Bridging disparate data in a fragmented world
In today’s world, consumers engage with brands across multiple platforms, including social media, online marketplaces, in-store experiences, and customer service touchpoints. However, the main challenge for marketers and advertisers is the fragmentation of customer data across these different channels. Each platform generates its own set of data, which is stored in different databases and formats. Integrating these various data sources to create a unified view of the customer is a complex task involving technology and understanding customer behavior across different digital and physical channels.
Businesses must link these data fragments to avoid creating a disconnected customer experience. For example, a person may browse products on a mobile app, ask questions through a customer service chat, and eventually purchase in an online marketplace. Traditional data analysis methods often need to recognize these activities as those of a single customer, which can result in missed opportunities to deliver personalized customer experiences across the customer journey.
Identity resolution: The key to a unified customer experience
Connecting online interactions across various platforms is a challenge for brands. Identity resolution enables enterprises to overcome this challenge by stitching together disparate signals and records to orchestrate experiences and analyze outcomes more effectively. By pairing Experian’s identity capabilities with AWS Clean Rooms, enterprises can securely collaborate with their partners to derive deeper insights without exposing sensitive underlying data sets.
This partnership between AWS and Experian enables effective matching between disparate data sets, bolstering privacy-enhanced media planning, insights, data enrichment, media activation, and measurement use cases. Depending on their distinct needs and existing identifiers, customers can use two specific offerings of Experian’s identity resolution solutions paired with AWS Clean Rooms.
Experian’s identity resolution products ensure a frictionless brand experience across various channels, enhancing the customer journey from start to finish. Brands can employ our adaptable identity resolution solutions to forge connections between contextual, behavioral, lifestyle, and purchase-based data sources, assembling comprehensive customer profiles. Use dependable digital data to make informed decisions and elevate consumer engagement. Advanced deterministic and probabilistic features, combined with data science and cutting-edge technology, work hand in hand to mitigate risk and uphold data privacy.
Such recognition enables a more comprehensive understanding of your clientele, fostering trust and amplifying campaign effectiveness by utilizing securely managed, standardized customer data. With this strategic approach, businesses can achieve their objectives regulatory-compliant.
The consumer perspective: Why consistency matters
Data fragmentation can lead to inconsistent experiences for consumers, which can be frustrating and erode brand trust. For instance, imagine receiving a promotional email for a product you already purchased through an app or being targeted for a product you decided against.
Consumers are increasingly tech-savvy and demand a seamless, integrated experience regardless of how they interact with a brand. They want to feel valued and recognized at every touchpoint and don’t care about the complexities of data analytics. As a result, brands face significant pressure to get identity resolution right.
Data security and privacy: A Fort Knox for your data
AWS Clean Rooms empowers their customers to establish a secure data clean room within minutes, facilitating collaboration with any other entity within AWS. This fosters the generation of unique insights regarding advertising campaigns, investment decisions, clinical research, and more. With AWS Clean Rooms, the need to store or maintain a separate copy of data outside the AWS environment for subsequent dispatch to another party for consumer insight analysis, marketing measurement, forecasting, or risk assessment becomes obsolete.
AWS Clean Rooms provides an expansive set of privacy-enhancing controls for clean rooms. This includes query controls, query output restrictions, and query logging that allows customers to tailor restrictions on the queries executed by each clean room participant. Moreover, AWS Clean Rooms include advanced cryptographic computing tools that maintain data encryption—even during query processing—to adhere to stringent data-handling policies. This process employs a client-side encryption tool—an SDK or command line interface (CLI)—that utilizes a shared secret key with other participants in an AWS Clean Rooms collaboration.
With a wealth of expertise in data privacy management, Experian enhances campaign effectiveness and fosters trust by managing standardized customer data securely. By using the identity graph, you can preserve a unique identity for each customer. This strategy enables you to comprehensively understand your clientele and reach your business objectives in a regulatory-compliant manner.
The future of data-driven marketing starts here
AWS customers can use AWS Clean Rooms to establish their own clean rooms in mere minutes, initiating the analysis of their collective data sets without sharing their underlying data with each other. Customers can use the AWS Management Console to choose their collaboration partners, select data sets, and configure participant restrictions. With AWS Clean Rooms, customers can effortlessly collaborate with hundreds of thousands of companies already using AWS without needing to move data out of AWS or upload it to a different platform. When running queries, AWS Clean Rooms accesses data in its original location and applies built-in, adaptable analysis rules to assist customers in maintaining control over their data.
