The Billboard And Outdoor Advertising Market size was valued at USD 34.90 billion in 2023 and is projected to reach USD 59.43 billion by 2032, exhibiting a CAGR of 7.9 % during the forecasts period. The Billboard and Outdoor Advertising Market encompasses the industry specializing in outdoor advertising referring to any advertising that is placed on outdoor mediums including billboards, transit ad surfaces, street furniture, and also electronic panels. These platforms are situated in busy areas where a large number of people pass through to ensure that a maximum number of people access the information passed through them. Some of the applications of outdoor advertising are; creating brand familiarity, selling products and services, public health and civic reminders, and identifying events. Field adoption cuts across, retail, hospitality, automotive industries, and entertainment industries. Other trends that are evident in the market include a shift towards digital billboards for dynamic content, place-based and mobile advertising as well as going green trends such as energy-efficient digital displays and the use of eco-friendly products. With the growth of cities and the development of digital media, the outdoors is still a flexible and effective method to communicate with the audience from densely urbanized areas as well as from suburbia.

TechCorner Mobile Purchase & Engagement Data (2024-2025)

Context

TechCorner Mobile Sales & Customer Insights is a real-world dataset capturing 10 months of mobile phone sales transactions from a retail shop in Bangladesh. This dataset was designed to analyze customer location, buying behavior, and the impact of Facebook marketing efforts.

The primary goal was to identify whether customers are from the local area (Rangamati Sadar, Inside Rangamati) or completely outside Rangamati. Since TechCorner operates a Facebook page, the dataset also includes insights into whether Facebook marketing is effectively reaching potential buyers.

Additionally, the dataset helps in determining: ✔ How many customers are new vs. returning buyers ✔ If customers are followers of the shop’s Facebook page ✔ Whether a customer was recommended by an existing buyer

This dataset is valuable for:

Retail sales analysis to understand product demand fluctuations.

Marketing impact measurement (Facebook engagement vs. actual purchase behavior).

Customer segmentation (local vs. non-local buyers, social media influence, word-of-mouth impact).

Sales trend analysis based on preferred phone models and price ranges.

With a realistic, non-uniform distribution of daily sales and some intentional missing values, this dataset reflects actual retail business conditions rather than artificially smooth AI-generated data.

Marketing & Customer Queries

Does he/she Come from Facebook Page? → Whether the customer came from a Facebook page (Yes/No). Used to analyze Facebook marketing reach.

Does he/she Followed Our Page? → Whether the customer is already a follower of the shop’s Facebook page (Yes/No). Helps measure brand loyalty and organic engagement.

Did he/she buy any mobile before? → Whether the customer is a repeat buyer (Yes/No). Determines the percentage of returning customers.

Did he/she hear of our shop before? → Whether the customer knew about the shop before purchasing (Yes/No). Identifies the impact of referrals or previous marketing efforts.

Was this customer recommended by an old customer? → Whether an existing customer referred them to the shop (Yes/No). Helps evaluate the effectiveness of word-of-mouth marketing.

Acknowledgements

This dataset is derived from real-world mobile sales transactions recorded at TechCorner, a retail shop in Bangladesh. It accurately reflects customer purchasing behavior, pricing trends, and the effectiveness of Facebook marketing in driving sales. Special appreciation to TechCorner for providing comprehensive insights into daily sales patterns, customer demographics, and market dynamics.

This dataset can be used for:

📊 Predictive modeling of sales trends based on customer demographics and marketing channels. 📈 Marketing effectiveness analysis (impact of Facebook promotions vs. organic sales). 🔍 Clustering customers based on purchasing habits (new vs. returning buyers, Facebook users vs. walk-ins). 📌 Understanding demand for different smartphone brands in a local retail market. 🚀 Analyzing how word-of-mouth recommendations influence new customer acquisition.

💡 Can you build a model to predict if a customer is likely to return? 💬 How effective is Facebook in driving actual sales compared to walk-ins? 🔍 Can we cluster customers based on behavior and brand preferences?

