AI in Digital Marketing Market Outlook

According to our latest research, the AI in Digital Marketing market size stood at USD 19.8 billion in 2024, reflecting robust adoption across industries. The market is experiencing a strong growth trajectory, with a projected CAGR of 28.6% from 2025 to 2033. By the end of 2033, the global market is forecasted to reach USD 181.2 billion. This rapid expansion is primarily fueled by the increasing demand for personalized marketing, data-driven decision-making, and automation in digital marketing strategies.

One of the primary growth factors driving the AI in Digital Marketing market is the escalating need for hyper-personalized customer experiences. Brands are leveraging AI-powered tools to analyze consumer behavior, segment audiences, and deliver tailored content in real time. This shift towards customer-centric marketing has significantly improved engagement rates and conversion metrics for businesses. Furthermore, AI algorithms can process vast datasets far more efficiently than traditional analytics, enabling marketers to identify trends, predict customer preferences, and optimize campaigns dynamically. As competition in the digital space intensifies, the ability to deliver relevant and timely content has become a crucial differentiator, prompting organizations to invest heavily in AI technologies.

Another significant factor contributing to market growth is the integration of AI in automation and workflow optimization. AI-driven platforms streamline repetitive marketing tasks such as email scheduling, social media posting, and ad bidding, freeing up valuable human resources for strategic initiatives. The adoption of AI-powered chatbots and virtual assistants has also revolutionized online customer support, offering instant, accurate, and personalized responses to customer queries. This not only enhances user satisfaction but also reduces operational costs for businesses. Additionally, the proliferation of digital channels and the explosion of online data have made manual campaign management increasingly untenable, further accelerating the adoption of AI solutions in digital marketing.

The rapid evolution of AI technologies, including machine learning, natural language processing, and computer vision, is fostering innovation across digital marketing applications. These advancements are enabling marketers to harness predictive analytics, automate content creation, and optimize ad targeting with unprecedented precision. The growing ecosystem of AI-enabled marketing tools is also driving collaboration between technology providers, marketing agencies, and end-users, leading to the development of more sophisticated and integrated solutions. As regulatory frameworks around data privacy and ethical AI mature, organizations are increasingly confident in deploying AI at scale, further propelling market growth.

Regionally, North America remains the dominant market for AI in Digital Marketing, owing to its advanced digital infrastructure, high adoption rates of emerging technologies, and strong presence of leading AI vendors. However, Asia Pacific is rapidly catching up, driven by the digital transformation of businesses in China, India, and Southeast Asia. Europe is also witnessing significant growth, supported by increasing investments in AI research and a strong focus on data privacy. Latin America and the Middle East & Africa are emerging markets with notable potential, as businesses in these regions embrace digitalization to expand their global reach. The overall regional landscape is characterized by diverse adoption rates, regulatory environments, and market maturity levels, shaping the growth trajectory of the global market.

Component Analysis

The Component segment of the AI in Digital Marketing market is bifurcated into software and services, with each playing a pivotal role in enabling digital transformation for marketers. Software solutions dominate the market, accounting for a substantial share due to their ability to automate, analyze, and optimize marketing processes at scale. These solutions encompass a wide range of applications, from AI-powered analytics platforms and content generators to campaign management tools and customer data platforms. The continual enhancements in AI algorithms and user interfaces are making these software offerings more accessible and efficient, driving widespread adoption among both large enterprises and SMEs.

Smart Advertising Market Outlook

According to our latest research, the global Smart Advertising market size in 2024 is valued at USD 32.7 billion, with a robust CAGR of 18.3% anticipated from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 144.1 billion, driven by the rapid adoption of automation, artificial intelligence, and data-driven strategies across digital marketing ecosystems. The surge in demand for personalized and measurable advertising solutions, coupled with technological advancements in programmatic platforms and real-time analytics, is fundamentally reshaping how brands engage consumers worldwide.

The primary growth factor propelling the Smart Advertising market is the increasing reliance on data-driven decision-making by advertisers. With the advent of big data and advanced analytics, brands are now able to target their audiences with unprecedented precision. The integration of artificial intelligence and machine learning algorithms into advertising platforms has enabled the automation of ad placements, real-time bidding, and optimization of campaigns, resulting in significantly higher ROI for marketers. These technologies facilitate hyper-personalized content delivery, which increases user engagement and conversion rates, further fueling market expansion. Additionally, the proliferation of connected devices and the exponential growth in digital content consumption are generating vast datasets, which, when leveraged effectively, enable marketers to refine their targeting strategies and maximize advertising efficiency.

