Restaurant email list is a database of contact information for professionals working in the Restaurant. These lists are created for businesses that want to sell their products or services to Restaurant. Instead of general contacts, these lists often focus on key decision-makers who are responsible for purchasing. There are, such as general managers, Restaurant owners, directors of sales or marketing, and directors of food and beverage. Moreover, you don’t have to search for contacts. A pre-built list saves time. This lets your sales team focus on building relationships and closing deals. Five star restaurants often have booking systems on their websites. This makes it simple for guests to choose and reserve a room.

Restaurant email list is crucial for any business that sells to the hospitality industry. You can contact people likely to buy your product. This includes software, cleaning supplies, or amenities. You can send specific messages to them. This leads to better results and more interest. In short, this lead is a powerful tool for efficiently and effectively connecting with the hospitality industry. This helps people book rooms even when they are on the go. Resorts, which have many activities, should highlight these on their websites. So, get it now from our website, List to Data. Restaurant email database is a list that shows the phone numbers of all restaurant companies. This resource is just what you need. This special list includes verified phone numbers of all nationals across global, giving you direct access to this important group. Also, this isn’t just any list; it’s carefully made to help you get the best results.

Restaurant email database can create marketing messages that speak directly to the community. This makes your efforts more successful and helps build trust with your audience. It’s also great for businesses looking to form partnerships, find investors, or explore new markets. Don’t waste time on general marketing that doesn’t work.

Listing of all active businesses currently registered with the Office of Finance. An "active" business is defined as a registered business whose owner has not notified the Office of Finance of a cease of business operations. Update Interval: Monthly.

🧾 All Mizoram Restaurant & Food Outlet Database – Verified & Updated Contact Directory in ExcelThe All Mizoram Restaurant & Food Outlet Database is a comprehensive, verified, and regularly updated Excel directory of active restaurants, cafés, cloud kitchens, and food vendors across Mizo...

Explore detailed Palm Kernel Oil import data of Faca Restaurant Owners Crop in the USA—product details, price, quantity, origin countries, and US ports.

Zomato🍕🍮 is a popular online platform and mobile app that provides information about restaurants, cafes, and other food outlets. It offers a comprehensive database of eateries in various cities worldwide, including their menus, contact details, location, operating hours, customer reviews, and ratings. Users can search for restaurants based on their preferences, such as cuisine type, price range, and location.

The platform also allows users to explore photos of dishes, read and write reviews, and make online reservations or orders for delivery and takeaway services. Zomato provides a convenient and user-friendly interface for food enthusiasts to discover new dining options, share their experiences, and make informed decisions when dining out or ordering food.

For restaurant owners, Zomato offers a significant online presence and marketing opportunity, enabling them to showcase their offerings to a large audience of potential customers. Restaurants can manage their profiles, respond to user reviews, and improve their visibility and reputation on the platform.

Zomato has become a key player in the food and restaurant industry, serving as a reliable resource for both customers seeking dining options and restaurants looking to connect with their target audience. The data generated from user interactions, reviews, and ratings on Zomato can be valuable for various data analyses and insights related to restaurant preferences, customer behavior, and dining trends.

About this Dataset

Contact information for over 20,000 restaurants across the US. All restaurants from the NAICS code 72251: Restaurants and Other Eating Places. This includes all set down, fast casual, fast food, and ethnic restaurants. List includes name, address, phone number, website, contact email address, and a brief description. Data was collected from a combination of web scrapping and manual data entry. Similar lists cost over $1500 from lead generation and business data companies.

Category

Lead Generation

Keywords

restaurants,contact,mailing

Row Count

21210

Price

$499.00

This information is derived from inspections of restaurants and other food establishments in Chicago from January 1, 2010 to the present. Inspections are performed by staff from the Chicago Department of Public Health’s Food Protection Program using a standardized procedure. The results of the inspection are inputted into a database, then reviewed and approved by a State of Illinois Licensed Environmental Health Practitioner (LEHP). For descriptions of the data elements included in this set, go to http://bit.ly/tS9IE8
Disclaimer: Attempts have been made to minimize any and all duplicate inspection reports. However, the dataset may still contain such duplicates and the appropriate precautions should be exercised when viewing or analyzing these data. The result of the inspections (pass, pass with conditions or fail) as well as the violations noted are based on the findings identified and reported by the inspector at the time of the inspection, and may not reflect the findings noted at other times. For more information about Food Inspections, go to http://bit.ly/tD91Sb. Data Owner: Chicago Department of Public Health. Time Period: 2010 - Present. Frequency: Data is updated weekly.

The Health Division of the Department of Inspectional Services (ISD) creates and enforces food safety codes to protect public health. All businesses which prepare and sell food to the public must possess a food service permit. In order to qualify for a permit, at least one full time employee must be must be certified through an accredited food manager program, which provides guidance on handling and serving food to the public.