In this study, 403 Chinese consumers generalizable to the broader population were surveyed on their motivations to shop for fashion apparel in both high street and e-commerce environments. Statistical analysis was undertaken through multiple T-Tests and MANOVA with the assistance of SPSS and G*Power.

To increase the profits of international brands, this paper presents the motivations of Chinese consumers to engage in fashion retail, building upon established theory in hedonic and utilitarian motivations. With China set to capture over 24% of the $212 billion fashion market, international brands need to understand the unique motivations of Chinese consumers in order to capitalise on the market. However, the motivations of Chinese people to engage in fashion retail are as yet undefined, limiting the ability for international fashion retailers to operate with prosperity in the Chinese market.

Solution Publishing by Allforce: Marketing Professionals

Redefining B2B Marketing and Advertising Outreach

Connect with over 2 million U.S. marketing and advertising contacts across approximately 500,000 companies.

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Our database encompasses professionals in: - Market Research and Analysis - Brand Management - Product Marketing - Content Creation and Management - Social Media and Digital Marketing - Advertising and Public Relations - SEO/SEM and Analytics - CRM and Marketing Operations - Strategic Planning - Creative Design - Email and Influencer Marketing - Partnership Marketing

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All data is maintained for compliance and safe for email marketing

Programmatic Advertising Market Outlook

The programmatic advertising market is poised for significant growth, with its market size estimated to reach USD 150 billion in 2023, and projected to grow at a robust CAGR of approximately 12% to reach USD 360 billion by 2032. This impressive growth is largely driven by the increasing shift towards digital advertising solutions and the adoption of advanced technologies that facilitate automated ad buying. The convenience and efficiency that programmatic advertising offers to marketers in reaching their target audiences are major factors fueling this expansion. The increasing demand for data-driven marketing strategies and personalized ad experiences is also contributing to the market growth.

One of the primary growth factors of the programmatic advertising market is the increasing penetration of the internet and digital devices globally. The proliferation of smartphones and tablets has significantly expanded the reach of digital ads, enabling advertisers to target potential customers more effectively. As more users access the internet through mobile devices, advertisers are leveraging programmatic advertising to capture this growing audience segment. The widespread availability of high-speed internet and the increasing use of social media and online streaming platforms are further accelerating the adoption of programmatic advertising, as these platforms are equipped with the necessary infrastructure to support automated ad placements.

Another crucial growth factor is the rising need for efficient and effective advertising solutions in an increasingly competitive digital landscape. Programmatic advertising offers the ability to automate the ad buying process, which significantly reduces human intervention and minimizes errors. This automation not only improves the efficiency of ad campaigns but also enhances their effectiveness by allowing real-time bidding and targeting capabilities. Additionally, programmatic advertising enables advertisers to access a vast array of data, which can be analyzed to gain insights into consumer behavior and preferences, thereby facilitating more targeted and personalized ad campaigns. This level of precision and scalability in advertising efforts is a key driver of the market's growth.

The integration of artificial intelligence (AI) and machine learning (ML) is also playing a pivotal role in the expansion of the programmatic advertising market. These advanced technologies enhance the ability of advertisers to analyze large datasets and generate actionable insights, which can be used to optimize ad spend and improve campaign performance. AI and ML algorithms enable real-time adjustments to ad placements, ensuring that the most relevant messages are delivered to the right audiences at the right times. As these technologies continue to evolve, they will further enhance the capabilities and outcomes of programmatic advertising, driving greater adoption among businesses seeking to maximize their marketing ROI.

Regionally, North America is currently the largest market for programmatic advertising, owing to the early adoption of digital advertising technologies and the presence of major industry players in the region. The United States, in particular, is at the forefront, with companies investing heavily in programmatic technologies to maintain their competitive edge. Meanwhile, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid digitalization, increasing internet penetration, and the rising popularity of e-commerce platforms. As businesses in Asia Pacific continue to recognize the benefits of programmatic advertising, they are likely to increase their investment in this area, creating significant growth opportunities for market players.

