analytics to understand the ever-evolving industry. They gather and store large amounts of data from various sources, such as online transactions and social media.

Acquiring more extensive data from consumers allows the company to have a more holistic view of its target market. This way, it can better identify vital and useful patterns and trends.

Additionally, big data revolutionizes market analysis by providing a huge amount of valuable data. This helps companies adapt strategies, predict market fluctuations, and stay ahead of the competition.

Final Words

Big data analytics is revolutionizing the fintech consumer experience by enhancing risk evaluation, improving security, customizing services, and personalizing interactions.

Fintech companies may enhance client happiness and loyalty by providing more customised, efficient, and secure services by utilising extensive data sources. This allows them to remain ahead of industry trends and expectations.

The post 5 ways big data analytics enhance fintech consumer experience appeared first on RoboticsBiz.

One significant benefit of HR Analytics tools is their capacity to offer insights grounded in data when managing the workforce. By collecting and analyzing various HR metrics, such as employee turnover, performance, recruitment, and engagement, these tools empower HR professionals to make well-informed decisions. They aid in identifying trends, patterns, and potential workforce challenges, enabling HR teams to proactively address issues and optimize HR processes.

Another key advantage is the enhanced efficiency and productivity of HR operations. HR Analytics tools automate data collection and reporting, reducing the manual workload associated with HR tasks. This automation streamlines processes such as payroll, performance assessments, and compliance reporting, allowing HR teams to dedicate more time to strategic initiatives and employee-focused activities.

Furthermore, HR Analytics tools bolster talent acquisition and retention strategies. They assist organizations in pinpointing effective recruitment sources, predicting turnover risks, and evaluating the impact of training and development programs. With these insights, HR professionals can tailor their recruitment efforts, devise targeted retention strategies, and allocate resources more efficiently to attract and retain top talent.

In addition, HR Analytics tools encourage data-driven decision-making across the entire organization. By providing easily accessible and understandable HR data, these tools empower HR professionals to convey insights to senior management and other departments. This fosters a culture of data-driven decision-making, ultimately leading to improved overall organizational performance and alignment with business objectives.

This article will compile a list of leading HR Analytics tools available in the market.

1. Bullhorn ATS and CRM

Bullhorn is a comprehensive applicant tracking system (ATS) and customer relationship management (CRM) software designed to streamline HR professionals’ recruitment and staffing processes. Bullhorn offers an array of features that contribute to HR analytics. It excels in data management, gathering and centralizing extensive candidate and client information, including resumes, interview notes, and communication history. Its reporting and analytics tools are highly customizable, enabling HR teams to track various recruitment metrics, monitor performance, and gain valuable insights into the hiring process. Additionally, Bullhorn’s seamless integration capabilities allow it to work harmoniously with other HR and business software, facilitating data sharing and providing a holistic view of HR activities.

2. Oracle Human Capital Management (HCM) Cloud

Oracle HCM Cloud is a comprehensive suite of human resource management and talent management applications delivered as a cloud-based solution. It encompasses many features, including robust HR analytics capabilities, which play a critical role in helping organizations make data-driven HR decisions. Oracle HCM Cloud offers a rich set of features for HR analytics. It collects and consolidates HR data from various sources, providing a unified view of the workforce. The platform includes customizable reporting and analytics tools, allowing HR professionals to create reports and dashboards tailored to their needs. Users can analyze key HR metrics such as employee turnover, performance, and workforce diversity. Additionally, Oracle HCM Cloud leverages artificial intelligence (AI) and machine learning to provide predictive analytics, enabling organizations to forecast trends and make proactive HR decisions.

3. IBM Watson Talent

IBM Watson Talent is integral to IBM’s AI-powered solutions, designed to assist HR professionals in making data-driven decisions and optimizing their workforce management processes. IBM Watson Talent offers a suite of features tailored for HR analytics. It leverages artificial intelligence (AI) and data analytics to provide deep insights into an organization’s workforce. This includes predictive analytics to forecast future talent needs and identify potential retention risks. The platform collects and integrates data from various HR sources, creating a unified and comprehensive view of employee information. Additionally, it offers customizable reporting and dashboards, enabling HR teams to analyze key metrics such as employee performance, engagement, and turnover. Integration with other HR systems and external data sources is a notable feature, facilitating seamless data flow.

4. Adrenalin HCM

Adrenalin HCM is a comprehensive Human Capital Management (HCM) software solution incorporating HR analytics capabilities to help organizations effectively manage their workforce and make data-driven HR decisions. Adrenalin HCM offers a range of features that contribute to HR analytics. It includes data collection and centralization, consolidating employee information, performance metrics, and other HR data into a single database. The platform provides customizable reporting and analytics tools, allowing HR professionals to create tailored reports and dashboards. These tools enable analysis of key HR metrics such as employee engagement, talent development, and workforce demographics. Adrenalin HCM also supports integration with other HR systems, ensuring seamless data flow and a holistic view of HR activities.