Another significant driver for the Smart Advertising market is the rapid evolution of digital media platforms and the diversification of advertising channels. The shift from traditional media to digital, mobile, and connected TV platforms has opened new avenues for advertisers to reach their target audiences. The rise of programmatic advertising and real-time bidding has revolutionized the way digital ad inventory is bought and sold, offering greater transparency, efficiency, and scalability. Marketers are increasingly investing in data management platforms (DMPs) to consolidate and analyze consumer data from multiple sources, which empowers them to deliver more relevant and timely ads. Furthermore, the integration of smart advertising solutions with social media, search engines, and e-commerce platforms is driving the adoption of automated ad technologies across various industry verticals.

The growing emphasis on regulatory compliance and data privacy is also shaping the Smart Advertising market landscape. As governments worldwide implement stricter data protection regulations such as GDPR and CCPA, advertisers are compelled to adopt transparent and ethical data collection practices. This has led to the development of privacy-centric ad technologies that prioritize user consent and data security, thereby enhancing consumer trust in digital advertising. The demand for privacy-compliant solutions is prompting vendors to innovate and offer advanced features such as contextual targeting and consent management, which ensure regulatory adherence while maintaining advertising effectiveness. This trend is expected to continue, further influencing market dynamics and driving the adoption of smart advertising platforms globally.

From a regional perspective, North America currently leads the Smart Advertising market, accounting for the largest revenue share in 2024. The region's dominance is attributed to the high penetration of digital technologies, robust infrastructure, and the presence of major industry players. Europe follows closely, driven by stringent data privacy regulations and increasing investments in digital transformation initiatives. The Asia Pacific region is emerging as a lucrative market, fueled by rapid urbanization, expanding internet penetration, and the growing adoption of mobile devices. Latin America and the Middle East & Africa are also witnessing steady growth, supported by the digitalization of businesses and rising consumer demand for personalized experiences. The global Smart Advertising market is thus characterized by dynamic regional trends and diverse growth opportunities across mature and emerging economies.

Marketing Copilot Market Outlook

According to our latest research, the global Marketing Copilot market size reached USD 2.8 billion in 2024, reflecting robust adoption across industries. The market is projected to grow at a CAGR of 27.1% from 2025 to 2033, reaching an estimated USD 22.1 billion by 2033. This impressive growth trajectory is primarily driven by the increasing demand for AI-powered marketing automation and personalized customer engagement solutions worldwide.

One of the most significant growth factors propelling the Marketing Copilot market is the rapid advancement and integration of artificial intelligence and machine learning technologies into marketing platforms. Organizations are leveraging these advanced technologies to automate campaign management, optimize customer segmentation, and enhance lead generation processes. The ability of Marketing Copilot solutions to analyze vast data sets in real-time and provide actionable insights is transforming traditional marketing strategies, enabling businesses to deliver more targeted and effective campaigns. This shift towards data-driven marketing is further amplified by the growing emphasis on customer experience, which has become a key differentiator in highly competitive markets.

Another critical driver for the Marketing Copilot market is the increasing adoption of cloud-based solutions, which offer scalability, flexibility, and cost-effectiveness. Cloud deployment allows businesses of all sizes, especially small and medium enterprises (SMEs), to access sophisticated marketing tools without the need for significant upfront investments in infrastructure. This democratization of technology is fueling widespread adoption across various industry verticals, including retail and e-commerce, BFSI, healthcare, and IT and telecommunications. The shift to cloud-based Marketing Copilot platforms also facilitates seamless integration with existing business systems, ensuring a unified approach to marketing automation and customer relationship management.

Additionally, the growing demand for personalized content and real-time customer engagement is accelerating the adoption of Marketing Copilot solutions. Modern consumers expect brands to deliver relevant and timely communications across multiple channels, and Marketing Copilot platforms enable organizations to meet these expectations efficiently. By leveraging advanced analytics and reporting capabilities, businesses can continuously refine their marketing strategies and maximize return on investment. Furthermore, the proliferation of digital channels and the increasing complexity of customer journeys are compelling organizations to invest in intelligent marketing automation tools to maintain a competitive edge.

From a regional perspective, North America currently dominates the Marketing Copilot market, accounting for the largest revenue share in 2024. The region's leadership is attributed to the presence of leading technology providers, high digital adoption rates, and significant investments in AI-driven marketing solutions. Europe and Asia Pacific are also witnessing rapid growth, driven by increasing digital transformation initiatives and the rising popularity of cloud-based marketing platforms. Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by expanding internet penetration and growing awareness of the benefits of marketing automation.

