Making Your Pictures Worth a Thousand Labels! (with Cloud Vision API)

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In this post, I’ll be showing some amazing ways the Vision API can extract meaning from your images – keep reading, or jump directly into a tutorial using Python, Node.js, Go, or Java! This tutorial can be completed at no cost within the Google Cloud Free Tier.
They say a picture is worth a thousand words. But how do you make those words available and useful? Around the world, we are generating more images than ever before, and it’s no surprise that businesses are turning to image recognition technology to help meet the immense opportunities created with this growing set of data.
Cloud Vision API is a powerful tool that enables you to perform a variety of tasks including label detection, text recognition, and object tracking on your image data. Whether it’s identifying products in a retail store, analyzing social media posts for brand mentions, or scanning through millions of images to find a specific object, the Cloud Vision API can help businesses automate their image analysis workflows and gain valuable insights from their visual data. To protect privacy, and help you build responsibly, the Cloud Vision API offers features to limit personal identification, such as person blur, which hides identifiable features.
Let’s explore a few of the key features of the Cloud Vision API.
Detect famous landmarks
Landmark detection allows you to analyze images to identify specific landmarks such as buildings, natural features, and other recognizable locations. Cloud Vision API recognizes landmarks and provides information about them, including their name, location, and other relevant details. Perhaps you are trying to identify the landmarks in images shared by customers as part of social campaigns, or want to build a mobile app that provides information to tourists on famous landmarks.
In the below left-hand side image, Cloud Vision API has detected the Eiffel Tower, shown in the visualized response. Not shown in this visualization here, but also detected, were Pont de Bir-Hakeim (the bridge) and Champs de Mars (the park in front of the Eiffel Tower).

Detect objects and label images
Object detection and labels are two related features that enable you to identify and classify objects within an image. Object detection detects and locates objects within an image, and provides information such as the position, size, and orientation of each object. Labels, on the other hand, provide a general classification of the content within an image.
Object detection has practical applications in many industries such as self-driving vehicles (where it’s critical), retail, manufacturing and more, while labels can be used to help classify and organize large collections of images, or to categorize and filter content.
You can see the similarities and differences in the responses provided by the object detection and labeling features in this image taken in Setagaya.


Detect text
Cloud Vision API detects and extracts text from any image, even if it’s handwritten or in different languages. Once it detects text, the API can provide information about the position, orientation, and size of each text element, as well as individual words, and their bounding boxes.
In this image of a traffic sign, Cloud Vision API has detected the text and provided it in the response.

Detect explicit content
Cloud Vision API can automatically identify and flag explicit or inappropriate content within an image using five categories: adult, spoof, medical, violence, and racy. The API provides a score that indicates the likelihood for each category in the image, which you can use to set thresholds in your application and decide how to handle those that exceed them. This feature is particularly useful for filtering or moderating user-generated content.
Luckily for the images I shared here, each category has been deemed “very unlikely” to be present. Phew!



Next Steps
These are just a few features of the Cloud Vision API and how it can help your business with automating image analysis workflows and gaining valuable insights from your visual data.
Head to the interactive walkthrough tutorials in Python, Node.js, Go, and Java to see step-by-step how to access the API and learn more about all the features that you can integrate into your own applications! Again, this tutorial can be completed at no cost within the Google Cloud Free Tier.
Medical Data Breakthrough: Google Aids PicnicHealth’s Growth

