Business Listings Database is the source of point-of-interest data and can provide you with all the information you need to analyze how specific places are used, what kinds of audiences they attract, and how their visitor profile changes over time.

The full fields description may be found on this page: https://docs.dataforseo.com/v3/databases/business_listings/?bash

Number of State of Iowa Google My Business Profiles (office locations)

This dataset provides insights by month on how people find State of Iowa agency listings on the web via Google Search and Maps, and what they do once they find it to include providing reviews (ratings), accessing agency websites, requesting directions, and making calls.

Auto-generated structured data of Google My Business Field Reference from table Fields

This dashboard provide insights by month on how people find State of Iowa agency listings on the web via Google Search and Maps, and what they do once they find it to include providing reviews (ratings), accessing agency websites, requesting directions, and making calls.

The Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

The number of times during the month someone clicked through to the department or agency website from their Google My Business profile.

This Dataset contains review information on Google map (ratings, text, images, etc.), business metadata (address, geographical info, descriptions, category information, price, open hours, and MISC info), and links (relative businesses) up to Sep 2021 in California.

Please note, this is a subset of the orginal datset found here. You can find other federal states there.

Format is one-review-per-line in json. See examples below for further help reading the data.

{
 'user_id': '106533466896145407182', 
 'name': 'Amy VG', 
 'time': 1568748357166, 
 'rating': 5, 
 'text': "I can't say I've ever been excited about a dentist visit before, but there's a first for everything! Loved my experience at Lush today. Every person in the office was friendly and personable- plus the office itself is gorgeous! Great experience, I highly recommend!", 
 'pics': [
 {
  'url': ['https://lh5.googleusercontent.com/p/AF1QipMBzN4BJV9YCObcw_ifNzFPm-u38hO3oimOA8Fb=w150-h150-k-no-p']
 }, 
 {
  'url': ['https://lh5.googleusercontent.com/p/AF1QipNS1PEXEvadfUlhRkRDJ09id
  Mxh3CveZGZYuTo5=w150-h150-k-no-p']
 }
 ], 
 'resp': {
  'time': 1568770503975, 
  'text': 'We love getting to meet new patients like yourself. Thanks for giving our office a chance to take care of your dental needs and thanks for the nice review!'
 }, 
 'gmap_id': '0x87ec2394c2cd9d2d:0xd1119cfbee0da6f3'
}
{
 'user_id': '101463350189962023774', 
 'name': 'Jordan Adams', 
 'time': 1627750414677, 
 'rating': 5, 
 'text': 'Cool place, great people, awesome dentist!', 
 'pics': [
 {
  'url': ['https://lh5.googleusercontent.com/p/AF1QipNq2nZC5TH4_M7h5xRAd
  61hoTgvY1o9lozABguI=w150-h150-k-no-p']
 }
 ], 
 'resp': {
  'time': 1628455067818, 
  'text': 'Thank you for your five-star review! -Dr. Blake'
 }, 
 'gmap_id': '0x87ec2394c2cd9d2d:0xd1119cfbee0da6f3'
}

where

user_id - ID of the reviewer name - name of the reviwer time - time of the review (unix time) rating - rating of the business text - text of the review pics - pictures of the review resp - business response to the review including unix time and text of the response gmap_id - ID of the business

Citations

UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining Jiacheng Li, Jingbo Shang, Julian McAuley Annual Meeting of the Association for Computational Linguistics (ACL), 2022 pdf Personalized Showcases: Generating Multi-Modal Explanations for Recommendations An Yan, Zhankui He, Jiacheng Li, Tianyang Zhang, Julian Mcauley The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2023 pdf

Acknowlegement

Foto von Maarten van den Heuvel auf Unsplash

Reports the average customer ratings for State of Iowa Google My Business profiles.

The number of times during the month someone called State Offices from their Google My Business profiles.

Google Data for Market Intelligence, Business Validation & Lead Enrichment Google Data is one of the most valuable sources of location-based business intelligence available today. At Canaria, we’ve built a robust, scalable system for extracting, enriching, and delivering verified business data from Google Maps—turning raw location profiles into high-resolution, actionable insights.

Our Google Maps Company Profile Data includes structured metadata on businesses across the U.S., such as company names, standardized addresses, geographic coordinates, phone numbers, websites, business categories, open hours, diversity and ownership tags, star ratings, and detailed review distributions. Whether you're modeling a market, identifying leads, enriching a CRM, or evaluating risk, our Google Data gives your team an accurate, up-to-date view of business activity at the local level.

