12 months worth of sales data. The data contains hundreds of thousands of electronics store purchases broken down by order Number and it's date, product type and it's quantity, cost and purchase address

Column descriptors : Each dataset has:

Order Id: The id of the order.

Product: The type of the product bought.

Quantity Ordered: How many of the product was ordered.

Price Each: The price of a single item.

Order Date: When the product was ordered including the year,month, day, hours and minutes

Purchase Adrress: Where to deliver the order.

Questions: Question 1: What was the best month for sales? How much was earned that month?

Question 2 : Which city had the highest number of sales?

Question 3: What time should we display advertisements to maximize the likelihood of purchasses and Sales?

Question 4: What products are most often sold together?

Question 5: Which product was sold the most?

Vrinda Store: Interactive Ms Excel dashboardVrinda Store: Interactive Ms Excel dashboard Feb 2024 - Mar 2024Feb 2024 - Mar 2024 The owner of Vrinda store wants to create an annual sales report for 2022. So that their employees can understand their customers and grow more sales further. Questions asked by Owner of Vrinda store are as follows:- 1) Compare the sales and orders using single chart. 2) Which month got the highest sales and orders? 3) Who purchased more - women per men in 2022? 4) What are different order status in 2022?

And some other questions related to business. The owner of Vrinda store wanted a visual story of their data. Which can depict all the real time progress and sales insight of the store. This project is a Ms Excel dashboard which presents an interactive visual story to help the Owner and employees in increasing their sales. Task performed : Data cleaning, Data processing, Data analysis, Data visualization, Report. Tool used : Ms Excel The owner of Vrinda store wants to create an annual sales report for 2022. So that their employees can understand their customers and grow more sales further. Questions asked by Owner of Vrinda store are as follows:- 1) Compare the sales and orders using single chart. 2) Which month got the highest sales and orders? 3) Who purchased more - women per men in 2022? 4) What are different order status in 2022? And some other questions related to business. The owner of Vrinda store wanted a visual story of their data. Which can depict all the real time progress and sales insight of the store. This project is a Ms Excel dashboard which presents an interactive visual story to help the Owner and employees in increasing their sales. Task performed : Data cleaning, Data processing, Data analysis, Data visualization, Report. Tool used : Ms Excel Skills: Data Analysis · Data Analytics · ms excel · Pivot Tables

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Sales Intelligence Market Size 2025-2029

The sales intelligence market size is forecast to increase by USD 4.86 billion at a CAGR of 17.6% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing demand for custom-made solutions that cater to the unique needs of businesses. This trend is fueled by the rapid advancements in cloud technology, enabling real-time access to comprehensive and accurate sales data from anywhere. However, the high initial cost of implementing sales intelligence solutions can act as a barrier to entry for smaller organizations. Furthermore, regulatory hurdles impact adoption in certain industries, requiring strict compliance with data privacy regulations. With the advent of cloud computing and SaaS customer relationship management (CRM) systems, businesses are able to store and access customer information more efficiently. Moreover, the exponential growth of marketing intelligence, driven by big data and natural language processing (NLP) technologies, enables organizations to gain valuable insights from customer interactions.
Despite these challenges, the market's potential is vast, with opportunities for growth in sectors such as healthcare, finance, and retail. Companies seeking to capitalize on these opportunities must navigate these challenges effectively, investing in cost-effective solutions and ensuring regulatory compliance. By doing so, they can gain a competitive edge through improved lead generation, enhanced customer insights, and streamlined sales processes.

What will be the Size of the Sales Intelligence Market during the forecast period?

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In today's business landscape, sales intelligence has become a critical driver of revenue growth. The go-to-market strategy of companies relies heavily on predictive lead scoring and sales pipeline analysis to prioritize opportunities and optimize resource allocation. Sales operations teams leverage revenue intelligence to gain insights into sales performance and identify trends. Data quality is paramount in sales analytics dashboards, ensuring accurate sales negotiation and closing. Sales teams collaborate using sales enablement platforms, which integrate CRM systems and provide sales performance reporting. Sales process mapping and sales engagement tools enable effective communication and productivity. Conversational AI and sales automation software streamline sales outreach and prospecting efforts. Messaging and alerting features help sales teams engage with potential customers effectively, while chatbots facilitate efficient communication.
Sales forecasting models and intent data inform sales management decisions, while salesforce automation and data governance ensure data security and compliance. Sales effectiveness is enhanced through sales negotiation training and sales enablement training. The sales market is dynamic, with trends shifting towards advanced analytics and AI-driven solutions. Companies must adapt to stay competitive, focusing on data-driven strategies and continuous improvement.