Dynamic Ad Insertion is becoming an increasingly important aspect of programmatic advertising, particularly as advertisers seek to deliver more personalized and contextually relevant content to their audiences. This technology allows advertisers to seamlessly insert ads into digital content in real-time, ensuring that the most appropriate message is delivered to the viewer based on their current context and preferences. By dynamically inserting ads, marketers can enhance the viewer experience, reduce ad fatigue, and increase engagement rates. This approach is particularly effective in environments such as streaming services and podcasts, where content is consumed on-demand and ad relevance is crucial. As the demand for more personalized advertising experiences grow

The app analytics market, valued at $7.29 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 21.09% from 2025 to 2033. This surge is driven by several key factors. The increasing adoption of mobile applications across diverse industries, coupled with the rising need for businesses to understand user behavior and optimize app performance, fuels the demand for sophisticated analytics solutions. Furthermore, advancements in data analytics technologies, including artificial intelligence (AI) and machine learning (ML), are enabling more insightful and actionable data analysis, further propelling market expansion. The diverse application of app analytics across marketing/advertising, revenue generation, and in-app performance monitoring across various sectors like BFSI, e-commerce, media, travel and tourism, and IT and telecom significantly contributes to this growth. The market is segmented by deployment (mobile apps and website/desktop apps) and end-user industry, with mobile app analytics currently dominating due to the widespread adoption of smartphones. The competitive landscape is characterized by a mix of established technology giants like Google and Amazon alongside specialized app analytics providers like AppsFlyer and Mixpanel. These companies are continuously innovating, integrating new technologies, and expanding their product offerings to cater to the evolving needs of businesses. While the North American market currently holds a significant share, the Asia-Pacific region is expected to witness substantial growth in the coming years driven by increasing smartphone penetration and digitalization initiatives. However, factors like data privacy concerns and the rising complexity of integrating various analytics tools could pose challenges to market growth. Nonetheless, the overall outlook for the app analytics market remains positive, indicating substantial opportunities for players across the value chain. Recent developments include: June 2024 - Comscore and Kochava unveiled an innovative performance media measurement solution, providing marketers with enhanced insights. This cutting-edge cross-screen solution empowers marketers to understand better how linear TV ad campaigns impact both online and offline actions. By integrating Comscore’s Exact Commercial Ratings (ECR) data with Kochava’s sophisticated marketing mix modeling, the solution facilitates the measurement of crucial metrics, including mobile app activities (such as installs and in-app purchases) and website interactions., June 2024 - AppsFlyer announced its integration of the Data Collaboration Platform with Start.io, an omnichannel advertising platform that focuses on real-time mobile audiences for publishers. Through this collaboration, businesses leveraging the AppsFlyer Data Collaboration Platform can merge their Start.io data with campaign metrics and audience insights, creating a more comprehensive dataset for precise audience targeting.. Key drivers for this market are: Increasing Usage of Mobile/Web Apps Across Various End-user Industries, Increasing Adoption of Technologies like 5G Technology and Deeper Penetration of Smartphones; Increase in the Amount of Time Spent on Mobile Devices Coupled With the Increasing Focus on Enhancing Customer Experience. Potential restraints include: Increasing Usage of Mobile/Web Apps Across Various End-user Industries, Increasing Adoption of Technologies like 5G Technology and Deeper Penetration of Smartphones; Increase in the Amount of Time Spent on Mobile Devices Coupled With the Increasing Focus on Enhancing Customer Experience. Notable trends are: Media and Entertainment Industry Expected to Capture Significant Share.

Unlock the power of direct engagement with our comprehensive dataset of 34 million verified global phone numbers. This dataset is curated for businesses and data-driven teams looking to enhance customer acquisition, power targeted outreach, enrich CRM records, and fuel B2C growth at scale.