This dataset is updated daily and is fully customizable, allowing you to pull exactly what you need, whether you're targeting a specific geography, industry segment, review range, or open-hour window.

What Makes Canaria’s Google Data Unique? • Location Precision – Every business record is enriched with latitude/longitude, ZIP code, and Google Plus Code to ensure exact geolocation • Reputation Signals – Review tags, star ratings, and review counts are included to allow brand sentiment scoring and risk monitoring • Diversity & Ownership Tags – Capture public-facing declarations such as “women-owned” or “Asian-owned” for DEI, ESG, and compliance applications • Contact Readiness – Clean, standardized phone numbers and domains help teams route leads to sales, support, or customer success • Operational Visibility – Up-to-date open hours, categories, and branch information help validate which locations are active and when

Our data is built to be matched, integrated, and analyzed—and is trusted by clients in financial services, go-to-market strategy, HR tech, and analytics platforms.

What This Google Data Solves Canaria Google Data answers critical operational, market, and GTM questions like:

• Which businesses are actively operating in my target region or category? • Which leads are real, verified, and tied to an actual physical branch? • How can I detect underperforming companies based on review sentiment? • Where should I expand, prospect, or invest based on geographic presence? • How can I enhance my CRM, enrichment model, or targeting strategy using location-based data?

Key Use Cases for Google Maps Business Data Our clients leverage Google Data across a wide spectrum of industries and functions. Here are the top use cases:

Lead Scoring & Business Validation • Confirm the legitimacy and physical presence of potential customers, partners, or competitors using verified Google Data • Rank leads based on proximity, star ratings, review volume, or completeness of listing • Filter spammy or low-quality leads using negative review keywords and tag summaries • Validate ABM targets before outreach using enriched business details like phone, website, and hours

Location Intelligence & Market Mapping • Visualize company distributions across geographies using Google Maps coordinates and ZIPs • Understand market saturation, density, and white space across business categories • Identify underserved ZIP codes or local business deserts • Track presence and expansion across regional clusters and industry corridors

Company Risk & Brand Reputation Scoring • Monitor Google Maps reviews for sentiment signals such as “scam”, “spam”, “calls”, or service complaints • Detect risk-prone or underperforming locations using star rating distributions and review counts • Evaluate consistency of open hours, contact numbers, and categories for signs of listing accuracy or abandonment • Integrate risk flags into investment models, KYC/KYB platforms, or internal alerting systems

CRM & RevOps Enrichment • Enrich CRM or lead databases with phone numbers, web domains, physical addresses, and geolocation from Google Data • Use business category classification for segmentation and routing • Detect duplicates or outdated data by matching your records with the most current Google listing • Enable advanced workflows like field-based rep routing, localized campaign assignment, or automated ABM triggers

Business Intelligence & Strategic Planning • Build dashboards powered by Google Maps data, including business counts, category distributions, and review activity • Overlay business presence with population, workforce, or customer base for location planning • Benchmark performance across cities, regions, or market verticals • Track mobility and change by comparing past and current Google Maps metadata

DEI, ESG & Ownership Profiling • Identify minority-owned, women-owned, or other diversity-flagged companies using Google Data ownership attributes • Build datasets aligned with supplier diversity mandates or ESG investment strategies • Segment location insights by ownership type ...

Recent analysis of Google My Business (GMB) data in select countries found that there was an increase of customer conversion based on the average review star rating of a business. As of 2019, businesses experiences a pivotal growth moment of customers conversions when reaching a *** rating on a * star scale.

Introduction: I have chosen to complete a data analysis project for the second course option, Bellabeats, Inc., using a locally hosted database program, Excel for both my data analysis and visualizations. This choice was made primarily because I live in a remote area and have limited bandwidth and inconsistent internet access. Therefore, completing a capstone project using web-based programs such as R Studio, SQL Workbench, or Google Sheets was not a feasible choice. I was further limited in which option to choose as the datasets for the ride-share project option were larger than my version of Excel would accept. In the scenario provided, I will be acting as a Junior Data Analyst in support of the Bellabeats, Inc. executive team and data analytics team. This combined team has decided to use an existing public dataset in hopes that the findings from that dataset might reveal insights which will assist in Bellabeat's marketing strategies for future growth. My task is to provide data driven insights to business tasks provided by the Bellabeats, Inc.'s executive and data analysis team. In order to accomplish this task, I will complete all parts of the Data Analysis Process (Ask, Prepare, Process, Analyze, Share, Act). In addition, I will break each part of the Data Analysis Process down into three sections to provide clarity and accountability. Those three sections are: Guiding Questions, Key Tasks, and Deliverables. For the sake of space and to avoid repetition, I will record the deliverables for each Key Task directly under the numbered Key Task using an asterisk (*) as an identifier.