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In the fragmented world of U.S. healthcare, patients often have to wait in line or on hold, navigate multiple patient portals, and fill out numerous request forms—all in pursuit of their own medical history. Healthcare technology startup PicnicHealth is on a mission to put control back with the patient, where it belongs.
PicnicHealth’s growth, from closing successful venture rounds to winning machine learning (ML) competitions, speaks to not only improvements and opportunities in healthcare, but also how startups are leveraging Google Workspace and Google Cloud services to accelerate momentum.
The company does the heavy lifting of collecting records and leverages human-in-the-loop ML to transcribe and validate them with an abstraction team of medical professionals. The records are then structured into a complete medical history that patients can access and share with providers to get better care.
But PicnicHealth helps to improve patient health on more than one front. It allows patients to contribute their data to de-identified medical research, building high-quality, anonymized datasets that researchers and life sciences companies can use to better understand disease progression and treatment in the real world.
Google Workspace has been part of PicnicHealth from day one, helping the founders collaborate and shape the company’s vision using cloud-synced documents and spreadsheets to collaborate and model predictions. “I’ve had a Gmail account since 2006, and in college 100% of people worked out of Google Docs. Workspace continues to be the best choice for online collaboration, and that’s why it’s still the default standard for startups,” said Troy Astorino, CoFounder & CTO of PicnicHealth. “When we started PicnicHealth, my co-founder Noga Leviner was in San Francisco and I was in Southern California, and of course we used Workspace.”
Today, Google Workspace continues to play a central role in the company’s collaboration. “We create design documents in Google Docs, primarily for engineering and product changes, and get really healthy, vibrant discussions through comments,” noted Astorino. “This practice has grown beyond engineering and is used for everything from how the company operates to communication norms.”
Google Workspace offers everything the team needs to collaborate, no matter where employees are. PicnicHealth’s team has spread from San Francisco to being distributed across the country and around the world. Instant collaboration is crucial.
“We work in a complex domain where people need a lot of information to make good decisions,” said Astorino. “Google Workspace allows us to operate in a mode of default transparency, where people can easily get the information they need even if it wasn’t intentionally or directly shared with them. Whether it’s working in Docs or scheduling in Calendar, we can operate much more effectively than we could otherwise.”
By any measure, PicnicHealth’s trajectory is one of record success. The startup is a 2014 alumni of Y Combinator, a program that helped launch household names like Airbnb, DoorDash, and Dropbox. Three years later, the team went on to win the $1 million grand prize at Google’s Machine Learning Competition. And the momentum has continued— PicnicHealth has recently announced a $60 million Series C round, bringing the amount raised to date to over $100 million. With the Series C, PicnicHealth is investing in expanding its reach to more patients across over 30 diseases.
As a healthcare startup, PicnicHealth faced a very particular set of challenges, especially when working with and accessing data. Data fragmentation and interoperability are only some of the challenges of realizing the value of big data in the cloud. The healthcare industry is notoriously difficult to navigate due to sensitive data protection laws and regulations like the Health Insurance Portability and Accountability Act (HIPAA).
PicnicHealth started in the cloud on Amazon Web Services (AWS). However, after migrating over to Kubernetes and facing an expanding list of requirements for HIPAA compliance, the company started to explore alternatives.
“We needed to be HIPAA compliant, which was going to be painful on AWS, and we wanted to get away from managing and operating our own Kubernetes clusters,” recalled Astorino. “We had heard good things about GKE (Google Kubernetes Engine). And particularly valuable for us, — many technical requirements you need for HIPAA compliance are configured by default on Google Cloud.”
PicnicHealth would have had to implement a lot of changes and get specialized instance types to get their existing configuration to work. So, they began experimenting with Google Cloud and discovered a much smoother experience.
“It was a lot easier to manage in terms of product setup and developer experience,” said Astorino. “There is a sane product hierarchy of resources you can access and use through Google Cloud and the relationships between them, from coordinated IAM (identity and access management) to using Google Groups for granting permissions. Overall, it’s cleaner.”
Astorino added that the move has also opened the doors to taking advantage of other services in the Google Cloud ecosystem like Cloud SQL, BigQuery, and Cloud Composer. PicnicHealth also uses Security Command Center because it easily integrates with everything but also helps meet various compliance frameworks’ requirements, providing visibility, near-real-time asset discovery, and security information and event management.
But most importantly, the integrated ecosystem has simplified the work needed for PicnicHealth to create a secure environment for employees to use when working with sensitive medical records while still providing all the tools they need. For example, abstractors not only use Google Workspace but also have Chromebooks because they are easy to manage and secure.
Altogether, Google Cloud helps form a technology stack that has enabled the startup to build a massive labeled dataset containing over 100 million labeled medical data concepts. In turn, it accelerates PicnicHealth’s ability to generate highly-performant AI models and feed other ML pipelines, which has been vital for processing and reviewing data at scale.
To learn more about how Google Workspace and Google Cloud help startups like PicnicHealth accelerate their journey, visit our startups solutions pages for Google Workspace and Google Cloud.
2022 Healthcare Trends: Healthcare Data, M&As, Better Patient Care, AI in Drug Development & Strategic Partnerships