5. PeopleStreme HCM

PeopleStreme HCM is a comprehensive Human Capital Management (HCM) software solution incorporating HR analytics to empower organizations to strategically manage their workforce. PeopleStreme HCM offers a range of features that are instrumental in HR analytics. It excels in data management, centralizing employee information, performance metrics, and other HR data into a unified database. The platform provides robust reporting and analytics tools, enabling HR professionals to create tailored reports, dashboards, and visualizations. These tools support the analysis of key HR metrics, such as employee engagement, talent development, succession planning, and workforce demographics. PeopleStreme HCM supports integration with other HR systems, ensuring seamless data flow and a comprehensive view of HR activities.

6. Cegid HR Analytics

Cegid HR Analytics is a specialized solution that provides organizations with deep insights into their workforce data, enabling data-driven decision-making and enhancing HR processes. Cegid HR Analytics offers a wide range of features tailored for HR analytics. It excels in data management, collecting and centralizing HR data from various sources, including employee records, performance metrics, and payroll data. The platform provides robust reporting and analytics tools, enabling HR professionals to create customized reports, dashboards, and visualizations. These tools support the analysis of key HR metrics such as employee turnover, workforce demographics, and talent acquisition effectiveness. Cegid HR Analytics also supports integration with other HR systems and external data sources, facilitating seamless data flow and providing a holistic view of HR activities.

7. Crunchr

Crunchr is a specialized HR analytics platform designed to help organizations effectively manage their workforce data, gain insights into HR metrics, and make data-driven decisions. Crunchr offers a comprehensive set of features tailored for HR analytics. It excels in data consolidation, gathering HR data from various sources, including employee records, performance metrics, and engagement surveys. The platform provides robust reporting and analytics tools, allowing HR professionals to create customized reports, dashboards, and visualizations. These tools support the analysis of key HR metrics such as employee turnover, workforce diversity, and talent development. Crunchr also supports data integration with other HR systems and external sources, ensuring seamless data flow and a holistic view of HR activities.

8. Infor Talent Science

Infor Talent Science is a specialized HR analytics solution designed to assist organizations in making informed decisions about talent acquisition, development, and management. It leverages data analytics and predictive modeling to provide insights into workforce behavior and performance. Infor Talent Science offers a range of features geared towards HR analytics. It excels in predictive analytics, using data from assessments, employee records, and other sources to forecast employee performance, retention risks, and job fit. The platform provides customizable reporting and analytics tools, enabling HR professionals to generate tailored reports and visualizations. These tools support the analysis of key HR metrics such as employee turnover, talent gaps, and succession planning. Infor Talent Science supports integration with other HR systems, ensuring data flow and a holistic view of HR activities.

9. MicroStrategy Analytics

MicroStrategy Analytics is a versatile business intelligence and data analytics platform that organizations can leverage for various needs, including HR analytics. It provides robust tools to analyze and visualize HR data to support data-driven decision-making within HR departments. MicroStrategy Analytics offers a range of features suited for HR analytics. It excels in data integration, allowing organizations to consolidate HR data from various sources such as HRIS systems, performance records, and employee surveys. The platform provides advanced reporting and analytics tools, enabling HR professionals to create customized reports, dashboards, and interactive data visualizations. These tools facilitate the analysis of essential HR metrics like employee turnover, workforce demographics, and talent development. MicroStrategy supports integration with other HR and business systems, ensuring seamless data flow and comprehensive insights.

10. Sage HRMS and Sage People

Sage HRMS and Sage People are comprehensive Human Resource Management Systems (HRMS) designed to assist organizations in managing their workforce efficiently. They include features and tools that support HR analytics, enabling organizations to gain insights from their HR data and make informed decisions. Sage HRMS offers HR data management, payroll processing, benefits administration, and time and attendance tracking features. On the other hand, Sage People is a cloud-based HR and People management system with features including HR analytics and reporting, recruitment and onboarding, talent management, and workforce planning. Both platforms provide customizable reporting and analytics tools, allowing HR professionals to create tailored reports, dashboards, and visualizations. These tools facilitate the analysis of key HR metrics such as employee turnover, performance, and workforce demographics. Sage HRMS and Sage People support integration with other HR and business systems, ensuring seamless data flow and comprehensive insights.

11. SAP SuccessFactors and SAP Analytics Cloud

SAP SuccessFactors is a comprehensive Human Capital Management (HCM) suite offered by SAP, and it includes a range of HR modules designed to help organizations manage their workforce effectively. SAP Analytics Cloud is an integrated analytics platform by SAP that complements SuccessFactors by providing advanced analytics and visualization capabilities. SAP SuccessFactors offers various HR features, including talent management, recruiting, onboarding, learning management, performance management, and workforce planning. It centralizes HR data and provides tools for HR analytics, allowing organizations to track and analyze key metrics such as employee engagement, retention, and performance. When integrated with SuccessFactors, SAP Analytics Cloud enhances analytics capabilities by providing interactive dashboards, predictive analytics, and data visualization tools. HR professionals can create dynamic reports and explore data to uncover insights and trends.