Section 1 - Ask: A. Guiding Questions: Who are the key stakeholders and what are their goals for the data analysis project? What is the business task that this data analysis project is attempting to solve?

B. Key Tasks: Identify key stakeholders and their goals for the data analysis project *The key stakeholders for this project are as follows: -Urška Sršen and Sando Mur - co-founders of Bellabeats, Inc. -Bellabeats marketing analytics team. I am a member of this team. Identify the business task. *The business task is: -As provided by co-founder Urška Sršen, the business task for this project is to gain insight into how consumers are using their non-BellaBeats smart devices in order to guide upcoming marketing strategies for the company which will help drive future growth. Specifically, the researcher was tasked with applying insights driven by the data analysis process to 1 BellaBeats product and presenting those insights to BellaBeats stakeholders.

Section 2 - Prepare: A. Guiding Questions: Where is the data stored and organized? Are there any problems with the data? How does the data help answer the business question?

B. Key Tasks: Research and communicate the source of the data, and how it is stored/organized to stakeholders. *The data source used for our case study is FitBit Fitness Tracker Data. This dataset is stored in Kaggle and was made available through user Mobius in an open-source format. Therefore, the data is public and available to be copied, modified, and distributed, all without asking the user for permission. These datasets were generated by respondents to a distributed survey via Amazon Mechanical Turk reportedly (see credibility section directly below) between 03/12/2016 thru 05/12/2016. *Reportedly (see credibility section directly below), thirty eligible Fitbit users consented to the submission of personal tracker data, including output related to steps taken, calories burned, time spent sleeping, heart rate, and distance traveled. This data was broken down into minute, hour, and day level totals. This data is stored in 18 CSV documents. I downloaded all 18 documents into my local laptop and decided to use 2 documents for the purposes of this project as they were files which had merged activity and sleep data from the other documents. All unused documents were permanently deleted from the laptop. The 2 files used were: -sleepDaymerged.csv -dailyActivitymerged.csv Identify and communicate to stakeholders any problems found with the data related to credibility and bias. *As will be more specifically presented in the Process section, the data seems to have credibility issues related to the reported time frame of the data collected. The metadata seems to indicate that the data collected covered roughly 2 months of FitBit tracking. However, upon my initial data processing, I found that only 1 month of data was reported. *As will be more specifically presented in the Process section, the data has credibility issues related to the number of individuals who reported FitBit data. Specifically, the metadata communicates that 30 individual users agreed to report their tracking data. My initial data processing uncovered 33 individual IDs in the dailyActivity_merged dataset. *Due to the small number of participants (...

Context

** One analysis Done in spreadsheets with 202004 and 202005 data **

Content

To adjust for outlier Ride lengths like the max and min below: Max RL =MAX(N:N)978:40:02 minimum RL =MIN(N:N)-0:02:56

TRIMMean to shave off the top and bottom of a dataset. TRIMMEAN =TRIMMEAN(N:N,5%)0:20:20 =TRIMMEAN(N:N,2%)0:21:27

Otherwise the Ride length for 202004 is Average RL 0:35:51

The most common day of the week is Sunday. There are 61,148 members and 23,628 casual riders. mode of DOW 1 CountIf member of MC 61148 CountIf casual of MC 23628

Pivot table 1 2020-04 member_casual AVERAGE of ride_length

Same calculations for 2020-05 Average RL 0:33:23 Max RL 481:36:53 minimum RL -0:01:48 mode of DOW 7 CountIf member of MC 113365 CountIf casual of MC 86909 TRIMMEAN 0:25:22 0:26:59

There are 4 pivot tables included in seperate sheets for other comparisons.

Acknowledgements

I gathered this data using the sources provided by the Google Data Analytics course. All work seen is done by myself.

Inspiration

I want to further use the data in SQL, and Tableau.