Whether you're running SMS marketing campaigns, telemarketing, building a mobile app user base, or performing identity validation, this dataset offers a scalable, compliant foundation to reach real users worldwide.

🔍 What’s Included: ✅ 34,000,000+ mobile and landline numbers

🌍 Global coverage, including high volumes from the US, UK, Canada, Europe, and emerging markets

🧹 Clean, structured format (CSV/JSON/SQL) for easy integration

📱 Includes carrier, country code, line type, and location data (where available)

🧠 Ideal Use Cases: B2C & D2C marketing campaigns

SMS and voice call outreach

Lead generation & prospecting

Mobile app user acquisition

Identity verification & enrichment

Market analysis and segmentation

Leverage the most reliable and compliant mobile device location/foot traffic dataset on the market. Veraset Movement (Mobile Device GPS / Foot Traffic Data) offers unparalleled insights into footfall traffic patterns across North America.

Covering the United States, Canada and Mexico, Veraset's Mobile Location Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement. Ideal for ad tech, planning, retail analysis, and transportation logistics, Veraset's Movement data helps in shaping strategy and making data-driven decisions.

Veraset’s North American Movement Panel: - United States: 768M Devices, 70B+ Pings - Canada: 55M+ Devices, 9B+ Pings - Mexico: 125M+ Devices, 14B+ Pings - MAU/Devices and Monthly Pings

Uses for Veraset's Mobile Location Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

Company Domain to IP Address Linkage Database | Company IP Data | Monthly Feed | 3M+ Domains to IP Connections

Elevate your business strategy with our premier "Company Domain to IP Address Linkage Database," a meticulously crafted dataset that connects over 3 million company domains directly to their corresponding IP addresses. Updated monthly, this essential resource is designed for organizations seeking to enhance their cybersecurity measures, refine digital marketing strategies, and streamline B2B communications with unparalleled accuracy and efficiency.

Optional Add-On: MAID to Business Domains Dataset Enhance your dataset with our optional add-on, the "MAID to Business Domains Dataset." This valuable extension provides a linkage between Mobile Advertising IDs (MAIDs) and company domains, offering a deeper layer of insight for targeted marketing and precise audience engagement strategies. Ideal for businesses looking to leverage mobile data for enhanced targeting and analytics.

Key Features:

Comprehensive Coverage: Gain access to a vast database of over 3 million company domains linked to their respective IP addresses, covering a wide spectrum of industries and global regions. Premium Data Quality: Enjoy the highest standards of data integrity with our dataset, meticulously maintained and regularly refreshed to ensure the most accurate and up-to-date information. Seamless Integration: Effortlessly integrate our database into your existing tech stack, enhancing your cybersecurity, CRM, and marketing automation tools with rich, actionable data.

Benefits:

Enhanced Digital Marketing: Use precise domain-to-IP mappings to refine your digital marketing efforts, ensuring content and ads reach the right corporate audiences, significantly boosting B2B engagement. Robust Cybersecurity: Strengthen your cybersecurity framework by utilizing detailed domain and IP information for improved network monitoring and threat detection. Strategic Insights: Draw on our extensive dataset for market analysis, competitor tracking, and customer behavior insights, informing strategic decisions across your organization. Efficient B2B Communication: Identify and engage corporate networks more effectively, optimizing B2B outreach and operational efficiency. Applications:

Targeted B2B Engagement: Equip your marketing and sales teams with detailed data, allowing for highly targeted content delivery and advertising to specific corporate networks. Advanced Security Measures: Leverage the dataset for comprehensive network monitoring and access management, enhancing your cybersecurity posture. Informed Strategic Planning: Utilize deep insights from domain and IP linkages for market analysis and business intelligence, driving forward-looking strategies. Operational Excellence: Streamline the identification and engagement process with corporate networks, improving efficiency and response rates in B2B communication. The "Company Domain to IP Address Linkage Database" is a foundational tool for any business aiming to excel in the digital age, offering critical data for improving digital marketing, cybersecurity, and strategic planning. Available exclusively on Datarade.ai, this database stands at the forefront of digital innovation.