12. Sisense

Sisense is a versatile business intelligence platform that includes features for HR analytics, enabling organizations to gather, analyze, and visualize HR data for data-driven decision-making. Sisense offers a range of features tailored for HR analytics. It excels in data integration, allowing organizations to centralize HR data from various sources, including HRIS systems, employee records, and performance management platforms. The platform provides robust reporting and analytics tools, enabling HR professionals to create customized reports, dashboards, and visualizations. These tools facilitate the analysis of key HR metrics such as employee turnover, workforce demographics, and talent development. Sisense also supports integration with other HR and business systems, ensuring seamless data flow and comprehensive insights.

13. Tableau

Tableau is a widely used data visualization and business intelligence platform that organizations can leverage for HR analytics. It provides powerful tools for analyzing and visualizing HR data to gain valuable insights and support data-driven HR decision-making. Tableau offers a range of features suited for HR analytics. It excels in data integration, allowing organizations to consolidate HR data from various sources such as HRIS systems, performance records, and employee surveys. The platform provides robust reporting and analytics tools, enabling HR professionals to create customized reports, dashboards, and interactive data visualizations. These tools facilitate the analysis of key HR metrics such as employee turnover, workforce demographics, and talent development. Tableau also supports integration with other HR and business systems, ensuring seamless data flow and comprehensive insights.

14. UKG Pro

UKG Pro, formerly Ultimate Software, is a comprehensive Human Capital Management (HCM) platform with HR analytics capabilities. It is designed to help organizations manage their workforce effectively, streamline HR processes, and make data-driven decisions. UKG Pro offers a range of features geared towards HR analytics. It excels in data integration, allowing organizations to consolidate HR data from various sources, including HRIS systems, payroll records, and employee surveys. The platform provides robust reporting and analytics tools, enabling HR professionals to create customized reports, dashboards, and visualizations. These tools facilitate the analysis of key HR metrics such as employee turnover, workforce demographics, and talent development. UKG Pro supports integration with other HR and business systems, ensuring seamless data flow and a holistic view of HR activities.

15. Visier People

Visier People is a specialized HR analytics solution designed to help organizations gain insights into their workforce data and drive data-driven decision-making within the HR department. Visier People offers a range of features tailored for HR analytics. It excels in data integration, allowing organizations to centralize HR data from various sources, including HRIS systems, payroll records, performance management platforms, etc. The platform provides robust reporting and analytics tools, enabling HR professionals to create customized reports, dashboards, and visualizations. These tools facilitate the analysis of key HR metrics such as employee turnover, workforce demographics, and talent development. Visier People also supports integration with other HR and business systems, ensuring seamless data flow and providing comprehensive insights.

16. Workday HCM

Workday HCM is a comprehensive Human Capital Management platform designed to assist organizations in managing their workforce efficiently. It includes a range of HR modules and features, with HR analytics capabilities to help organizations make data-driven decisions. Workday HCM offers a wide array of features suitable for HR analytics. It excels in data integration, allowing organizations to centralize HR data from various sources such as HRIS systems, payroll records, and performance management platforms. The platform provides robust reporting and analytics tools, enabling HR professionals to create customized reports, dashboards, and visualizations. These tools facilitate the analysis of key HR metrics such as workforce demographics, talent development, and succession planning. Workday supports integration with other HR and business systems, ensuring seamless data flow and comprehensive insights.

17. Zoho People and Zoho Analytics

Zoho People is a human resources management system (HRMS) designed to streamline HR processes and manage the workforce efficiently. Zoho Analytics, on the other hand, is an advanced business intelligence and analytics platform. Combined, these tools provide a comprehensive solution for HR analytics, enabling organizations to gain insights from their HR data. Zoho People offers employee self-service, time tracking, leave management, and performance appraisal. It centralizes HR data and provides tools for HR analytics, allowing organizations to track and analyze key HR metrics such as employee turnover, performance, and attendance. Zoho Analytics complements Zoho People by offering powerful reporting and data visualization capabilities. Users can create customized reports, dashboards, and charts to gain insights into HR data. These tools facilitate HR analytics by providing a visual representation of workforce data.

The post 17 top HR analytics tools in 2024 appeared first on RoboticsBiz.

From unraveling the mysteries hidden in vast datasets to architecting the infrastructure that houses the digital goldmine, a degree in analytics empowers you to explore intellectually stimulating and financially rewarding roles. In this article, we’ll delve into the world of analytics and unveil the highest-paying jobs you can aspire to secure after earning your analytical credentials.

This post explores the highest-paying jobs you can get after pursuing a degree in Analytics. The high salaries for these jobs are due to the high demand for these skills and the relatively low supply of qualified candidates. In addition to the high salaries, these jobs also offer good job prospects and benefits.

1. Data Scientist

Data scientists use their statistics, mathematics, and programming knowledge to extract data insights. They work with a variety of data sources, including structured data (such as customer records and financial transactions), unstructured data (such as social media posts and customer reviews), and streaming data (such as sensor data and clickstream data).