How is this Sales Intelligence Industry segmented?

The sales intelligence industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  Cloud-based
  On-premises


Component

  Software
  Services


Application

  Data management
  Lead management


End-user

  IT and Telecom
  Healthcare and life sciences
  BFSI
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period. In today's business landscape, sales intelligence platforms have become indispensable tools for organizations seeking to optimize their sales processes and gain a competitive edge. These solutions offer various features, including deal tracking, win-loss analysis, data mining, sales efficiency, customer journey mapping, sales process optimization, pipeline management, sales cycle analysis, revenue optimization, market research, data integration, customer segmentation, sales engagement, sales coaching, sales playbook, sales process automation, business intelligence (BI), predictive analytics, target account identification, lead generation, account-based marketing (ABM), sales strategy, sales velocity, real-time data, artificial intelligence (AI), sales insights, sales enablement content, sales enablement, sales funnel optimization, sales performance metrics, competitive intelligence, sales methodology, customer churn, and machine learning (ML) for sales forecasting and buyer person

1) Data Introduction • The Sample Sales Data is a retail sales dataset of 2,823 orders and 25 columns that includes a variety of sales-related data, including order numbers, product information, quantity, unit price, sales, order date, order status, customer and delivery information.

2) Data Utilization (1) Sample Sales Data has characteristics that: • This dataset consists of numerical (sales, quantity, unit price, etc.), categorical (product, country, city, customer name, transaction size, etc.), and date (order date) variables, with missing values in some columns (STATE, ADDRESSLINE2, POSTALCODE, etc.). (2) Sample Sales Data can be used to: • Analysis of sales trends and performance by product: Key variables such as order date, product line, and country can be used to visualize and analyze monthly and yearly sales trends, the proportion of sales by product line, and top sales by country and region. • Segmentation and marketing strategies: Segmentation of customer groups based on customer information, transaction size, and regional data, and use them to design targeted marketing and customized promotion strategies.

This fictional sales dataset was created using a R code for the purpose of visualizing trends in customer demographics, product performance, and sales over time. A link to my Github repository containing all the codes used in generating the data frame and all the preceding processes can be found here

Analysis of ‘Sample Sales Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kyanyoga/sample-sales-data on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Sample Sales Data, Order Info, Sales, Customer, Shipping, etc., Used for Segmentation, Customer Analytics, Clustering and More. Inspired for retail analytics. This was originally used for Pentaho DI Kettle, But I found the set could be useful for Sales Simulation training.

Originally Written by María Carina Roldán, Pentaho Community Member, BI consultant (Assert Solutions), Argentina. This work is licensed under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License. Modified by Gus Segura June 2014.

--- Original source retains full ownership of the source dataset ---

Context

You are provided with historical sales data from 2009 to 2012. This data contain 3 product category which are office supplies, technology, and furniture. Each category has several sub-categories. The company also runs promotional in the form of a discount.

There is two CSV file provided in the dataset. The raw_data.csv is the unformatted file that has 5499 rows and 1 column. While clean_data.csv is a formatted file that has 5499 rows and 10 columns.

Content

Attribute Information: - order_id : unique order number - order_status : status of the order, whether is finished or returned - customer : customer name - order_date : date of the order - order_quantity : the quantity on a particular order - sales : sales generated on a particular order, the value is in IDR(Indonesia Rupiah) currency - discount : a discount percentage - discount_value : a sales multiply by discount, the value is in IDR(Indonesia Rupiah) currency - product_category : a category of the product - product_sub_category : a subcategory from product category

Acknowledgements

DQLab is an Online Data Science Learning Center to produce data practitioners who can make an impact. This dataset is part of a project in order to build analytical skills and apply knowledge to industry problems.