Consider enhancing your strategic toolkit further with our "MAID to Business Domains Dataset" add-on, a powerful resource for businesses focused on maximizing the impact of mobile marketing and engagement.

This synthetic but realistic dataset contains 90+ customer reviews for 6 smartphone models (from Apple, Samsung, and Google), along with: - Product specifications (Price, Screen Size, Battery, Camera, RAM, Storage, 5G, Water Resistance) - Customer reviews (Star Ratings, Review Text, Verified Purchase Status) - Sales data (Units Sold per Model)

Potential Use Cases: ✅ Feature importance analysis (Which specs drive ratings?) ✅ Sentiment analysis (NLP on reviews) ✅ Pricing strategy optimization ✅ Market research (Comparing Apple vs. Samsung vs. Google)

Smartphone Customer Satisfaction Survey

Objective: Understand how product features influence purchasing decisions and satisfaction.

Section 1: Demographic & Purchase Behavior

1. Which smartphone brand did you purchase?

   • ☐ Apple
   • ☐ Samsung
   • ☐ Google
   • Maps to brand column.

2. Which model did you purchase?

   • Apple: ☐ iPhone 14 | ☐ iPhone 15
   • Samsung: ☐ Galaxy S22 | ☐ Galaxy S23
   • Google: ☐ Pixel 7 | ☐ Pixel 8
   • Maps to model_name column.

3. Where did you purchase the phone?

   • ☐ Online (e.g., Amazon, Brand Website)
   • ☐ Physical Store
   • Justifies verified_purchase (assumed online = verified).

Section 2: Product Feature Ratings

1. How would you rate the following features? (1 = Poor, 5 = Excellent)

   • Battery Life: ⭐⭐⭐⭐⭐
   • Camera Quality: ⭐⭐⭐⭐⭐
   • Screen Size: ⭐⭐⭐⭐⭐
   • Performance (RAM/Processor): ⭐⭐⭐⭐⭐
   • Aggregates into star_rating (average of these).

2. Which feature is MOST important to you?

   • ☐ Battery Life
   • ☐ Camera Quality
   • ☐ Screen Size
   • ☐ Performance
   • ☐ Price
   • Explains review_text keywords (e.g., "battery" mentions).

Section 3: Price & Satisfaction

1. How do you feel about the price of your phone?

   • ☐ Very Affordable
   • ☐ Fairly Priced
   • ☐ Slightly Expensive
   • ☐ Too Expensive
   • Maps to price vs. star_rating correlation.

2. Would you recommend this phone to others?

   • ☐ Definitely Yes
   • ☐ Probably Yes
   • ☐ Neutral
   • ☐ Probably No
   • ☐ Definitely No
   • Linked to star_rating (5 = Definitely Yes).

Column Details (Metadata)

Column Name (Type) Description "Example"**

model_id (Integer) Unique ID for each phone model 1 (iPhone 14)

brand (String) Manufacturer (Apple, Samsung, Google) "Apple"

model_name (String) Name of the phone model "iPhone 15"

price (Integer) Price in USD 999

screen_size (Float) Screen size in inches 6.1

battery (Integer) Battery capacity in mAh 4000

camera_main (String) Main camera resolution (MP) "48MP"

ram (Integer) RAM in GB 8

storage (Integer) Storage in GB 128

has_5g (Boolean) Whether the phone supports 5G TRUE

water_resistant (String) Water resistance rating (IP68 or None) "IP68"

units_sold (Integer) Estimated units sold (for market analysis) 15000

review_id (Integer) Unique ID for each review 1

user_name (String) Randomly generated reviewer name "John"

star_rating (Integer) Rating from 1 (worst) to 5 (best) 5

verified_purchase (Boolean) Whether the reviewer bought the product TRUE

review_date (Date) Date of the review (YYYY-MM-DD) "2023-05-10"

review_text (String) Simulated review text based on features & rating "The 48MP camera is amazing!"