Component Analysis

The Marketing Copilot market is segmented by component into Software and Services, both of which play critical roles in the overall ecosystem. The software segment encompasses a wide range of solutions, from AI-powered campaign management platforms to advanced analytics engines. These software solutions are designed to automate repetitive marketing tasks, streamline workflow processes, and deliver actionable insights for better decision-making. The increasing sophistication of Marketing Copilot software, including features such as natural language processing, predictive analytics, and real-time personalization, is driving strong demand across industries. Businesses are increasingly investing in comprehensive software suites that offer end-to-end marketing automation capabilities, enabling them to stay ahead in an ever-evolving digital landscape.

The services segment, which includes implementation, consulting, training, and support services, is equally vital to the suc

In business-to-business (B2B) companies, marketing and sales teams face significant challenges in identifying, qualifying, and prioritizing a large number of leads. Lead prioritization is a critical task for B2B organizations because it allows them to allocate resources more effectively, focus their sales force on the most viable and valuable opportunities, optimize their time spent qualifying leads, and maximize their B2B digital marketing strategies. This article addresses the topic by presenting a case study of a B2B software company's development of a lead scoring model based on data analytics and machine learning under the consumer theory approach. The model was developed using real lead data generated between January 2020 and April 2024, extracted from the company's CRM, which were analyzed and evaluated by fifteen classification algorithms, where the results in terms of accuracy and ROC AUC showed a superior performance of the Gradient Boosting Classifier over the other classifiers. At the same time, the feature importance analysis allowed the identification of features such as “source” and “lead status,” which increased the accuracy of the conversion prediction. The developed model significantly improved the company's ability to identify high quality leads compared to the traditional methods used. This research confirms and complements existing theories related to understanding the application of consumer behavior theory and the application of machine learning in the development of B2B lead scoring models. This study also contributes to bridging the gap between marketers and data scientists in jointly understanding lead scoring as a critical activity because of its impact on overall marketing strategy performance and sales revenue performance in B2B organizations.

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OPTIMIZED FOR:

• Real-time digital onboarding with verified, high-coverage identity data

• KYC/AML automation aligned with LGPD, PDPA, GDPR, and regional frameworks

• Cross-border credit risk modeling and thin-file scoring in underserved markets

• Synthetic fraud detection and account takeover prevention using telecom-verified identity resolution

• AI training datasets for segmentation, risk scoring, and fraud analytics

• Inclusive identity verification for superapps, payments, and remittance ecosystems Living Identity™ transforms identity from a barrier into an enabler—delivering trust, precision, and regulatory-grade intelligence to the organizations shaping the future of digital economies.

Programmatic Advertising Display Market Outlook

The global programmatic advertising display market size was valued at approximately USD 150 billion in 2023 and is projected to reach around USD 300 billion by 2032, growing at a compound annual growth rate (CAGR) of 8% during the forecast period. The growth of this market is largely driven by the increasing demand for efficient and targeted advertising solutions that leverage data to optimize marketing strategies. The shift towards digitalization and the growing penetration of smart devices globally are also significant contributors to this growth. Programmatic advertising displays have revolutionized the way marketers interact with potential customers, offering a more streamlined and data-driven approach compared to traditional advertising methods.

One of the primary growth factors for the programmatic advertising display market is the surge in internet usage and the proliferation of digital content consumption. As more users spend time online across various digital platforms, advertisers have recognized the potential of programmatic advertising to reach specific audiences effectively. The ability to collect and analyze large data sets in real-time allows advertisers to target specific demographics with precision, leading to higher engagement and conversion rates. The rise of social media and digital streaming platforms has further expanded the reach of programmatic advertising, making it an essential component of any comprehensive digital marketing strategy.

Furthermore, advancements in artificial intelligence and machine learning technologies have significantly enhanced the capabilities of programmatic advertising. These technologies enable advertisers to harness predictive analytics to optimize their campaigns, allowing for better decision-making and improved return on investment (ROI). AI-driven algorithms can analyze consumer behavior patterns and preferences, enabling more personalized and relevant ad placements. This level of customization not only improves the user experience but also enhances the effectiveness of advertising campaigns, making programmatic advertising an increasingly attractive option for businesses of all sizes.

The integration of programmatic advertising into mobile and app-based platforms is another critical factor driving market growth. As mobile devices become the primary means of accessing the internet for a significant portion of the global population, advertisers are focusing on optimizing their strategies for mobile platforms. Programmatic advertising allows for seamless ad placements across various mobile apps and websites, ensuring that users are targeted with relevant content irrespective of their device. This adaptability, coupled with the growing adoption of mobile payment systems, further solidifies the role of programmatic advertising in the modern digital landscape.