Source

Project Data Analysis for Retail: Sales Performance Report: https://academy.dqlab.id/main/package/project/182?pf=0

The global Sales Data Fusion market is experiencing robust growth, driven by the increasing need for businesses to leverage disparate data sources for improved sales performance and strategic decision-making. The market's expansion is fueled by the rising adoption of cloud-based solutions, advancements in artificial intelligence (AI) and machine learning (ML) for data integration and analysis, and the growing demand for real-time sales insights. Key players like Thomson Reuters, AGT International, and LexisNexis are leading the charge, offering comprehensive platforms that consolidate data from CRM systems, marketing automation tools, and other relevant sources. This consolidation provides a holistic view of customer interactions, sales performance, and market trends, enabling businesses to optimize sales strategies, improve forecasting accuracy, and ultimately enhance revenue generation. The market is segmented by deployment (cloud, on-premise), by industry (BFSI, retail, healthcare, manufacturing), and by component (software, services). While data security and privacy concerns represent a potential restraint, the overall market outlook remains positive, indicating continued growth driven by technological advancements and the ever-increasing value placed on data-driven decision-making within organizations. The forecast period of 2025-2033 is expected to witness significant expansion, building upon a strong historical period (2019-2024). Assuming a conservative CAGR of 15% (a reasonable estimate given the growth drivers mentioned), we can expect substantial market expansion. This growth will be particularly evident in regions with high technological adoption rates and robust digital infrastructures. The competitive landscape is characterized by both established players and emerging technology companies, creating a dynamic and innovative ecosystem. Future growth will likely be shaped by advancements in big data analytics, improved data integration capabilities, and the increasing availability of sophisticated sales intelligence tools. The market will continue to attract investments as businesses recognize the critical role of effective sales data fusion in achieving a competitive advantage.

Envestnet®| Yodlee®'s Online Purchase Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

The Global CRM Analytics Market Size Was Worth USD 9.85 Billion in 2023 and Is Expected To Reach USD 27.72 Billion by 2032, CAGR of 12.18%.

Market Analysis for Sales Tools The global sales tools market is estimated to reach a value of $574 million by 2033, growing at a CAGR of 4.8% from 2025 to 2033. The market is primarily driven by the increasing adoption of cloud-based sales tools and the growing need for efficient and effective sales management solutions. Small and medium-sized enterprises (SMEs) and large enterprises are the key market segments, while cloud-based and on-premises are the dominant types of sales tools. Major players in the market include Salesflare, Snov.io, and HubSpot Sales Hub. Key trends in the sales tools market include the integration of artificial intelligence (AI) and machine learning (ML) into sales software, the increasing use of mobile sales tools, and the adoption of data analytics to improve sales performance. Additionally, the shift towards remote and hybrid work models is fostering the demand for cloud-based sales tools that enable seamless collaboration and productivity.

The online revenue of gemini.pl amounted to US$98.9m in 2024. Discover eCommerce insights, including sales development, shopping cart size, and many more.

Retail Fashion Boutique Data Sales Analytics 2025

Overview

This comprehensive fashion retail synthetic dataset contains 2,176 real-world style records spanning seasonal collections, customer purchasing behavior, pricing strategies, and return analytics. Perfect for data science projects, machine learning models, and business intelligence dashboards focused on retail analytics and e-commerce insights.

Dataset Highlights

• 📊 Complete Sales Cycle: Purchase patterns, pricing strategies, and customer feedback
• 🔄 Return Analytics: Detailed return tracking with specific reasons and patterns
• 🛍️ Multi-Brand Coverage: 8 major fashion brands across diverse product categories
• 📈 Seasonal Intelligence: Four-season data with realistic markdown strategies
• ⭐ Customer Insights: Rating systems and purchasing behavior analysis
• 💰 Pricing Analytics: Original pricing, markdown percentages, and final pricing data

Key Applications

• Retail Analytics: Sales performance analysis and trend identification
• Customer Segmentation: Behavior analysis and purchasing pattern recognition
• Inventory Management: Stock optimization and seasonal demand forecasting
• Return Prediction: Machine learning models for return likelihood prediction
• Pricing Strategy: Dynamic pricing and markdown optimization analysis
• Business Intelligence: Comprehensive retail KPI dashboards and reporting

Column Details

Data Quality Features

• ✅ Realistic Business Logic: 15% return rate matching industry standards
• ✅ Seasonal Pricing: Authentic markdown patterns aligned with retail cycles
• ✅ Missing Data Handling: Strategic nulls for data cleaning practice (15% in ratings, size nulls for accessories)
• ✅ Balanced Distribution: Even representation across brands, categories, and seasons
• ✅ Price Consistency: Mathematically accurate pricing with discount calculations