Suggested Analysis Ideas to inspire data analysis: A. Feature Impact on Ratings Regression: star_rating ~ battery + camera_main + price Key drivers: Does battery life affect ratings more than camera quality?

B. Sentiment Analysis (NLP) Use tidytext (R) or NLTK (Python) to extract most-loved/hated features. Example: r library(tidytext) reviews_tidy <- final_data %>% unnest_tokens(word, review_text)
reviews_tidy %>% count(word, sort = TRUE) %>% filter(n > 5)

C. Brand Comparison Apple vs. Samsung vs. Google: Which brand has higher average ratings? Price sensitivity: Do cheaper phones (e.g., Pixel) get better value ratings?

D. Sales vs. Features Correlation: units_sold ~ price + brand Premium segment analysis: Do iPhones sell more despite higher prices?

The Canadian contribution and data set prepared as part of the Global Media and Internet Concentration (GMIC) project offers an independent academic, empirical and data-driven analysis of a deceptively simple yet profoundly important question: have telecom, media and internet markets become more concentrated over time, or less? Media Ownership and Concentration is presented from more than a dozen sectors of the telecom-media-internet industries, including film, music and book industries. Note: The Master GMICP Workbook was revised to remove instances where App Store and advertising revenue for Advertising-based Video-on-Demand (AVOD) services delivered over the Internet had been double counted in the original data set released through Dataverse. The text of the report was also revised to reflect these corrections.

Mobility data is collected through location-aware mobile apps using an SDK-based implementation. Users explicitly consent to share their location data via a clear opt-in process and are provided with clear opt-out options. Factori ingests, cleans, validates, and exports all location data signals to ensure the highest quality data is available for analysis.

• Record Count: 90 Billion
• Capturing Frequency: Once per Event
• Delivering Frequency: Once per Day
• Updated: Daily

Mobility Data Reach

Our data reach encompasses the total counts available across various categories, including attributes such as country location, MAU (Monthly Active Users), DAU (Daily Active Users), and Monthly Location Pings.

Data Export Methodology

We collect data dynamically, offering the most updated data and insights at the best-suited intervals (daily, weekly, monthly, or quarterly).

Business Needs

Our data supports various business needs, including consumer insight, market intelligence, advertising, and retail analytics.