Median Annual Salary (U.S. Bureau of Labor Statistics, 2021): $108,370
Average Salary (Indeed): $129,000
Average Salary (Glassdoor): $111,000

2. Machine Learning Engineer

Machine learning engineers (MLEs) build, deploy, and maintain machine learning (ML) models. They work with data scientists to understand the business problem that the ML model is trying to solve, and then they design and implement the ML model. Once the model is implemented, MLEs monitor its performance and adjust as needed.

Median Annual Salary (U.S. Bureau of Labor Statistics, 2021): $110,140
Average Salary (Indeed): $132,000
Average Salary (Glassdoor): $110,000

3. Business Intelligence Analyst

Business intelligence (BI) analysts collect, analyze, and interpret data to help businesses make better decisions. They use a variety of data sources, including sales data, customer data, and financial data, to identify trends and patterns. They then communicate their findings to business stakeholders clearly and concisely.

Median Annual Salary (U.S. Bureau of Labor Statistics, 2021): $93,010
Average Salary (Indeed): $89,000
Average Salary (Glassdoor): $92,000

4. Data Architect

Data architects design and build data management systems that organizations use to store, manage, and access their data. They work with various data technologies, including databases, warehouses, and lakes. Data architects also develop and implement governance policies and procedures to ensure data is secure and compliant with regulations.

Median Annual Salary (U.S. Bureau of Labor Statistics, 2021): $109,260
Average Salary (Indeed): $115,000
Average Salary (Glassdoor): $109,000

5. Data Engineer

Data engineers build and maintain the infrastructure that powers data pipelines and warehouses. They use various tools and technologies to collect, clean, transform, and store data. Data engineers also work with data scientists and other professionals to ensure data is accessible and usable for analysis.

Median Annual Salary (U.S. Bureau of Labor Statistics, 2021): $106,330
Average Salary (Indeed): $111,000
Average Salary (Glassdoor): $106,000

6. Marketing Analyst

Marketing analysts use data to understand consumer behavior and develop and evaluate marketing campaigns. They typically work for advertising agencies, marketing research firms, or in-house marketing departments at companies. According to the U.S. Bureau of Labor Statistics, the median annual salary for marketing analysts was $70,530 in 2021.

Median Annual Salary (U.S. Bureau of Labor Statistics, 2021): $70,530
Average Salary (Indeed): $89,000
Average Salary (Glassdoor): $92,000

7. Financial Analyst

Financial analysts use data to evaluate financial performance and make investment recommendations. They typically work for investment banks, commercial banks, or other financial institutions. According to the U.S. Bureau of Labor Statistics, the median annual salary for financial analysts was $85,660 in 2021.

Median Annual Salary (U.S. Bureau of Labor Statistics, 2021): $85,660
Average Salary (Indeed): $95,000
Average Salary (Glassdoor): $100,000

8. Operations Research Analyst

Operations research analysts use data to develop and implement mathematical models to solve complex business problems. They typically work for consulting firms, research and development departments, or government agencies. According to the U.S. Bureau of Labor Statistics, the median annual salary for operations research analysts was $84,280 in 2021.

Median Annual Salary (U.S. Bureau of Labor Statistics, 2021): $84,280
Average Salary (Indeed): $93,000
Average Salary (Glassdoor): $99,000

9. Quantitative Analyst

Quantitative analysts use data to develop and implement financial models to assess risk and make investment decisions. They typically work for hedge funds, investment banks, or commercial banks. According to the U.S. Bureau of Labor Statistics, the median annual salary for quantitative analysts was $114,700 in 2021.

Median Annual Salary (U.S. Bureau of Labor Statistics, 2021): $114,700
Average Salary (Indeed): $132,000
Average Salary (Glassdoor): $110,000

10. Risk Analyst

Risk analysts assess and manage risk for companies and organizations. They typically work for insurance companies, financial institutions, or consulting firms. According to the U.S. Bureau of Labor Statistics, the median annual salary for risk analysts was $94,600 in 2021.

Median Annual Salary (U.S. Bureau of Labor Statistics, 2021): $94,600
Average Salary (Indeed): $105,000
Average Salary (Glassdoor): $104,000

11. Market Research Analyst

Analysts collect and analyze data to understand consumer behavior and market trends. They typically work for marketing research firms, consulting firms, or in-house market research departments at companies. According to the U.S. Bureau of Labor Statistics, the median annual salary for market research analysts was $69,730 in 2021.

Median Annual Salary (U.S. Bureau of Labor Statistics, 2021): $69,730
Average Salary (Indeed): $86,000
Average Salary (Glassdoor): $102,000

12. Management Consultant

Management consultants help companies improve their performance and efficiency. They typically work for consulting firms with various clients in different industries. According to the U.S. Bureau of Labor Statistics, the median annual salary for management consultants was $94,600 in 2021.