DSP Advertising Demand Side Platform plays a pivotal role in the programmatic advertising ecosystem, acting as an intermediary that enables advertisers to purchase digital ad inventory across various platforms. By utilizing a DSP, advertisers can efficiently manage and optimize their ad campaigns through a single interface, leveraging real-time bidding and data analytics to reach their target audience effectively. The use of DSPs allows for greater control over ad placements, budget allocation, and audience targeting, making it an indispensable tool for marketers aiming to maximize their advertising impact. As the demand for more sophisticated and data-driven advertising solutions grows, DSPs continue to evolve, offering advanced features such as AI-driven insights and cross-channel campaign management to enhance the overall effectiveness of programmatic advertising strategies.

From a regional perspective, North America currently holds a dominant position in the programmatic advertising display market, driven by the high adoption of digital technologies and advanced marketing strategies. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, primarily due to the expanding internet user base and increasing investments in digital infrastructure. Countries like China and India are emerging as key markets for programmatic advertising, as businesses increasingly recognize the potential to tap into the region's burgeoning digital audience. Europe also presents significant opportunities for growth, with a strong emphasis on data privacy and consumer protection shaping the regulatory landscape.

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Bellabeat, a high-tech company that manufactures health-focused smart products, founded by Urška Sršen and Sando Mur. Sršen used her background as an artist to develop beautifully designed technology that informs and inspires women around the world. Collecting data on activity, sleep, stress, and reproductive health has allowed Bellabeat to empower women with knowledge about their own health and habits. Since it was founded in 2013, Bellabeat has grown rapidly and quickly positioned itself as a tech-driven wellness company for women. By 2016, Bellabeat had opened offices around the world and launched multiple products. Bellabeat products became available through a growing number of online retailers in addition to their own e-commerce channel on their website. The company has invested in traditional advertising media, such as radio, out-of-home billboards, print, and television, but focuses on digital marketing extensively. Bellabeat invests year-round in Google Search, maintaining active Facebook and Instagram pages, and consistently engages consumers on Twitter. Additionally, Bellabeat runs video ads on Youtube and display ads on the Google Display Network to support campaigns around key marketing dates. Sršen knows that an analysis of Bellabeat’s available consumer data would reveal more opportunities for growth. She has asked the marketing analytics team to focus on a Bellabeat product and analyze smart device usage data in order to gain insight into how people are already using their smart devices. Then, using this information, she would like high-level recommendations for how these trends can inform Bellabeat marketing strategy.

(FitBit Fitness Tracker Data (CC0: Public Domain, dataset made available through Mobius): This Kaggle data set contains personal fitness tracker from thirty fitbit users. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. It includes information about daily activity, steps, and heart rate that can be used to explore users’ habits.)

Account-Based AI Market Outlook

According to our latest research, the global Account-Based AI market size is valued at USD 2.8 billion in 2024, demonstrating robust expansion driven by the accelerating adoption of artificial intelligence in account-based strategies across enterprises. The market is projected to grow at a CAGR of 19.2% from 2025 to 2033, reaching an estimated USD 13.3 billion by 2033. This remarkable growth is attributed to the increasing demand for hyper-personalized marketing, advanced analytics, and automation in sales and customer engagement processes, as organizations seek to optimize their account-based initiatives and drive higher ROI through AI-powered solutions.

One of the primary growth factors propelling the Account-Based AI market is the rising need for businesses to deliver highly targeted and personalized experiences to key accounts. Traditional marketing and sales approaches are often inefficient in todayÂ’s competitive landscape, where customers expect tailored content and seamless interactions. Account-Based AI leverages machine learning, natural language processing, and predictive analytics to analyze massive datasets, identify intent signals, and automate personalized outreach at scale. This enables organizations to prioritize high-value accounts, streamline lead generation, and nurture prospects with content and messaging that resonates, resulting in improved conversion rates and deeper customer relationships. The growing emphasis on customer-centricity and data-driven decision-making is thus fueling the adoption of account-based AI solutions across industries.

Another significant driver is the rapid digital transformation occurring across sectors such as BFSI, IT and telecommunications, healthcare, retail, and manufacturing. Enterprises are increasingly investing in advanced AI-powered platforms to enhance their sales and marketing effectiveness, optimize resource allocation, and increase operational efficiency. The integration of account-based AI with CRM, marketing automation, and analytics tools allows organizations to gain real-time insights into account behaviors, forecast sales opportunities, and proactively address customer needs. Furthermore, the proliferation of cloud-based deployment models has made these sophisticated solutions more accessible and scalable, enabling even small and medium enterprises (SMEs) to leverage account-based AI for competitive advantage. This democratization of AI technologies is a key factor underpinning the sustained growth of the market.