Company Domain to MAID | Company IP Data | Monthly Feed | 200M+ MAIDs to Business Domain Connections Introducing our "Company Domain to MAID" dataset, a groundbreaking resource that links over 200 million Mobile Advertising IDs (MAIDs) to corresponding business domains. Updated monthly, this expansive dataset is engineered for organizations aiming to revolutionize their mobile marketing strategies, enhance customer engagement, and gain deeper insights into mobile user behaviors. As a perfect complement to our "Company Domain to IP Address Linkage Database," this product extends the value of your digital marketing and cybersecurity efforts by integrating mobile data for comprehensive B2B insights. Company IP Data Company Data Ideal Add-On for Domain to IP Product This dataset serves as an ideal add-on to our "Company Domain to IP Address Linkage Database," enabling a multi-dimensional approach to digital strategy by encompassing both traditional and mobile digital landscapes. Together, these products offer a holistic view of digital footprints, ensuring your marketing, security, and analytical capabilities are both broad and deeply integrated. Key Features: • Massive Dataset: Access a robust linkage of over 200 million MAIDs to business domains, providing unparalleled coverage across various industries and markets. • High-Quality Data: Benefit from a dataset characterized by its high accuracy and monthly updates, ensuring you have the most current and reliable information for your mobile marketing and engagement strategies. • Seamless Compatibility: Designed for easy integration with existing marketing, CRM, and cybersecurity platforms, enhancing your digital outreach and security protocols with valuable mobile user insights. Benefits: • Advanced Mobile Marketing: Leverage precise MAID to domain mappings to target and engage mobile users more effectively, driving higher engagement rates and improving campaign ROI. • Enhanced Customer Insights: Gain deeper understanding of customer mobile behaviors and preferences, enabling more personalized and impactful marketing strategies. • Comprehensive Digital Footprint: Combine with our Domain to IP product for a complete overview of corporate digital presence, from desktop to mobile, enhancing all aspects of digital marketing and cybersecurity. • Improved Data-Driven Decisions: Utilize the extensive insights provided by linking MAIDs to business domains to inform strategic decisions, from marketing to security to product development. Applications: • Holistic Marketing Strategies: Employ our dataset to craft comprehensive digital marketing campaigns that effectively reach business audiences across all devices, maximizing coverage and impact. • Enhanced B2B Targeting: Perfectly complement your Domain to IP strategies by including mobile targeting, ensuring that your messages reach the right audience, no matter the device. • Robust Cybersecurity Posture: Enhance your cybersecurity measures by incorporating mobile data, providing a more complete picture of potential vulnerabilities and threat vectors. • Market and Competitor Analysis: Analyze mobile engagement trends and behaviors for insights into market dynamics and competitor strategies, guiding your business decisions with rich, actionable data. Our "Company Domain to MAID" dataset is a must-have for businesses looking to capitalize on the immense potential of mobile marketing and engagement, offering a significant advantage in understanding and reaching B2B audiences. As a standalone product or in conjunction with our "Company Domain to IP Address Linkage Database," it represents the pinnacle of digital insight, enabling businesses to navigate the complexities of the modern digital landscape with confidence and precision.

Leverage the most reliable and compliant global mobility and foot traffic dataset on the market. Veraset Movement (Mobile Device GPS Mobility Data) offers unparalleled real-time insights into footfall traffic patterns globally.

Covering 200+ countries, Veraset's Mobile Location Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement.

Ideal for ad tech, planning, retail analysis, and transportation logistics, Veraset's mobile location data helps in shaping strategy and making data-driven decisions.

Veraset Global Movement panel (mobile location) includes: - 1.8+ Billion Devices Monthly - 200 Billion Pings Monthly Device and Ping counts by Country are available upon request

Common Use Cases of Veraset's Mobile Location Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

Please visit: https://www.veraset.com/docs/movement for more information and schemas

Access 561M records of verified, geo-coded consumer behavior data across 6 key emerging markets. GeoLifestyle enables detailed audience segmentation, predictive analytics, and behavior-driven marketing, with full on-premise security and regulatory compliance.

GeoLifestyle Consumer Behavior Data: Scalable Behavioral and Audience Intelligence Across 6 Emerging Markets

GeoLifestyle provides companies with instant access to 561 million geo-coded consumer behavior profiles across six of the world’s most dynamic emerging economies. Designed for organizations focused on behavioral analytics, audience modeling, market research, and precision-targeted marketing, this dataset offers high-value, real-world intelligence ready for direct integration into AI, marketing, and research workflows.

Key Features: • Volume: 561,000,000 records • Countries Covered: 6 key emerging markets • Historical Span: 12 months of behavior and location history • Attributes: 78 lifestyle and behavioral attributes per profile • Location Precision: Latitude/Longitude at 2-meter radius • Data Delivery: On-premise, quarterly updates, fully compliant

What’s Inside: Each record includes rich lifestyle indicators across major categories — such as shopping preferences, travel behaviors, media consumption, household composition, mobility patterns, income estimation, and affluence segmentation — combined with precise geolocation signals. This allows for granular, real-world audience understanding at scale.

Primary Use Cases: • Behavioral and predictive analytics • Location-based audience segmentation • AI-driven audience modeling • Data-driven market research • Personalization and hyper-targeted marketing strategies

Ideal For: • Marketing technology firms • Financial institutions • AI/ML teams building customer models • Retailers expanding into emerging markets • Agencies designing precision marketing campaigns

Data Quality and Compliance: GeoLifestyle is stored securely on-premise with regulatory alignment to GDPR, LGPD, PDPA, and similar frameworks. With continuous quarterly refresh cycles, buyers are assured of current, compliant, and operationally ready datasets.