Median Annual Salary (U.S. Bureau of Labor Statistics, 2021): $94,600
Average Salary (Indeed): $100,000
Average Salary (Glassdoor): $101,000

13. Data Warehouse Architect

Data warehouse architects design and build data warehouses to store and manage large amounts of data. They typically work for consulting firms, technology companies, or in-house IT departments at companies. According to the U.S. Bureau of Labor Statistics, the median annual salary for database administrators was $95,640 in 2021.

Median Annual Salary (U.S. Bureau of Labor Statistics, 2021): $95,640
Average Salary (Indeed): $105,000
Average Salary (Glassdoor): $104,000

The post Highest paying jobs you can get after pursuing a degree in Analytics appeared first on RoboticsBiz.

These tools are equipped with mechanisms to access websites, navigate their structure, and extract specific data elements, such as text, images, links, and more, from web pages. This data can be collected in structured formats like CSV or JSON. Users can customize and extend the functionality of these tools to suit their specific needs. This might involve modifying the scraping logic, adding new features, or integrating the tool with other systems.

These tools are also designed to automate the process of data extraction, making it more efficient and less error-prone than manual methods. They can handle large volumes of data from multiple web sources.

Open source tools are typically free to use, which can be a major advantage for individuals and organizations with budget constraints. There are no licensing fees or subscription costs associated with open source software. They provide transparency into their source code. Users can review the code, understand how the tool functions, and even modify it to suit their specific needs. This transparency fosters trust and helps ensure that the tool behaves as expected.

In this post, we will look at the top 15 open source web crawling and scraping tools, which offer cost savings, flexibility, transparency, and a supportive community, making them an attractive choice for those looking to collect and process data from the web.

1. Heritrix

Heritrix is a web crawling tool primarily designed for web archiving and collecting historical web data. It offers extensive customization options, allowing users to define crawling rules, apply URL filtering, and capture and store web content. One of its main strengths is its suitability for archival purposes. Pros include its excellent web archiving capabilities, high configurability, open-source nature, and active maintenance by the Internet Archive. However, its complexity in setup and configuration may be challenging for those looking for a more straightforward scraping tool.

2. Nutch

Nutch is an extensible web crawling and indexing framework. It boasts a flexible plugin system, supports distributed crawling, and is a popular choice for building custom search engines. Its key features include scalability and the ability to adapt to specific needs. Nutch is highly customizable and enjoys an active open-source community. However, it may require Java expertise for extensive customization, making it less user-friendly for those not well-versed in Java.

3. Scrapy

Scrapy is a Python-based web scraping framework known for its user-friendliness. It provides a high-level API, built-in support for common web scraping tasks, and comprehensive documentation. Scrapy supports both CSS and XPath selectors and offers concurrency support. Pros include its user-friendliness, Python-based scripting, concurrent request handling, and a large and active community. It’s ideal for a wide range of web scraping tasks, but newcomers to Python or web scraping may encounter a learning curve.

4. GNU Wget

GNU Wget is a command-line utility designed for downloading files from the web. It excels at recursive downloading, bandwidth throttling, and handling various protocols, making it ideal for bulk downloads. Its simplicity and effectiveness make it a valuable tool for downloading files and websites recursively. It is widely available on Unix-like systems. However, it has limited web scraping capabilities and is primarily designed for downloading, lacking advanced data extraction features.

5. ht://Dig

ht://Dig is a web indexing and searching system with a focus on customizable search algorithms and query options. It is suitable for website search functionality and offers extensive customization. While it is open source and caters to specific search requirements, it may require significant configuration. It is better suited for website search purposes rather than general web scraping.

6. HTTrack

HTTrack is a user-friendly website copier designed to download entire websites for offline browsing. It captures HTML, images, and other web resources, making it ideal for creating local copies of websites for offline use. Its simplicity and a user-friendly interface are its strengths. However, it may have limitations with dynamic websites and may not handle complex JavaScript-based interactivity effectively.

7. YaCy

YaCy stands out as a decentralized search engine that operates on a peer-to-peer network of users. It prioritizes privacy-focused searching and decentralized indexing. Its decentralized nature, privacy-friendly features, open-source status, and community-driven development make it suitable for creating decentralized search engines. However, its indexing speed and search results may not match those of centralized search engines like Google.

8. WebSphinX

WebSphinX is a web crawling and search engine system with an emphasis on its customization. It caters to specific web crawling and indexing needs, and it is open source. However, it may require extensive configuration and is better suited for web search functionality rather than general-purpose web scraping.

9. WebLech

WebLech is an open-source web crawler designed for indexing websites. It offers customization options and the ability to extract data from web pages. While it’s suitable for web crawling and scraping tasks, it may not have the same level of features and community support as some other tools like Scrapy or Nutch.

10. JSpider

JSpider is a Java-based web spidering framework. It provides the foundation for building web crawlers to collect data from websites. It’s a flexible tool for custom web scraping tasks, but it may require Java programming skills to use effectively.

11. Arachnid

Arachnid is a web scraping and crawling framework for Python. It allows users to build web scrapers to extract data from websites. Being Python-based, it’s relatively accessible for those familiar with the language. Its flexibility and scripting capabilities make it suitable for a range of web scraping projects.