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The COVID-19 pandemic continues to push the healthcare and life sciences industry in entirely new ways. In record time, we’ve witnessed public health officials, vaccine developers, equipment manufacturers, and essential workers take life saving actions—regularly putting their own lives at risk—to respond to the exceptional challenges of our time.
Yet after all the turmoil and uncertainty of the pandemic, record breaking levels of investment continue to come into the market to fuel innovations.
Vaccine development is now measured in weeks rather than years; providers are leveraging telehealth technologies to improve the physician and patient experience; and individuals have embraced a variety of devices to assume greater ownership and control of their personal health.
With that in mind, here are five of the many innovations I see driving healthcare and life sciences for at least the next 12 months:
1. Unleashing the power of healthcare and life sciences data
People have arguably never had as much access and understanding to their personal health data than they can in 2022. They have the ability to understand their genetic makeup, medical history, family history, and activity levels to ensure they are living a healthy lifestyle. Taken together, this longitudinal profile can become the basis for more personalized medicine. Add wearable devices to the mix and patients gain real- or near-real-time updates on their health status.
Further, global regulatory agencies have continued to mandate the need for providers to maintain a longitudinal health record that provides a holistic view of the patient across all the encounters they have had with a health system. Physician surveys conducted by the The Harris Poll and Google Cloud show that a 360-degree view of the patient, across all provider encounters, leads to faster, more accurate diagnosis, and better outcomes.
If secure data access and interoperability can begin to include insurers, researchers, public health officials, and others in the field, the powerful network effects benefiting patients will only grow. When combined with those longitudinal phenome profiles, the opportunities for personalized and preventative medicine enter a whole new era.
2. Healthcare and life sciences M&A boom continues
Although the pandemic initially slowed activity on mergers and acquisitions in the early part of 2020, the healthcare and life sciences industry has seen a rapid rebound and acceleration of deals ever since. The success of COVID-19 vaccines, the importance of telehealth, and the focus on molecular modeling and genomic-based drug development have all boosted investment as organizations look to enter new markets, develop new therapies, and leverage low interest rates while they last.
In 2021, the total funding of US digital health startups surpassed $29 billion across 729 deals, according to advisory Rock Health. That’s almost double the levels of 2020, which itself set records. Analysts at PWC meanwhile estimate M&A investments in biopharmaceutical and life sciences could approach $400 billion this year across all sub-sectors
Clearly, the pandemic has been a primary driver of investment as the focus on healthcare has dramatically increased. Yet the increased activity also reflects changing business models and emerging technologies that are now required to compete in the rapidly evolving space. For organizations to capitalize on these investments, it will take not only great vision and intellectual property but also the right technologies—like cloud—and the right data interoperability models, to make partnerships and acquisitions more scalable, feasible, and seamless.
3. Transforming the patient experience at a new rate
The pandemic has shined a spotlight on the inefficiencies and complexities that exist in healthcare markets across the globe. As wave after wave has surged, global healthcare systems remain overwhelmed on most every aspect of patients’ treatment journeys. Even before COVID-19, healthcare was already one of the largest spend areas for governments around the world. The pandemic has only exacerbated the known issues.
Clearly, administrators, regulators, physicians, nurses, and patients would agree that the processes and models need to change. There’s a need to maintain this momentum and even increase the tempo to achieve lasting change.
Take telehealth. Within months of the start of the pandemic, providers moved to provide more remote capabilities so physicians could still meet with patients virtually to ensure health and safety on all sides. Payers recognized the importance of telehealth and began to update reimbursement rules. And organizations are now reimagining policies in areas such as prior authorization, submission, and adjudication to reduce complexity and bureaucracy while improving responsiveness.
Looking beyond the system, organizations are also recognizing and deepening their understanding of the structural and social determinants of health that impact patient care and health outcomes, especially for historically underserved communities. Private and public sectors are learning from, and increasingly partnering with, the social sciences, public health, biomedical informatics, computer science, public policy and community groups around how to build a mI’ore equitable and inclusive consumer products and Health IT strategies.
The newfound levels of transparency, visibility, and accountability that patients, caregivers, and organizations are achieving will ultimately increase competition and provide a more effective, equitable, efficient and, above all, healthier marketplace for all patients. As we move past the worst of the pandemic, regulators and organizations should keep fighting for progress over business as usual.
4. AI is now a core competency for Drug Development
The ability of organizations like Pfizer, Moderna, Johnson & Johnson, and Astrazeneca to develop COVID-19 vaccines has been a remarkable accomplishment—particularly the historic speed with which they were created and deployed. This innovation acceleration was largely enabled by the use of new drug development platforms that allow researchers to use artificial intelligence and machine learning to model protein and cellular interactions to rapidly advance the science.
No longer must researchers rely on traditional laboratory testing (and retesting). With their improved understanding of the molecular and genetic structure of a patient and, for example, their tumor, researchers can use AI to enable simulations on computers rather than testing in live conditions. This technology can process thousands, even millions of simulations to help identify high-potential candidates for treatment consideration and subsequent analysis.
AI-enabled drug discovery models can eliminate months and years from the research process, which can reduce the time to develop a drug and accelerate the time to treatment for an individual patient. As just one example, consider the work on AlphaFold2 by Google’s DeepMind unit who leverages AI to predict effective protein shapes for new drugs. Healthcare and life sciences organizations already recognize the potential of AI. Now comes the investments to leverage this rapidly evolving technology to support their efforts now and in the future.
5. Ecosystem partnerships tackle complexity and spur innovation
As the importance and growth of the healthcare and life sciences industry continues, we will see even more new players and partnerships emerging to address old problems in new ways. This trend will touch all aspects of the healthcare value chain and will, increasingly, see three- and four-player partnerships emerge to address the complex challenges of today’s healthcare marketplace.
Technology will continue to play a key role as capabilities and platforms will transform all aspects of the marketplace. Cell phones, wearable devices, and other technologies will provide real-time updates and notifications to patients on everything from glucose levels to payments for healthcare services. Voice recognition software will document physician and patient discussions to reduce the burden of record keeping. Real-world data will be used to simplify and confidentially recruit patients for participation in clinical trials.
New players will continue to enter the market to improve health outcomes and reduce costs. Major retailers are among the companies extending their pharmacies to provide additional diagnostic and concierge services, saving patients from additional appointments while boosting prevention. Community organizations are emerging to help identify and care for underserved communities whose health outcomes are significantly lower than the average patient.
For all the exhausting and heart-wrenching challenges of the past two years, the opportunities the pandemic has laid bare cannot be overlooked. We owe it to those who have worked and fought so hard for every life to forge even more new partnerships—and make it easier to do so—so that the next crisis, when it does arise, will never be as bad as the one we’re now conquering. Technology can be the enabler in this effort and help bring us together to continue to conquer the challenges that lie ahead.
Poor Product Discovery Causes Shoppers’ Abandonment, Time to Augment ‘Search Experience’ with Google’s Retail Search!