This dataset contains a list of restaurants that met the City's standards to become licensed food service establishments.

Explore the historical Whois records related to restaurant-manager-jobs-search.com (Domain). Get insights into ownership history and changes over time.

This dataset provides restaurant inspections, violations, grades and adjudication information

.RESTAURANT Whois Database, discover comprehensive ownership details, registration dates, and more for .RESTAURANT TLD with Whois Data Center.

Eateries in in New York City Department of Parks & Recreation properties including snack bars, food carts, mobile food trucks, and restaurants.

"The data set provided to you is the data set of a Café Chain for one of its restaurants. Do a thorough analysis of the data.

The owner of the restaurant wants you to use this data to come up with a set of recommendations that can help his Café Chain increase his revenues. He has not been able to launch a loyalty program and is unable to provide you with a data set that has customer level information. But, he is able to provide you with a data set for POS (point of sale data) for one of his chains.

Restaurant Accounting Software Market Outlook

The global restaurant accounting software market size was valued at approximately USD 2.5 billion in 2023 and is projected to reach around USD 5.3 billion by 2032, registering a compound annual growth rate (CAGR) of 9.1% during the forecast period. This significant growth can be attributed to the increasing digitization of restaurant operations and the growing need for efficient financial management tools in the hospitality sector. The market is driven by a robust demand for solutions that streamline accounting processes, improve financial accuracy, and enhance overall operational efficiency. Additionally, the rise of cloud-based technologies and the increasing adoption of mobile devices are further propelling the market forward.

One of the primary growth factors for the restaurant accounting software market is the escalating complexity of financial operations within the restaurant industry. As restaurants continue to expand their operations both locally and globally, managing multiple revenue streams, diverse supplier contracts, and varying tax regulations has become increasingly challenging. This complexity necessitates advanced accounting solutions that can automate financial processes, reduce errors, and provide real-time insights into financial performance. Consequently, restaurant owners and managers are increasingly investing in sophisticated accounting software to gain a competitive edge, ensuring compliance and optimizing their financial management practices.

Another significant growth driver is the rising demand for data-driven decision-making in the restaurant industry. With the advent of big data and analytics, restaurant owners are placing greater emphasis on leveraging financial data to drive strategic business decisions. Restaurant accounting software provides comprehensive reporting and analytics capabilities that allow businesses to track key performance indicators, analyze trends, and forecast financial outcomes. This data-driven approach enables restaurants to make informed decisions regarding menu pricing, cost control, and resource allocation, ultimately leading to improved profitability and business sustainability. The growing recognition of the value of data analytics is fueling the adoption of restaurant accounting software across the industry.

Moreover, the increasing focus on enhancing customer experiences is indirectly contributing to the growth of the restaurant accounting software market. Today's consumers expect personalized, efficient, and seamless dining experiences, prompting restaurants to invest in technologies that improve service delivery and customer engagement. Accounting software solutions integrate with point-of-sale systems and other customer-facing technologies, enabling restaurants to offer faster transactions, accurate billing, and loyalty program integration. By providing these enhanced customer experiences, restaurants can attract and retain more customers, thereby driving revenue growth and necessitating advanced accounting software to manage the associated financial transactions efficiently.

The regional outlook for the restaurant accounting software market reveals a diverse landscape with varying growth trajectories across different regions. North America currently holds a significant share of the market, driven by the presence of a large number of restaurant chains and the early adoption of advanced technologies. The region's mature market is characterized by a high level of digital literacy and a strong focus on operational efficiency, contributing to the widespread implementation of restaurant accounting software. In contrast, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to the rapid expansion of the foodservice industry and increasing investments in digital infrastructure. The growing middle-class population and rising disposable incomes in countries like China and India are further fueling the demand for restaurant accounting solutions.

In the broader landscape of financial management within the hospitality industry, tools such as the Retail Accounting Tool have emerged as vital resources for businesses seeking to optimize their financial operations. This tool is particularly beneficial for retail-oriented restaurant chains that require precise tracking of sales, inventory, and customer transactions. By integrating with existing point-of-sale systems, the Retail Accounting Tool provides real-time data analytics, enabling restaurant managers to make infor

If you're looking to share a valuable dataset on Kaggle, consider uploading the TripAdvisor New York Restaurant Dataset, which contains over 10,000 records of restaurant reviews in New York. The dataset comprises several key entities, including restaurant titles, reviews, comments, popular foods, categories, and whether online delivery is available or not.

With this dataset, data analysts, researchers, and machine learning practitioners can gain insights into restaurant trends in New York and make data-driven decisions. For instance, by analyzing the most popular foods or categories, restaurant owners can tailor their menus and services to meet the demands of customers. Data scientists can also develop predictive models that forecast restaurant sales or customer satisfaction based on specific factors like reviews, categories, or delivery options.