12. Xapian

Xapian is an open-source search engine library that can be used to build search applications. While it’s not a dedicated web crawling tool, it plays a role in search functionality. It provides powerful indexing and search capabilities, making it useful for creating search engines.

13. OpenWebSpider

OpenWebSpider is an open-source web spider designed for crawling websites. It offers basic web crawling and scraping features. However, it may not have the same level of advanced features and community support as more popular tools like Scrapy or Nutch.

14. iCrawler

iCrawler is a web scraping framework for Python. It provides a foundation for building web scrapers and supports various data extraction tasks. Being Python-based, it’s accessible to Python developers and suitable for a wide range of scraping needs.

15. Sphider

Sphider is a simple web spider and search engine. It can crawl websites and provide basic search functionality. It’s an open-source tool but may be more limited in features compared to larger web crawling frameworks.

The post 15 open source web crawling and scraping tools appeared first on RoboticsBiz.

In this article, we have compiled a list of how using retail industry, and business analytics can improve your business speedily. You might also be interested in checking the uses of business intelligence in the retail industry article at EPAM Anywhere Business.

Benefits of Retail Analytics and BI  

One of the benefits of business analytics and business intelligence solutions in the retail sector is the historical views it gives of the business. In addition, it also gives insight into the present and futuristic analytics of the business. RetailTouchpoints reports that over 68% of retailers are collecting an enormous amount of data but still fail to maximize revenue since they are not using proper BI tools for analysis.

Let us take a closer look into some of the advantages of bi in the retail industry:

1. To Fulfill the Changing Needs of Customers

When you start a retail business, one of the things you need is the ability for you to meet the changing needs of your customers. Whether they ask for bulk purchases or socially responsible products, it is essential that all their needs are satisfied. When you employ effective retail bi analytics, you will gain insight into your customer demand map changes. When you have the insight, you can adjust your strategy. Some of the problems that bi applications in the retail industry solve include:

• Aiding supply to meet demand: You can see demand patterns peculiar to your customers when you have access to the right tools and data. Also, if you have a chain of stores, you can know what your customers demand across your stores. Moreover, you can learn when the demand is at a peak, during the holiday season, for example.

• Create personal experiences for in-store customers: With the correct Bi data, your staff can help your sales staff to understand the history of your customers’ purchases. This way, you can know their purchase history, select products, and run targeted promotions.

2. Knowing Emerging Trends

Bi retail management can help you know about emerging trends amongst your customers. It can also help you overcome obstacles your business will face in adjusting to trends. Even though many customers now shop in online stores, about 65 percent of people still prefer to shop at brick-and-mortar stores. So, it is important to use business intelligence analytics to identify how your customers react to new trends.

3. Better Decision Making

One of the benefits of business intelligence in the retail industry is the ability it gives its users to make better decisions. Using business intelligence, a brand can look through different data and aggregate them to see a better picture of their organizational operations. It would help you have a consolidated view of your organization and help you make better-informed decisions.

Also, applying business intelligence in retail will help you know where to deploy your resources. With Bi, you can reduce your customers’ wait time and provide the best prices. Furthermore, you can use the analysis to know how much time your customers spend on your online platforms and where in your store they spend more time. Using Bi tools, you can see what products are moving slowly and identify the products that aren’t getting enough attention from your customers.

4. Better Customer Satisfaction

One of the first rules of retail is “the customer is always right.” But, imagine if you could know the desires of your customers beforehand and work to exceed them. A good business intelligence analytics can help you achieve this quickly. We all know that customers’ needs constantly evolve as new trends emerge daily. It makes it difficult for retailers to keep up with the needs of their customers.

The insights from business intelligence allow you to tailor your customer’s shopping experience in a deeply satisfying way. When you improve your customers’ experiences using business intelligence, you can improve the rate at which you retain customers. It can also help you to build customer loyalty which means more revenue in the long run.

Final Thoughts

What better way to stay ahead in your business than having information that predicts the future? If you had a way to tell what your customers will do the next time they walk into your store, won’t you take it? With retail analytics and business intelligence, you can predict the shopping patterns of your customers and tailor their shopping experiences to improve retention and revenue.

The post Four benefits of Retail Analytics and Business Intelligence (BI) appeared first on RoboticsBiz.

There’s only one of you. And you can’t be running back and forth to every facility to check on each system. Unless, of course, you have a clone!

Have you considered intelligent video analytics?

The way we analyze security video and CCTV feeds is evolving every day. As head of your company’s security, there is technology available to make your life easier.

You’ll be able to work smarter, not harder. And it will free you up to focus on what you do best. That’s improving your company’s security and keeping everyone safe.

Sounds too good to be true, right? Well, today is your lucky day.

We’re going to go over how you can do exactly that in this article. By the time you’re done reading, you’ll be enthusiastic about launching a new, more efficient plan to protect your company.

Let’s begin!