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How many times has a shopper searched for a product on a store’s website only to get results that aren’t relevant—or worse, provided no search results at all? While most ecommerce sites have search capabilities, few accomplish their ultimate goal: making it easy for customers to discover the products they want.
Some 94% of U.S. consumers abandoned a shopping session because they received irrelevant search results, according to a 2021 survey conducted by The Harris Poll and Google Cloud. It’s a phenomenon known as “search abandonment.” Indeed, poor product discovery experiences can stop a purchase in its tracks and leave shoppers frustrated. Retailers miss out on a staggering $300 billion each year due to search abandonment in the U.S. alone.
With the announcement today of our latest discovery solution, Retail Search, now generally available, retailers worldwide can supercharge their websites and mobile apps with Google-quality search. Built on Google’s technologies that understand user intent and context, the solution helps businesses improve the search and overall shopping experience across all of their digital touchpoints.
Already, early adopters like Lowes, Fnac Darty, and Casas Pernambucanas have been using Google Cloud’s discovery solutions to increase sales conversions, boost basket sizes, and improve customer engagement.
Understanding user intent in search is a hard problem
While we’ve come a long way from the days when search was largely based on keywords and boolean rules, shoppers still struggle to find what they’re looking for. They often have to come up with a perfectly-worded query that a retailer’s site search engine will understand, sometimes rephrasing several times before getting the results they want—if they find anything relevant at all.
Traditional search technologies don’t work in the modern age of online retail, where tens or even hundreds of thousands of items are available on a single ecommerce site.