Additionally, the proliferation of digital channels and the explosion of customer data have created both opportunities and complexities for organizations. Navigating this data-rich environment requires advanced AI algorithms capable of extracting actionable insights from diverse sources, such as social media, web activity, and transactional records. Account-Based AI platforms are uniquely positioned to unify and analyze these datasets, empowering sales and marketing teams with a comprehensive view of each accountÂ’s journey and intent. As privacy regulations become more stringent and data quality becomes paramount, AI-driven account-based solutions offer a way to ensure compliance while delivering measurable business outcomes. This convergence of regulatory, technological, and business imperatives is accelerating the adoption of account-based AI on a global scale.

In the realm of account-based strategies, Lead-to-Account Matching Software plays a pivotal role in enhancing the precision and effectiveness of AI-driven initiatives. This software is designed to accurately match leads to existing accounts, ensuring that sales and marketing efforts are directed towards the right targets. By integrating seamlessly with CRM systems and leveraging advanced algorithms, Lead-to-Account Matching Software helps organizations maintain clean and organized databases, reducing redundancy and improving data accuracy. This, in turn, enables more personalized and relevant engagement with potential customers, ultimately boosting conversion rates and customer satisfaction. As businesses strive to optimize their account-based strategies, the adoption of such software is becoming increasingly essential, providing a competitive edge in a data-driven market.

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Web Analytics Platform Market Outlook

According to our latest research, the global web analytics platform market size in 2024 is valued at USD 7.21 billion, with robust growth trends driven by the increasing digitalization of businesses and the need for data-driven decision-making. The market is exhibiting a promising CAGR of 15.8% from 2025 to 2033, and is expected to reach USD 26.62 billion by 2033. The primary growth factor fueling this expansion is the escalating demand for actionable insights from online user behavior, which is critical for optimizing marketing strategies and enhancing customer experiences.

One of the key growth drivers for the web analytics platform market is the exponential increase in internet penetration and digital transformation across industries. As organizations shift their operations online, the volume of data generated from web interactions has surged, necessitating advanced analytics solutions to derive meaningful insights. Businesses are leveraging web analytics to monitor website performance, track user engagement, and personalize customer journeys, which in turn boosts conversion rates and revenue generation. The proliferation of e-commerce platforms and the growing importance of omnichannel marketing have further accelerated the adoption of sophisticated analytics tools, enabling enterprises to stay competitive in an increasingly digital ecosystem.

Another significant factor propelling market growth is the integration of artificial intelligence (AI) and machine learning (ML) technologies into web analytics platforms. These advancements empower organizations to automate data collection, enhance predictive analytics, and uncover deep behavioral patterns that were previously inaccessible through traditional analytics methods. The ability to process vast datasets in real-time and generate actionable insights has transformed how businesses approach digital marketing and customer engagement. Moreover, AI-powered analytics platforms are increasingly being used for targeting and behavioral analysis, multichannel campaign optimization, and real-time decision-making, which are vital in todayÂ’s fast-paced digital landscape.

The surge in demand for personalized customer experiences and data privacy compliance is also shaping the future of the web analytics platform market. With consumers expecting tailored interactions and regulatory bodies enforcing stricter data protection laws, businesses are investing in analytics platforms that offer robust privacy features and transparency. This dual focus on personalization and compliance is driving innovation, with vendors developing solutions that provide granular insights while ensuring data security. The growing adoption of cloud-based analytics platforms, which offer scalability, flexibility, and cost-efficiency, is further amplifying market growth, especially among small and medium enterprises (SMEs) seeking to leverage enterprise-grade analytics without significant upfront investments.

From a regional perspective, North America continues to dominate the web analytics platform market, accounting for the largest market share in 2024, followed by Europe and Asia Pacific. The regionÂ’s leadership is attributed to the presence of major technology providers, early adoption of digital marketing strategies, and a mature e-commerce ecosystem. However, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization, increasing internet and smartphone penetration, and a burgeoning e-commerce sector. The Middle East & Africa and Latin America are also experiencing steady growth, fueled by rising investments in digital infrastructure and a growing emphasis on data-driven business strategies. As organizations across regions recognize the strategic value of web analytics in achieving business objectives, the market is poised for sustained expansion through 2033.

Component Analysis

The web analytics platform market is segmented by component into software and services, each playing a pivotal role in the adoption and effectiveness of analytics solutions. Software solu

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Psychographic data goes beyond who your customers are; it delves into why they make the choices they do. VisitIQ's™ database captures a rich tapestry of consumer attributes, including values, beliefs, personality traits, preferences, and buying motivations. By integrating these deeper insights into your marketing approach, you can identify key psychological triggers that influence purchasing decisions, build stronger emotional connections, and create messaging that speaks directly to your audience’s core desires and needs.

Our psychographic data includes an extensive range of consumer interests and lifestyle indicators, allowing you to segment your audience with unparalleled precision. From health and wellness enthusiasts to tech-savvy early adopters, VisitIQ™ provides the granular data needed to define niche markets and tailor your products and services accordingly. This enables you to craft targeted campaigns that not only capture attention but also drive engagement and loyalty by aligning with the unique characteristics and preferences of your ideal customers.