Intelligent Video Analytics explained

Intelligent video analytics is the marriage of surveillance video and artificial intelligence. Since AI companies are beginning to develop deeper learning strategies for their software, many industries have grown leaps and bounds.

Security video is no different. These deeper learning patterns developing in AI frameworks allow security systems to do amazing things. It allows them to be intelligent.

Through intelligent video analytics, video systems can now recognize and analyze space and time events on your video feed.

For example, let’s say you spot a person making suspicious movements on camera. An intelligent video solution would be able to zoom on that person and get the footage necessary to stop a crime before it starts.

Normally, these intelligent video insights are caught on camera in real-time. But some companies are using intelligent video systems to mine historical data from previous recordings. It allows these companies to draw conclusions and get amazing insight into their business.

Gaining Insight

One of the ways companies are using this technology is in the retail space. Owners of brick and mortar retail stores can go back and watch footage of their store to monitor customer traffic.

By re-watching the old footage, companies can determine when their store is the busiest and what people buy most. They can even determine the average age of customers who shop with them!

Another more popular example is video surveillance. We all know how popular cameras are for security systems. People even have them in their doorbells now.

The problem with commercial security systems, however, is that they always depend on a human operator. This operator is the one responsible for watching the cameras and looking for anything suspicious. They may also be responsible for moving and zooming the cameras as well.

To make matters worse, most surveillance system operators are responsible for more than one camera. Adding more cameras may give you more coverage, but more cameras make it more difficult for the security system operator to monitor the system properly.

Seeing an entire building through dozens of cameras is great, but a human operator has to process that information. This human element can significantly compromise the effectiveness of most security systems.

Deep Learning Video

AI, or machine learning, is starting to revolutionize industries around the globe. But now, even AI is reaching new levels. Data engineering companies are starting to pioneer the concept of deep learning.

Deep learning is the idea that artificial intelligence applications are getting smarter. Due to advancements in electrical, computer, and cloud technology, AI companies can build products that can process deeper, more complex operations.

All of these technologies like vizard tools combine to create Deep Neural Networks (DNNs). These systems are making it possible for security teams to do amazing things with video.

A video system operating on a DNN can recognize specific objects and follow their movement. These video systems can also copy and track human behavior.

What’s more impressive is that the objects being tracked can be specific. Certain AI software can analyze video to single out things such as bicycles in real-time. Imagine what an application like that could do for security.

Where Did This Start?

So, how did we get here? Well, a tool known as Optical Character Recognition (OCR) has been in use for years. It is how your computer scanner can recognize words on a document. This is where the journey to deeper-learning video began.

Once OCR became functional for video feeds, you could see them used at toll booths and red light cameras to “read” a person’s license plate. A few weeks later, you’d get your ticket in the mail for not paying your toll or running a red light.

However, using video this way can be unreliable. The camera needs to be positioned at the exact angle for effective license plate recognition. And any OCR software used would have to recognize that this is, in fact, a license plate.

With deep learning, that’s all changed. Now video feeds can determine the exact screen portion where a license plate is likely to pop up. The system, through intelligence, can then run OCR non-stop on that part of the screen.

What Does the Future Hold?

We are just beginning to see the implications of deep learning on intelligent video analytics. And the results are already exciting.

But where could intelligent video go in the future? Where could it make the most impact?

1. Healthcare

Healthcare facilities spend a lot of money on security systems. These buildings are typically huge. It makes them hard to cover, and there are also many people inside who need to be safe.

All of that is changing now with intelligent video analytics. Cameras can monitor patients and keep track of when a nurse visits a particular patient. If it’s been too long between visits, the video system would then alert the nurse to go in and check on their patient.

The system would be able to analyze traffic in and out of the hospital as well. It is a game-changer because hospitals put a huge emphasis on who they let in and out of the building. An intelligent video system can monitor the traffic of both patients and visitors.

It may even be helpful to families with a relative in the hospital. If an intelligent video system is constantly watching, visiting hours may be a thing of the past. No more worrying about getting to the hospital at a certain time to spend time with the people you love!

2. Smart Cities

If you’ve ever lived in New York or Los Angeles, you know all about traffic. More and more people are moving to major metro areas by the day. More people means more cars on the road.

Intelligent video can come in handy for analyzing traffic patterns throughout major cities. By gaining insight from this video footage, intelligent video systems will decrease the number of traffic jams and accidents.

A video system may even be able to stop an accident before it happens. The intelligent video feed would have the ability to recognize a car traveling the wrong way on the road or in other hazardous situations.

A deep-learning video system could also help with traffic in other ways. A smart video feed can count cars in a particular area. It can also distinguish between a car, truck, taxi, police car, etc.

Knowing what types of vehicles are where and how many there are can help avoid major traffic jams and accidents. In addition, the intelligent video would also be able to read license plates of drivers who have broken the rules of the road.

It would also be helpful in speed cameras. The video system could pick out a specific car on the road based on its speed and catch speeders in the act.

3. Security

Using cameras and video for security systems is nothing new. But using intelligent video is revolutionary.