Today, people expect search engines to understand their intent more deeply, return relevant results faster, and help them discover new products easily with personalized recommendations.
Fortunately, product discovery experiences on retail sites can now offer superior, contextual experiences for consumers, combining marketing and data science insights along with advanced search technologies based on machine learning and artificial intelligence.
Now, through the power of Retail Search, when a shopper searches for a “long black dress with short sleeves and comfortable fit” on an ecommerce site, they should immediately get results for precisely that—rather than refining their search multiple times, or worse, giving up their shopping journey.
Helping retailers solve their search experience woes
Retail Search transforms the shopping experience and makes it easier for shoppers to find relevant products by surfacing results that are intuitive and contextual.
This fully managed service is easily customizable, enabling organizations to craft shopper-focused search experiences. Our site search solution builds upon decades of Google’s experience and innovation in search indexing, retrieval, and ranking. Retailers can make product discovery even easier for shoppers, while optimizing for their business goals with advanced capabilities, including:
- Advanced query understanding that produces better results from even the broadest queries, including non-product searches.
- Semantic search to effectively match product attributes with website content for fast, relevant product discovery.
- Optimized results that leverage user interaction and ranking models to meet specific business goals.
- State-of-the-art security and privacy practices that ensure retailer data is isolated with strong access controls and is only used to deliver relevant search results on their own properties.
Retailers can now shape product discovery into a shopping experience that is timely, relevant, and personalized—all powered by Google Cloud.
Early adopters seeing immediate impact
Several leading retailers around the world have been able to quickly create better online shopping experiences for their customers and capture more digital and omnichannel growth using Google Cloud’s product discovery solutions.
“With limited customer signals and no historical data, descriptive long-tail searches are some of the most challenging queries to understand,” said Neelima Sharma, senior vice president, technology, e-commerce, marketing and merchandising at Lowe’s. “We have been partnering with Google Cloud to give our customers relevant results for long-tail searches and have seen an increase in click-through and search conversion and a drop in our ‘No Results Found’ rate since we launched.”
“Making constant improvements to our website search engines has always been a priority for us as we aim to give our customers a simpler, more customized and enhanced online shopping experience,” said Olivier Theulle, Fnac Darty’s chief ecommerce and digital officer. “As we implement Google Cloud’s site search solution on our Fnac Darty websites and are the first French retailer to do so, we expect the solution to deliver increased conversion rates while also offering greater customer satisfaction.”
“Google Cloud has helped improve the indexing and quality of search results on Pernambucanas’ digital platforms, providing a better experience to our customers and improving sales conversions,” said Fabiano Rustice, chief information officer at Pernambucanas. “On Black Friday in November 2021—the largest retail date in Brazil—we saw a 20% reduction in search refinements per user, for which Retail Search was instrumental. We are proud to be an early adopter and to have Google Cloud as a strategic innovation partner.”
In addition to Google Cloud’s efforts working directly with customers, our ecosystem of key partners, including GroupBy, Lucidworks, GridDynamics, SpringML, and others are also leveraging Google Cloud discovery solutions to provide value added services to their customers.
“In the fast paced and extremely competitive ecommerce environment, we are seeing that forward-looking and innovative retailers are prioritizing product discovery,” said Roland Gossage, CEO of GroupBy. “GroupBy has proudly partnered with Google Cloud to launch its product discovery solutions across several client environments. Already we have seen a more than 10% increase in online revenues, which equates to millions of dollars, and this is just the beginning.”
To learn more, visit Discovery Solutions for Retail or contact your Google Cloud field sales representative.
Contact Center AI (CCAI) with Agent Assist can Lower Opex and Handle 28% More Chats