Perfect For

• Data Analytics Projects: Retail KPI analysis, sales forecasting, customer behavior studies
• Machine Learning Models: Return prediction, demand forecasting, recommendation systems
• Business Intelligence: Executive dashboards, performance tracking, trend analysis
• Academic Research: Retail analytics case studies, pricing strategy research
• Portfolio Development: Comprehensive data science project demonstrations

File Formats Available

• CSV: Universal compatibility for data analysis tools
• Excel: Business reporting and stakeholder presentations
• JSON: API integration and web applications
• SQL: Database integration and advanced querying

Sample Use Cases

1. Return Prediction Model: Build ML models to predict return likelihood based on product attributes
2. Seasonal Demand Forecasting: Analyze purchasing patterns across different seasons and categories
3. Pricing Optimization: Study markdown effectiveness and optimal pricing strategies
4. Customer Satisfaction Analysis: Correlate ratings with return patterns and product characteristi...

The sales performance management market size is projected to grow from USD 3.15 billion in the current year to USD 14.83 billion by 2035, representing a CAGR of 15.14%, during the forecast period till 2035.

The Predictive Sales Analytics Tools market is experiencing robust growth, driven by the increasing need for businesses to improve sales forecasting accuracy, optimize resource allocation, and enhance customer engagement. The market, currently valued at approximately $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant growth is fueled by several key factors, including the rising adoption of cloud-based solutions, advancements in artificial intelligence (AI) and machine learning (ML) technologies, and the growing demand for data-driven decision-making across various industries. Businesses are increasingly leveraging predictive analytics to identify high-potential leads, personalize sales strategies, and improve sales team performance, leading to substantial returns on investment. The market is segmented by deployment (cloud-based, on-premise), by application (lead scoring, sales forecasting, opportunity management), and by industry (BFSI, healthcare, retail, manufacturing), each segment contributing uniquely to the overall growth trajectory. Major players such as XANT, EverString, Dun & Bradstreet, and others are driving innovation within the market through continuous product development and strategic partnerships. The competitive landscape is characterized by both established players and emerging startups, fostering innovation and creating diverse solution offerings. However, challenges such as data security concerns, the need for skilled professionals to implement and interpret the analytics, and the high initial investment costs can potentially hinder market growth. Nevertheless, the overwhelming benefits of improved sales efficiency and revenue generation are expected to overcome these hurdles, ensuring sustained market expansion in the coming years.

The global Sales Mapping System market is experiencing robust growth, driven by the increasing need for businesses to optimize sales territories, improve route planning, and enhance sales force effectiveness. The market's expansion is fueled by the widespread adoption of cloud-based solutions, offering scalability and accessibility to businesses of all sizes. Large enterprises leverage these systems for comprehensive territory management and strategic sales planning, while SMEs benefit from simplified route optimization and improved customer relationship management (CRM) integration. The integration of Geographic Information System (GIS) technology plays a crucial role, enabling visually rich data analysis and insightful territory mapping. Emerging trends include the incorporation of advanced analytics for predictive modeling and the use of mobile applications for real-time data updates and field management. While the initial investment in implementing these systems can be a restraint for some businesses, the long-term return on investment (ROI) in terms of increased sales efficiency and reduced operational costs is a powerful driver. We estimate the 2025 market size to be approximately $2.5 billion, projecting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, leading to a market value exceeding $7 billion by 2033. This growth trajectory is supported by the continuous innovation in the field, resulting in more user-friendly interfaces and enhanced functionalities. The market is segmented by application (Large Enterprises and SMEs) and type (GIS, CRM integration, and Other). North America currently holds the largest market share, followed by Europe and Asia Pacific. However, developing economies in Asia Pacific are experiencing rapid growth, presenting significant opportunities for market expansion. The competitive landscape includes established players like Workbooks, MapMyCustomers, and Badger Maps, as well as emerging technology providers. These companies are actively involved in product innovation and strategic partnerships to gain a competitive edge. The continued demand for improved sales performance and efficient resource allocation will be key in sustaining this market's impressive growth trajectory throughout the forecast period.