Smart video feeds can now read license plates and recognize people in cars. All this can be done in real-time. It allows security personnel to recognize a threat or someone suspicious and act accordingly right there on the spot.

Security personnel can also grant or deny access to secure areas based on the video footage.

These cameras can also help solve crimes. Police can go back through older videos to search for suspects in a particular investigation. It could also be done in real-time and increase the number of crimes police can solve.

The applications are endless.

Safe and Sound

Intelligent video analytics are changing the face of civilization as we know it. Security teams can work smarter and do a better job with the help of deep learning.

Using these video systems is like having another team of security officers on your staff. It enables you and your team to have your eyes everywhere at once and still not take away from your ability to do your job.

About the author:

Paul Sun is a seasoned technology executive and serial entrepreneur. He previously ran a division of Taiwan’s national Industrial Technology Research Institute and founded three successful venture capital-funded companies: Motia Inc, Avidia Systems ($100M M&A), and DSL.net ($1.5B Nasdaq IPO). Mr. Sun is currently the CEO of IronYun USA Inc., the Vaidio AI Vision Platform developer, winner of ISC West’s 2021 New Product Showcase for Mobile Applications and Video Analytics.

The post What is Intelligent Video Analysis? Explained! appeared first on RoboticsBiz.

But no matter how rich the quality of your data is or how much of it you have, if it is not coherently organized, then all you have is a useless pile of figures and texts.

This is where data analytics and analytics tools come into play. Data analytics is used to examine large data sets to draw meaningful insights and identify patterns or trends, solve problems, and make more strategic decisions.

Any company or organization needs to leverage data analytics if it is to accomplish its objectives. However, with many analytics tools available, how do you decide which one is best for you? Here are a few factors you should consider when choosing the right analytics tools for you and your organization.

Factor #1: Business Objectives

First off, ask yourself what it is you want to track. As many areas that you want to follow closely, you will need to narrow down your choices and prioritize. Remember the Pareto principle where 80% of your output comes from 20% of what you do? Ascertain which goals have the most impact on your strategic route. Which business opportunities have significant potential? Which blocks, when taken out, can address the most critical problems?

If you’re concerned about the bureaucratic processes that slow down your speed-to-market performance, you can choose an association management software that can streamline backend processes and help organization members prioritize and save time. If you’re in the construction business and one of your main objectives is to find single-source data that can give you real-time progress tracking across projects, then you can definitely make use of construction project scheduling software. Know your core goals and work your analytics tools around them.

Factor #2: Pricing

Even with the best-laid objectives, you need to determine your working budget. The price range is wide. You have the Dataiku and Qlik analytics tools that are free and may be used as collaboration tools but, specifically for Qlik, may not be flexible. For paid options at the lower end of the price range, you have Klipfolio, which, at $29 per month, has user-friendly features and gives proper customer support. You can also opt for the Domo software that allows automatic cloud uploading but can be complicated.

At the more expensive end, you have the RapidMiner analytics tool that features an auto-modification process and is highly intuitive at $2,500 per employee per year. Or you can enjoy the “explore” function and other user-friendly features of the Looker software at $3k per month. Of course, how attractive the prices of the analytics tools are will depend on how large your organization is and on the usefulness of the features that come with the prices. Just remember to check for hidden costs and understand how your chosen analytics tools will impact your overhead expenses as your business scales. Consider initial cash outlays, as well as obtain a cost-benefit analysis for a more strategic perspective.

Factor #3: Features

Your business goals and budget will help you distinguish the must-have features versus the “nice-to-haves.” But first, explore the vast array of fantastic features that the analytics tools of today have—and you’ll be as mesmerized as a kid at a candy store.

Are you looking to automatically capture data through your desktop email platform? Or are you going for analytics software that can segmentalize users, customize reports and KPI dashboards, and create data funnels? What are the features of the analytics tools that make them intuitive? Do you need excellent presentation tools, drag-and-drop functionality, and the capability to handle large data sets and integrate with other apps?

With all the revolutionary breakthroughs in analytics, such as customer analytics, Business Intelligence, and digital experience platforms, there is no need to be overwhelmed. Instead of a full-stack platform, choose analytics tools with features that are necessary to attain your business goals.

Factor #4: User interface and visualization

It is also important to remember the end-user when selecting analytics tools. The ease of understanding menu choices, the quick navigability of the user interface, and the flexibility to switch between different set-ups should all be considered. Are the members of your sales team your target users? Then ensure that you can give them analytics tools that will help them make informed decisions in real-time as they engage with their customers. Or are your end-users from your I.T. department who can adapt to analytics tools that are more advanced but may require some knowledge of Java, for instance? The easier and faster your end-users can master their analytics tools, the closer you will be to accomplishing the goals for which they were designed.

Visualization capabilities are also essential. Visualization tools allow better appreciation of data summaries and segmentations through graphical data representations. Make sure you use the right visualization software that is clean, easy to read, allows rapid development and deployment, and of course, is visually appealing.

The post 4 factors to consider when choosing an Analytics tool appeared first on RoboticsBiz.