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Contact Center AI (CCAI) brings Google’s innovation in conversational AI to solve the most challenging customer service needs while lowering operational costs. More than a thousand customers have deployed CCAI and are steadily turning it on to power their production contact centers.
Today, we’re excited to announce that we’ve made CCAI even stronger with Agent Assist for Chat, now in public preview.
Agent Assist provides your human agents with continuous support during their calls and now chats by identifying the customers’ intent and providing them with real-time recommendations such as articles and FAQs as well as responses to customer messages to more effectively resolve the conversation.
Customers using Agent Assist for Chat have been able to manage up to 28% more conversations concurrently, while also driving up customer satisfaction by 10%. Additionally, we’ve seen them respond up to 15% faster to chats, reducing chat abandonment rates and solving more customer problems.
Agent Assist provides two key components to help agents manage conversations better:
- Smart Reply provides response suggestions to agents so they can quickly and appropriately respond to customer messages. These suggestions can be taken from your top performing agents as well as modified even further to ensure suggestions properly reflect the tone and voice of your brand. Agent Assist learns when and what recommendations to make by building a custom model that’s trained on your (and only your) data.
- Knowledge Assist unlocks the power of your knowledge base to provide articles and FAQ suggestions to agents in real-time as the conversation progresses. When using Knowledge Assist, agents no longer need to make the customer wait while they navigate multiple applications and data to find the resolution to the customer’s issue — the answer is delivered right to them.
“We’ve been very impressed by the chat capabilities of Agent Assist,” said Chris Smith, Vice President of Digital Service at Optus, one of the largest telecommunications companies in Australia
Optus has been using CCAI Dialogflow CX to send queries to virtual agents and sees great potential to use Agent Assist to provide recommendations to their customer support representatives. They expect Agent Assist to help minimize repetitive tasks by providing response and typeahead suggestions, helping improve the efficiency of their agents and the quality and consistency of service they provide.
Another customer, LoveHolidays, is using Agent Assist to support their agents and customers in the travel industry.
“Agent Assist has been a beneficial aid to agents and our customers alike… It gives us the power to flex our contact center staff levels in hours not weeks,” said Eugene Neale, Director of CX Engineering & Business IT at LoveHolidays
Analysts say online chat is becoming one of the most popular ways to reach out to businesses for customer support. IDC research finds that single-function contact centers worldwide are increasingly rare — in 2020, although phone/voice is still responsible for most interactions (at around 18%); email is responsible for around 13% of interactions, and live chat (without automation) is responsible for around 8% of interactions, according to IDC, Toward the AI-Powered Contact Center, Doc # EUR147017320, December 2020.
Deploying CCAI with Agent Assist for Chat
As part of Google’s Contact Center AI suite, Agent Assist provides a seamless handoff from chats managed by your Dialogflow CX virtual agents. If a conversation or customer requires a live agent, Agent Assist will help your team pick it up quickly and drive it to a satisfying resolution.
Historically, when managers saw contact center volumes increase they had two choices: allow customers to wait longer to speak to someone (lowering customer satisfaction) or bring on more agents (increasing cost to serve). Deploying CCAI provides contact center leaders with a third choice: equip agents with tools like, Agent Assist for Chat, to efficiently manage customer interactions while maintaining high quality service.
Global CCAI partners support Agent Assist for Chat
Agent Assist for Chat is a set of public APIs that your engineering team can integrate directly into an agent desktop to control the agent experience from end-to-end. For a more out-of-the-box solution, we have partnered with LivePerson and 247.ai to build Agent Assist directly into their agent desktops.
“Integrating our Conversational Cloud directly with Agent Assist means agents can leverage cutting-edge productivity AI to build even further on the massive ROI of conversational commerce, from reduced agent effort and time-to-respond to increased customer satisfaction and revenue,” said Alex Spinelli, CTO of LivePerson.
More Agent Assist resources
To learn more, check out the Agent Assist webpage. Give Agent Assist a try by training a model and then testing it using the Agent Assist simulator.
Three Data Insights That Set Marketing Leaders Apart from Marketing Laggards

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With insights directing the bulk of today’s marketing decisions, leading marketers are driving growth by embracing three core mindset shifts. Marketing leaders are working toward a holistic view of consumers; they are investing in machine learning to support their activities
1. Leading marketers are working toward a holistic view of consumers.
- 63% of leading marketers agree they are using KPIs to develop a single integrated view of the customer.
- 66% of leading marketers agree they should build teams for end-to-end customer experiences and journeys, across channels and devices.
- Marketing leaders are 60% more likely than laggards to believe that marketing teams should own a data-driven customer strategy that supports all organizational stakeholders.
2. Leading marketers are investing in machine learning to support their activities.
- Measurement-leaders are more than 2X as likely as their measurement-challenged counterparts to agree that their organization is already investing in automation and machine learning technologies to drive marketing activities.
- 75% of marketers who use machine learning to drive marketing activities said they were satisfied with how their KPIs inform and influence decision-making across their enterprise.
- 73% of marketing leaders who have invested in machine learning have shifted more than 10% of their time from manual activation to strategic insight generation.
3. Leading marketers believe how they apply their data is crucial to success.
- 66% of marketing leaders believe how companies apply their data will play a key role in their ability to thrive.
- 60% of leading marketers believe data-driven attribution is essential to understanding journeys of high-value customers.
- Marketing leaders are 53% more likely than laggards to say machine learning processes data signals to help marketers better understand consumer intent.
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