The local listing management software market is experiencing robust growth, driven by the increasing importance of online presence for businesses of all sizes. The need to manage online listings across multiple platforms like Google My Business, Yelp, Facebook, and others efficiently and accurately fuels this demand. Businesses are realizing that consistent and accurate information across all listings directly impacts local search rankings, driving foot traffic and sales. A projected CAGR of, let's assume, 15% (a reasonable estimate for a rapidly evolving tech sector like this) from 2025 to 2033 indicates a significant expansion of the market. This growth is further fueled by trends such as the rising adoption of mobile devices and the increasing sophistication of local SEO strategies. The market is segmented by software features (e.g., review management, citation building, reporting and analytics), deployment type (cloud-based vs. on-premise), and business size (SMB, enterprise). Competition is strong, with established players like Reputation, Moz, and Yext competing with emerging solutions. However, the market is large enough to support multiple providers as the need for sophisticated local listing management continues to grow. Challenges, such as the complexity of managing multiple platforms and the constant evolution of search engine algorithms, are continuously being addressed by the software providers, leading to improved functionalities and user experience. The market's expansion is fueled by the rising adoption of location-based services and the increasing demand for improved customer experience. This necessitates accurate and up-to-date business information across various online platforms. The increasing use of data analytics within the software allows businesses to track performance, identify areas for improvement, and measure ROI on their local SEO strategies. This data-driven approach further encourages adoption. Furthermore, the integration of social media management and review monitoring into these platforms is a significant factor in the market’s continuous growth. The continued expansion of e-commerce and online ordering also contributes to the importance of accurate local listings, linking online profiles directly to physical locations and facilitating easy transactions. We can anticipate continued innovation within the sector, with new features and integrations emerging to meet the evolving needs of businesses in the digital landscape.

The interactive map creation tools market is experiencing robust growth, driven by increasing demand for visually engaging data representation across diverse sectors. The market's value is estimated at $2 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several factors, including the rising adoption of location-based services, the proliferation of readily available geographic data, and the growing need for effective data visualization in business intelligence and marketing. The individual user segment currently holds a significant share, but corporate adoption is rapidly expanding, propelled by the need for sophisticated map-based analytics and internal communication. Furthermore, the paid use segment is anticipated to grow more quickly than the free use segment, reflecting the willingness of businesses and organizations to invest in advanced features and functionalities. This trend is further amplified by the increasing integration of interactive maps into various platforms, such as business intelligence dashboards and website content. Geographic expansion is also a significant growth driver. North America and Europe currently dominate the market, but the Asia-Pacific region is showing significant promise due to rapid technological advancements and increasing internet penetration. Competitive pressures remain high, with established players such as Google, Mapbox, and ArcGIS StoryMaps vying for market share alongside innovative startups offering specialized solutions. The market's restraints are primarily focused on the complexities of data integration and the technical expertise required for effective map creation. However, ongoing developments in user-friendly interfaces and readily available data integration tools are mitigating these challenges. The future of the interactive map creation tools market promises even greater innovation, fueled by developments in augmented reality (AR), virtual reality (VR), and 3D visualization technologies. We expect to see the emergence of more sophisticated tools catering to niche requirements, further driving market segmentation and specialization. Continued investment in research and development will also play a crucial role in pushing the boundaries of what's possible with interactive map creation. The market presents opportunities for companies to develop tools which combine data analytics and interactive map design.

This is an analysis of the Cyclistic rider usage data for the Google Data Analytics Professional Certification Capstone. This analysis will guide you through how I determined the difference in usage between Cyclistic members and non-members (casual user). This project will guide you through my process in R and answer the business question while providing 3 recommendations.

I took the limited data set for this project that I cleaned, organized, and transformed the data into a presentation for the fictitious company. This was to showcase my ability to analyze, clean, & visualize datasets to find insights and present them in a way that could represent a real life scenario.

This case study was the capstone project at the end of my Online course from Google in Data Analytics. This comes from a relatively large but limited data spreadsheet from a fictitious bicycle share service. In the scenario you were required to answer the first of three questions posed to the data analyst with a limited data set: “How do annual members differ from casual users?” This was to help the marketing lead build a new campaign to sign up casual users of the service to annual members based on findings from the financial analysts.

The number of times during the month someone searched the name of a State of Iowa Office with a Google My Business profile using Google Search or while on Google Maps.