The Data Intelligence Solutions for Sales market is experiencing robust growth, driven by the increasing need for businesses to enhance sales efficiency and improve lead conversion rates. The market, estimated at $15 billion in 2025, is projected to exhibit a healthy Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated market value exceeding $45 billion by 2033. This expansion is fueled by several key factors. Firstly, the widespread adoption of digital sales strategies necessitates access to high-quality data for effective targeting and personalization. Secondly, the growing complexity of sales processes, particularly within larger enterprises, necessitates solutions that streamline operations and provide actionable insights. Finally, the increasing sophistication of data analytics allows for more effective predictive modeling, leading to more accurate sales forecasting and improved resource allocation. Segmentation reveals a strong demand across all enterprise sizes, with large enterprises driving a significant portion of the market due to their greater investment capacity in advanced technologies. Popular functionalities like sales activity management, custom fields and workflows, and lead tracking are crucial components of these solutions. The competitive landscape is highly dynamic, featuring established players like LinkedIn and Dun & Bradstreet alongside specialized providers like Datanyze and Clearbit. These companies offer diverse solutions, ranging from comprehensive CRM integrations to highly focused lead generation tools. Geographic distribution shows significant concentration in North America and Europe, but the Asia-Pacific region is expected to witness significant growth in the coming years, driven by rising digital adoption and expanding business activity in key markets like China and India. The competitive pressure within the market is likely to intensify as innovative companies emerge and established players enhance their offerings. This will drive the need for constant innovation in data intelligence solutions, particularly in areas such as artificial intelligence (AI)-driven insights and predictive analytics. The increasing focus on data privacy and security regulations will necessitate robust security measures and compliance efforts within these solutions. Moreover, the integration of these tools with existing CRM and sales automation systems will remain a critical factor influencing adoption rates. While the market shows immense potential, challenges such as the complexity of data integration and the need for skilled professionals to manage and interpret the data will need to be addressed for sustained growth. Overall, the future of data intelligence solutions for sales is bright, driven by an increasing demand for efficient, data-driven sales strategies across a rapidly evolving global business landscape.

The Data Analysis Storage Management market is projected to be valued at $5 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 12%, reaching approximately $15 billion by 2034.

Retail Sales in the United States increased 0.50 percent in July of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Sales Intelligence Market size was valued at USD 3.26 billion in 2023 and is projected to reach USD 6.68 billion by 2032, exhibiting a CAGR of 10.8 % during the forecasts period. The Sale Intelligence Market is described as technologies and solutions that give client and market insight for sales and marketing effectiveness. They use data analysis, artificial intelligence, and machine language to capture large data from multiple sources such as CRM, social networking sites, and market databases. Some of the benefits of sales intelligence are; the generation of leads, segmenting customers, business sales predictions, and even the identification of competition. It is applied in business areas that include retail health, finance, and technology making it easier for the sales team to make proper decisions as well as increasing the sales performance. The trend in the market has extended to include the incorporation of behaviors or prog behaviors that rely on the feature of sales forecasts, the use of real-time data analysis for decision-making, and the use of effective customer engagement based on the analysis of data collected from the customers. With the evolution of the corporate culture promoting the utilization of business analytics, there is a clear need for integrated and advanced sales tools. Recent developments include: In June 2023, Vidyard Rooms announced the launch of its new Digital Sales Rooms (DSR). The company aims to transform how sellers and buyers engage in the digital-first era., In May 2023, Gong.io Inc. introduced Gong Insights, a new product that automatically transfers insights obtained from the Gong revenue intelligence platform to a company's current business intelligence (BI) platform. The solution is created in collaboration with the U.S.-based data cloud company, Snowflake. , In March 2023, 6sense announced the launch of revenue AI for sales. By making it simpler to locate prospects and accounts in-market for products, prioritize a seller's day with high-impact activities, and identify deeper data about buyers and marketing tools, this new platform was developed to improve sellers' daily lives. , In March 2023, DemandScience US, a top B2B demand generation company, announced the general release of Klarity, its next-generation self-service sales intelligence tool for creating, sharing, and saving contact lists. 'One-click prospecting' is now a reality for sales professionals because of Klarity's Chrome extension, user-friendly UI, and email accuracy. , In February 2023, FlashInfo introduced a new "Job Posting" filter. It enables sales teams to recognize key indicators that point to a prospective buying opportunity, enabling them to target clients and close deals more successfully. By offering access to real time information about job postings and company growth, the "Job Posting" filter is intended to aid sales teams in staying one step ahead of the competition. .