VisitIQ's™ psychographic database is built on a vast network of U.S. consumer graph data, continuously updated and sourced from a variety of reliable channels. Our advanced data science techniques ensure that the data is accurate, relevant, and actionable, giving you the confidence to make strategic decisions based on real-world insights.

Whether you are looking to enhance your market segmentation, develop personalized marketing campaigns, or optimize product offerings, VisitIQ's™ psychographic data provides the insights you need to understand your audience on a deeper level. Use this data to anticipate consumer needs, predict trends, and stay ahead of the competition in an ever-evolving marketplace.

By integrating our psychographic insights into your existing marketing stack, you can refine your targeting strategies across all channels, from digital advertising and email marketing to content creation and social media engagement. This holistic approach to consumer understanding enables you to deliver consistent, impactful messaging that converts and retains customers more effectively.

Gain a competitive edge with VisitIQ's™ Psychographic Data. Leverage our comprehensive U.S. consumer graph to uncover hidden opportunities, foster stronger customer relationships, and drive growth with data-driven precision. Experience the future of marketing with insights that go beyond the surface and tap into the true drivers of consumer behavior.

DRAKO specializes in Mobility Data, allowing businesses to assess traffic around out-of-home (OOH) advertising locations and measure the impact of their advertising strategies. By leveraging our Mobile Location Database connected to our Home Extension technology and Device Graph—which includes additional device types like CTV and UID2—we provide a comprehensive understanding of consumer behaviour and traffic dynamics. We can deliver more than just impression counts, we can identify the broader reach of exposed users’ devices as well as identify their Home and Work Locations using our proprietary modelling. Moreover, we can create and further segment actionable audiences for you to use on any DSP or Social Media site of your choice !

Our data allows for granular analysis, including timestamped traffic patterns, which is essential for optimizing visibility radius around advertising placements. We enable businesses to select high-traffic sites for OOH and digital out-of-home (DOOH) advertising while also measuring geolift and effective conversion tracking.

Key Features: - Detailed Mobility Data for car and foot traffic, providing insights into consumer movement - Advanced measurement of traffic around OOH locations to inform ad placement strategies - Integration with Mobile Location Database for tracking consumer engagement and audience creation - Home Extension technology to identify devices linked to specific locations - Device Graph capabilities that include CTV and UID2 for comprehensive audience profiling - Granular timestamp analysis to assess traffic patterns when your DOOH Ad is displayed

Use Cases: - Ad Placement Strategy: Optimise placement based on real-time traffic insights - Out-of-Home (OOH) Advertising: Evaluate high-traffic locations for maximum visibility and get impression counts - Digital Out-of-Home (DOOH) Advertising: Track effectiveness of digital campaigns in real-time - Outdoor Advertising: Measure foot and car traffic to enhance traditional advertising strategies - Visibility Radius Optimization: Fine-tune radius settings to maximise ad exposure - Advertising Impact Analysis: Measure geolift and conversion rates tied to specific campaigns - Market Analysis: Assess regional traffic patterns to identify potential markets for expansion. - Event Planning: Analyse traffic data to select optimal locations for events and promotions. - Retail Strategy: Determine peak traffic times to optimise staffing and inventory. - Urban Planning: Use traffic data to inform city development and infrastructure improvements. - Brand Health Tracking: Monitor changes in consumer engagement around advertising locations over time. - Competitor Analysis: Evaluate traffic patterns near competitor locations to refine marketing strategies.

Data Compliance: Our Mobility Data is collected and processed in accordance with all relevant data protection regulations, ensuring that consumer privacy is maintained throughout the analysis process. It is all fully CCPA compliant and 100% sourced from SDKs (Software Development Kits), the most reliable and consistent mobile data stream with end user consent available with only a 4-5 day delay. This means that our location and device ID data comes from partnerships with over 1,500+ mobile apps. This data comes with an associated location which is how we are able to segment using geofences.

Data Quality: DRAKO employs stringent quality control measures to ensure that our Mobility Data is accurate and reliable. Continuous updates and validations keep our datasets relevant and reflective of current traffic patterns.

Media Mix Modeling AI Market Outlook

According to our latest research, the global Media Mix Modeling AI market size in 2024 has reached USD 1.87 billion, reflecting a robust demand for advanced analytics in marketing strategies. The market is experiencing a strong growth trajectory, with a CAGR of 23.4% anticipated over the forecast period from 2025 to 2033. By the end of 2033, the Media Mix Modeling AI market is forecasted to achieve a value of approximately USD 14.2 billion. This impressive expansion is primarily driven by the increasing need for data-driven decision-making in marketing, rising digital advertising expenditures, and the rapid adoption of artificial intelligence technologies to optimize marketing investments.