To upload this dataset, start by preparing the data in a standard CSV format with appropriate headers for each field. Then, create a Kaggle account, navigate to the Datasets tab, and click "New Dataset." Fill in the relevant information, including a dataset title, description, and license, and upload the CSV file.

Be sure to provide clear documentation for the dataset, including data cleaning and preprocessing steps, any known limitations, and guidelines for proper usage. This information will help other users understand the dataset and use it effectively in their analyses.

Overall, sharing the TripAdvisor New York Restaurant Dataset on Kaggle can be a valuable contribution to the data science community and provide insights into one of the most vibrant food scenes in the world.

Aresta Sa Restaurant Manager Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Disclaimer:

I am not the owner of this dataset. My sole intention is to make the dataset easily available to enthusiasts who are curious about Entity Resolution. Here is the original source of the dataset. The dataset is also available through a R package, from which I downloaded it.

Description

The restaurant dataset is created with the help of 864 restaurant records from two different data sources (Fodor’s and Zagat’s restaurant guides) provided by Sheila Tejada. Restaurants are described by name, address, city, phone and category. Among these, 112 record pairs refer to the same entity present in the dataset.

Data characteristic

Size & targets

• 864 records
• 112 matches

Data Attributes

• name: Name of restaurant.
• address: Address of restaurant.
• city: In what city the restaurant is located.
• phone: Restaurant's phone number.
• category: Category of cuisines served at restaurant.
• cluster: Entity cluster id.

Restaurant POS Software Market Size And Forecast

Restaurant POS Software Market size was valued at USD 9.44 Billion in 2023 and is projected to reach USD 17.88 billion By 2030, growing at a CAGR of 6.88% during the forecast period 2024 to 2030.

Global Restaurant POS Software Market Drivers

The market drivers for the Restaurant POS Software Market can be influenced by various factors. These may include:

Growing Need for Efficiency: To increase customer satisfaction, boost productivity, and streamline operations, restaurant owners are looking for point-of-sale (POS) systems. By automating processes like order management, inventory tracking, and billing, point-of-sale (POS) software lowers human error and saves time.

Expanding Cloud-based Solution Adoption: Systems for point-of-sale hosted in the cloud provide accessibility, scalability, and flexibility from any location with an internet connection. Restaurant operators can now remotely access real-time data and analytics without having to purchase expensive on-premises technology thanks to this concept.

Integration of Advanced Features: Current point-of-sale (POS) systems incorporate advanced features including tableside ordering, internet ordering, loyalty programs, customer relationship management (CRM), and kitchen display systems (KDS). These features provide restaurants a competitive edge, improve customer satisfaction, and boost sales.

Demand for Contactless Solutions: The COVID-19 pandemic has prompted eateries to embrace contactless ordering and payment systems more quickly. In order to satisfy changing client demands and maintain security, point-of-sale software suppliers are providing mobile wallet connections, QR code-based ordering, and contactless payment options.

Emphasis on Data Analytics: POS systems for restaurants offer insightful data that helps companies make decisions. Restaurant owners can increase profitability by optimizing menu offerings, pricing tactics, and inventory management through the analysis of sales trends, customer preferences, and inventory levels.

The rise of omnichannel strategies: To engage customers across many channels, such as in-store dining, online ordering, smartphone apps, and delivery services, restaurants are implementing omnichannel strategies. Orders, payments, and inventory are all effortlessly managed across different channels thanks in large part to POS software.

Restaurants number of regulations: Restaurants are required to abide by a number of regulations pertaining to payment processing, food safety, and tax reporting. By automating compliance-related processes including tax computations, reporting, and accounting software integration, point-of-sale (POS) software lowers the possibility of non-compliance fines.

Global Restaurant Chain Expansion: As restaurant chains and franchisees open more locations worldwide, there is an increasing need for scalable point-of-sale (POS) systems that facilitate multi-location operations, centralized management, and reporting.

1. Introduction The purpose of this project was to analyze consumer preferences for restaurant attributes to identify the most desirable configurations for restaurants. This analysis focused on four key attributes of restaurant experiences: 1. Cuisine: Asian, American, Italian, Mexican 2. Dietary Considerations: No Dietary Restrictions, Vegan-Vegetarian, Keto, Pescatarian 3. Ambiance: Family Friendly, Fine Dining, Live Entertainment, Bar/Tavern 4. Menu Type: À la carte, Pre-set, Buffet, Seasonal Understanding consumer preferences for these attributes is essential for restaurant owners and marketers to tailor their offerings to meet customer demand effectively. (Kotler & Keller, 2016) The survey sought to uncover which combinations of these attributes would resonate most with the target audience and to identify statistically significant patterns in preferences.