Pricing and additional samples available upon request.

Leverage the most reliable and compliant mobile device location/foot traffic dataset on the market!

Veraset Movement (GPS Mobility Data) offers unparalleled insights into foot traffic patterns for dozens of countries across the Middle East.

Covering 14+ countries for the Middle East alone, Veraset's foot traffic Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement. Ideal for ad tech, planning, retail, and transportation logistics, Veraset's Movement data (footfall) helps shape strategy and make impactful data-driven decisions.

Veraset’s Africa Footfall Panel includes the following countries: - bahrain-BH - iran-IR - iraq-IQ - israel-IL - jordan-JO - kuwait-KW - lebanon-LB - oman-OM - palestinian territories-PS - qatar-QA - saudi arabia-SA - syria-SY - united arab emirates-AE - yemen-YE

Common Use Cases of Veraset's Foot Traffic Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

Online conversion rates of e-commerce sites were the highest in the food & beverage sector, at 3.1 percent in the fourth quarter of 2024. Beauty & skincare followed, with a three percent conversion rate. For comparison, the average conversion rate of e-commerce sites across all selected sectors stood at just over two percent. How does conversion vary by region and device? The conversion rate, which indicates the proportion of visits to e-commerce websites that result in purchases, varies by country and region. For instance, since at least 2023, e-commerce sites have consistently recorded higher conversion rates among shoppers in Great Britain compared to those in the United States and other global regions. Furthermore, despite the increasing prevalence of mobile shopping worldwide, conversions remain more pronounced on larger screens such as tablets and desktops. Online shopping cart abandonment on the rise Recently, the rate at which consumers abandon their online shopping carts has been gradually rising to more than 70 percent in 2024, showing a higher difficulty for e-commerce sites to convert website traffic into purchases. By the end of that year, food and beverage was one of the product categories with the lowest online cart abandonment rate, confirming the sector’s relatively high conversion rate. In the United States, the primary reason why customers abandoned their shopping carts is that extra costs such as shipping, tax, and service fees were too high at checkout.

During the fourth quarter of 2024, 3.1 percent of visits to e-commerce websites in the UK converted to purchases. In Switzerland, online shopper conversion rates stood at 2.9 percent. Mobile takes over e-shopping Online shopping has long since established itself as an everyday activity of online life – this holds for both desktop and mobile. As of the third quarter of 2024, more than three-quarters of retail site visits worldwide came from smartphones and generated about two-thirds of online shopping orders. Based on mobile retail performance growth, it is only a matter of time before mobile overtakes desktop in revenue generation.

In the fourth quarter of 2024, online shoppers spent an average of about 2.98 U.S. dollars per visit across all verticals. Electronics is the category in which consumers spent the most money per visit on average, at 3.32 U.S. dollars, followed by luxury apparel at 3.10 dollars. Nickels and dimes Over the past few years, the average order value for e-commerce purchases has increased globally, from around 118 U.S. dollars in September 2022 to around 126 U.S. dollars in the same month of 2023. The average order value also depends heavily on the online traffic source consumers use. In 2023, the value per order value was the highest when navigating directly, averaging around 167 dollars. Direct navigation means searching for a website directly in the browser's address bar, bypassing the use of search engines. Orders placed from social media stores were the lowest in value, with an average of less than 110 dollars. Mobile shopping on the rise Online shoppers have clear preferences when it comes to device type. When comparing gadgets, the average purchase amount has always been the highest for desktops, with an order value of 160 U.S. dollars. This indicates that bigger purchases are made via desktop computers. However, consumers are more likely to complete orders when shopping on mobile devices. Mobile devices were also clearly preferred when browsing retail websites, with around three-fourths of consumers using smartphones instead of desktops or tablets.