The exponential growth of the Media Mix Modeling AI market is fueled by the surging demand for actionable insights in advertising and marketing. As organizations strive to maximize their return on investment (ROI) in an increasingly competitive digital landscape, the adoption of AI-powered media mix modeling solutions has become indispensable. These solutions enable marketers to analyze vast and complex datasets, measure the effectiveness of various marketing channels, and allocate budgets more efficiently. The integration of AI enhances the accuracy of predictive analytics, allowing marketers to simulate different scenarios and select the most impactful media mix. Furthermore, the proliferation of omnichannel marketing strategies has created a pressing need for sophisticated modeling tools that can account for the interplay between traditional and digital channels, further propelling market growth.

Another significant growth factor for the Media Mix Modeling AI market is the rapid evolution of customer behavior and the increasing complexity of consumer journeys. Modern consumers interact with brands across multiple platforms, including social media, search engines, mobile applications, and offline touchpoints. This fragmented landscape presents a challenge for marketers seeking to attribute conversions and optimize media spend. AI-driven media mix modeling addresses this challenge by leveraging machine learning algorithms to identify patterns and correlations across disparate data sources. As a result, businesses can gain a holistic view of campaign performance and fine-tune their marketing strategies in real time. The growing availability of big data, coupled with advancements in AI and machine learning, is expected to further accelerate the adoption of media mix modeling solutions across various industries.

The increasing regulatory focus on data privacy and the phasing out of third-party cookies are also influencing the growth trajectory of the Media Mix Modeling AI market. As marketers face restrictions on traditional tracking methods, AI-powered media mix modeling offers a privacy-compliant alternative for measuring marketing effectiveness. These solutions can aggregate and analyze first-party data while maintaining compliance with evolving data protection regulations. Additionally, the shift toward privacy-centric advertising is prompting organizations to invest in advanced analytics platforms that can deliver accurate attribution and optimization without relying on invasive tracking techniques. This trend is expected to create new opportunities for vendors offering AI-driven media mix modeling solutions, particularly in regions with stringent data privacy laws.

From a regional perspective, North America continues to dominate the Media Mix Modeling AI market, accounting for the largest revenue share in 2024 due to the high concentration of technology-driven enterprises and early adoption of AI in marketing. Europe follows closely, driven by a strong emphasis on data-driven marketing and regulatory compliance. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by the rapid digitalization of economies, expanding e-commerce sectors, and increasing investments in AI technologies. Latin America and the Middle East & Africa are also experiencing steady growth, supported by the rising adoption of digital marketing practices and the emergence of new market entrants. The global nature of digital advertising and the universal need for marketing optimization are expected to ensure sustained demand for media mix modeling AI solutions across all regions.

Component Analysis

The Media Mix Modeling AI market by component is bifurcated into software and services, ea

Data Clean Room for Advertising Market Outlook

According to our latest research, the global Data Clean Room for Advertising market size reached USD 1.42 billion in 2024, with a robust growth trajectory driven by increasing data privacy regulations and the rising demand for secure data collaboration in advertising. The market is expected to expand at a CAGR of 22.1% from 2025 to 2033, reaching a forecasted value of USD 10.16 billion by 2033. The key growth factor propelling this market is the urgent need for privacy-compliant data sharing solutions among advertisers, publishers, and data providers, as organizations strive to maximize marketing ROI while adhering to evolving data protection standards.

The Data Clean Room for Advertising market is witnessing rapid growth due to the tightening of global data privacy regulations such as GDPR in Europe, CCPA in California, and similar frameworks worldwide. These regulations have fundamentally altered how organizations can collect, store, and utilize consumer data, making traditional data-sharing models obsolete in many cases. Data clean rooms provide a secure, privacy-centric environment where multiple parties can analyze and collaborate on data sets without exposing raw user-level data. This capability is increasingly sought after by advertisers and publishers aiming to maintain effective audience targeting and measurement while ensuring compliance. As a result, the adoption of data clean rooms is surging across sectors that rely heavily on data-driven advertising, particularly in retail, e-commerce, and media.

Another significant driver for the Data Clean Room for Advertising market is the ongoing deprecation of third-party cookies and the shift towards first-party data strategies. With major browsers phasing out support for third-party cookies, advertisers and marketers are compelled to find alternative ways to measure campaign effectiveness, attribute conversions, and optimize audience segmentation. Data clean rooms enable secure collaboration between brands and publishers, allowing them to match and analyze first-party data sets in a privacy-safe manner. This not only supports accurate audience measurement and advanced attribution analysis but also unlocks new opportunities for data monetization. The ability to conduct granular analytics without compromising consumer privacy is becoming a cornerstone of modern digital advertising, fueling the demand for sophisticated data clean room solutions.