2. Survey Design and Data Collection 2.1 Goal of the Survey The goal of the survey was to identify the most preferred restaurant configuration among respondents by: • Determining which specific attributes (e.g., cuisine type, ambiance) were most popular. • Understanding the intensity of preferences for different options within each attribute. • Using statistical methods to evaluate whether the observed differences in preferences were meaningful and significant. This information would help restaurant businesses make informed decisions about menu design, ambiance choices, and dietary offerings.

2.2 Survey Structure The survey contained a key question: "What would be the ideal type of restaurant you would like to have? (Please select one option for each attribute):" • Cuisine: Asian, American, Italian, Mexican • Dietary Considerations: No Dietary Restrictions, Vegan-Vegetarian, Keto, Pescatarian • Ambiance: Family Friendly, Fine Dining, Live Entertainment, Bar/Tavern • Menu Type: À la carte, Pre-set, Buffet, Seasonal Participants were asked to select one option from each attribute category, resulting in four responses per participant.

2.3 Survey Methodology The survey used a Ranked Choice and MaxDiff Scaling hybrid approach: 1. Ranked Choice: Participants ranked their preferred options within each attribute. This method allowed for a straightforward analysis of the most and least popular options for each attribute. Why Ranked Choice? (Tideman & Richardson, 1976) o It is simple for respondents to understand and complete. o It provides clear insights into preference hierarchies for each category. 2. MaxDiff Scaling (Best-Worst Scaling): Implicitly embedded in the design, MaxDiff allowed us to calculate scores by comparing the most and least preferred options, effectively capturing the intensity of preferences. Why MaxDiff? (Louviere, Hensher, & Swait, 2000) o It helps identify which options are strongly favored or disfavored. o It avoids the bias of traditional rating scales (e.g., everyone ranking everything as "highly preferred").

2.4 Data Collection The survey data was collected from respondents using an online platform. Each respondent’s answers for the four attributes were recorded in a structured format. The data included 285 valid responses, with the following key attributes: • Q10_1: Cuisine (Asian, American, Italian, Mexican) • Q10_2: Dietary Considerations (No Dietary Restrictions, Vegan-Vegetarian, Keto, Pescatarian) • Q10_3: Ambiance (Family Friendly, Fine Dining, Live Entertainment, Bar/Tavern) • Q10_4: Menu Type (À la carte, Pre-set, Buffet, Seasonal)

3. Methodology for Analysis 3.1 Descriptive Analysis The raw counts for each option in Q10_1 to Q10_4 were calculated to determine the most frequently selected preferences within each attribute.

3.2 Statistical Testing To evaluate whether the observed differences in preferences were statistically significant, Chi-Square tests of independence were conducted for each attribute. The null hypothesis for each test stated that the proportions of preferences were evenly distributed across all categories.

📹 Project Video available on YouTube - https://youtu.be/dQwnyCEZ-UU

🖇️Connect with me on LinkedIn - https://www.linkedin.com/in/rohit-grewal

It is a sales data of a restaurant company operating in multiple cities in the world. It contains information about individual sales transactions, customer demographics, and product details. The data is structured in a tabular format, with each row representing a single record and each column representing a specific attribute. This dataset can be commonly used for business intelligence, sales forecasting, and customer behaviour analysis.

Using this dataset, we answered multiple questions with Python in our Project.

Q.1) Most Preferred Payment Method ?

Q.2) Most Selling Product - By Quantity & By Revenue ?

Q.3) Which city had maximum revenue , or , Which Manager earned maximum revenue ?

Q.4) Date wise revenue.

Q.5) Average Revenue.

Q.6) Average Revenue of November & December month.

Q.7) Standard Deviation of Revenue and Quantity ?

Q.8) Variance of Revenue and Quantity ?

Q.9) Is revenue increasing or decreasing over time?

Q.10) Average 'Quantity Sold' & 'Average Revenue' for each product ?

These are the main Features/Columns available in the dataset :

1) Order ID: A unique identifier for each sales order. This can be used to track individual transactions.

2) Order Date: The date when the order was placed. This column is essential for time-series analysis, such as identifying sales trends over time or seasonality.

3) Product: The name or type of the product sold. This column is crucial for analyzing sales performance by product category.

4) Price : The unit price of the product. This, along with 'Quantity Ordered', is used to calculate the total price of each order.

5) Quantity : The number of units of the product sold in a single order. This is a key metric for calculating revenue and understanding sales volume.

6) Purchase Type : The order was made online or in-store or drive-thru.

7) Payment Method : How the payment for the order was done.

8) Manager : Name of the manager of the store.

9) City : The location of the store. This can be used for geographical analysis of sales, such as identifying top-performing regions or optimizing logistics.