Technological advancements and the proliferation of cloud-based solutions are further accelerating market growth. The integration of artificial intelligence, machine learning, and advanced encryption technologies into data clean room platforms is enhancing their scalability, security, and analytical capabilities. Cloud deployment models, in particular, are gaining traction due to their flexibility, cost-effectiveness, and ease of integration with existing advertising technology stacks. Organizations of all sizes, from large enterprises to SMEs, are increasingly leveraging cloud-based data clean rooms to facilitate cross-channel marketing, real-time campaign optimization, and collaborative audience insights. This technological evolution is expected to sustain the high growth momentum of the market throughout the forecast period.

Regionally, North America dominates the Data Clean Room for Advertising market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States leads in adoption, driven by a mature digital advertising ecosystem, stringent privacy regulations, and a high concentration of technology vendors. Europe is experiencing rapid growth due to GDPR-driven privacy concerns and increasing investments in data infrastructure. Meanwhile, Asia Pacific is emerging as a high-growth region, propelled by expanding digital economies, rising internet penetration, and growing awareness of data privacy issues. Latin America and the Middle East & Africa are also exhibiting steady growth, albeit from a smaller base, as businesses in these regions gradually embrace privacy-centric advertising solutions.

Component Analysis

The Data Clean Room for Advertising market by component is segmented into software and services. Software solutions form the backbone of data clean room infrastructure, providing the secure environments, privacy controls, and analytical tools necessary for compliant data collaboration. These platforms are designed to facilitate seamless

Urška Sršen and Sando Mur founded Bellabeat, a high-tech company that manufactures health-focused smart products. Sršen used her background as an artist to develop beautifully designed technology that informs and inspires women around the world. Collecting data on activity, sleep, stress, and reproductive health has allowed Bellabeat to empower women with knowledge about their own health and habits.

Since it was founded in 2013, Bellabeat has grown rapidly and quickly positioned itself as a tech-driven wellness company for women. By 2016, Bellabeat had opened offices around the world and launched multiple products. Bellabeat products became available through a growing number of online retailers in addition to their own e-commerce channel on their website.

The company has invested in traditional advertising media, such as radio, out-of-home billboards, print, and television, but focuses on digital marketing extensively. Bellabeat invests year-round in Google Search, maintaining active Facebook and Instagram pages, and consistently engages consumers on Twitter. Additionally, Bellabeat runs video ads on Youtube and display ads on the Google Display Network to support campaigns around key marketing dates.

Sršen knows that an analysis of Bellabeat's available consumer data would reveal more opportunities for growth. She has asked the marketing analytics team to focus on a Bellabeat product and analyze smart device usage data in order to gain insight into how people are already using their smart devices. Then, using this information, she would like high-level recommendations for how these trends can inform Bellabeat marketing strategy.

Context

Pricing has long been and will continue to be a core capability for retailers. Executives and merchants alike recognize it as one of the key value levers, and, accordingly, retailers have worked to refine their pricing strategy, tactics, and tools over the past several decades in hopes of optimizing their approach. Despite recent advances in analytics, decision-support tools, and methodologies, retailers are finding that the traditional approaches are not keeping pace. Indeed, the new digital era stemming from big data, mobile commerce, and the explosion of omnichannel retailing has meaningfully changed the environment and requires an overhaul of retailers’ pricing strategy and capabilities.

Real-time data updates (from sources such as mobile search and product reviews) generate terabytes of data, and global data generation is projected to grow at a rate of 40 percent annually. Armed with this data, retailers are hiring new talent, buying or building sophisticated tools to harness the data, and going after potential new margin increases of up to 60 percent, according to the McKinsey Global Institute.

So finding trends in pricing strategies is a key input to setting pricing strategy e.g. selecting key value categories (KVCs) and key value items (KVIs) and the relevance and evolution of these concepts as a core part of price strategy in today’s digital retail environment.

Ref. Pricing in retail: Setting strategy

Content

Over 7,000 electronic products with pricing information across 10 unique fields provided by Datafiniti's Product Database. The dataset also includes the brand, category, merchant, name, source, and more.

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Acknowledgements

Datafiniti's Product Database.

Inspiration

Identify retail industry trends in pricing strategies. E.g.:

• How does the prices.condition affect the pricing strategy of a product?
• Is there a correlation between the prices.dateSeen of a product and its dynamic pricing across merchants?
• What is the competitive pricing strategy for the same product from different merchants?
• What role does a product’s category play in its listing price?