Coupled with Experian’s trusted data privacy management and unique Experian ID, businesses can effectively manage customer data, secure partners’ communication, and achieve regulatory-compliance objectives. This combination allows companies to use data-backed insights to supercharge their marketing initiatives, resulting in more meaningful customer interactions, improved match rates, and business success.
About the authors
Kalyani Koppisetti, Principal Partner Solution Architect, AWS
Kalyani Koppisetti is a technology leader with over 25 years of experience in the Financial Services Industry. In her current role at AWS, Kalyani advises financial services partners on best-practice cloud architecture. Kalyani works closely with internal and external stakeholders to identify industry technical trends, develop strategies, and execute them to help Financial Services Industry partners build innovative solutions and services on AWS. Technical and Solution interests include Cloud Computing, Software-as-a-Service, Artificial Intelligence, Big Data, Storage Virtualization and Data Protection.
Matt Miller, Business Development Principal, AWS
In his role as Business Development Principal at AWS, Matt drives customer and partner adoption for the AWS Clean Rooms service specializing in advertising and marketing industry use cases. Matt believes in the primacy of privacy-enhanced data collaboration and interoperability underpinning data-driven marketing imperatives from customer experience to addressable advertising. Prior to AWS, Matt led strategy and go-to-market efforts for ad technologies, large agencies, and consumer data products purpose-built to inform smarter marketing and deliver better customer experiences.
Tyler Middleton, Sr. Partner Marketing Manager, Experian Marketing Services
Tyler Middleton is the Partner Marketing Lead at Experian. With almost 20 years of strategic marketing experience, Tyler’s focus is on creating marketing strategies that effectively promote the unique value propositions of each of our partners’ brands. Tyler helps our strategic partners communicate their mutual value proposition and find opportunities to stand out in the AdTech industry. Tyler is an alumnus of the Seattle University MBA program and enjoys finding new marketing pathways for our growing partner portfolio.
Latest posts
Published in MediaPost With the explosion of smartphones and digital tablets and the steady rise of Internet-connected televisions, gaming consoles, and more, consumers are increasingly watching online video when and where they want. New research from Experian Marketing Services on cross-device video found that as of October 2013, 48% of all U.S. adults and 67% of those under the age of 35 watched online video during a typical week, up from 45% and 64%, respectively, just six months earlier. At the same time, the share of households considered “cord-cutters” — those with high speed Internet but no cable or satellite TV — is on the rise, and that has a real impact on marketers and on the medium of television, the recipient of the largest share of advertising dollars. While the growing trend in cord-cutting is understandably disturbing to cable and satellite companies and disruptive to the television advertising revenue model overall, the growth in online viewing creates opportunities for marketers. Online video viewers can be more easily targeted and served up advertising that is more relevant, responsive and measureable. Marketers can also be more confident that their online ad was actually seen given that viewers are typically unable to skip ads. And while CPMs for online video ads may generally be lower than those of TV, marketers can use that savings to negotiate costs based on clicks or transactions rather than impressions, giving them a better picture into audience interest and insights to inform their budget allocation. Expect “Cutting the cord” to continue Today, over 7.6 million U.S. homes or 6.5% of households are cord-cutters, up from 5.1 million in 2010 or 4.5% of households. One thing enabling consumers to cut the cord is the rise in Internet-connected TVs, which allows viewing of Internet video on demand without sacrificing screen size. In fact, a third of adults (34%) now have at least one TV in the home that is connected to the Internet either directly or through a separate device like an Apple TV or Roku, up from 25% in 2012. With the launch of devices like Google’s Chromecast and the Amazon Fire TV, those numbers are sure to rise even more in the months and years ahead. Cord-cutters like the bigger screen Our analysis found that the act of watching streaming or downloaded video on any device is connected to higher rates of cord-cutting but the act of watching on a television is the most highly correlated. In fact, adults who watch online video on a television are 3.2 times more likely than average to be cord-cutters. Those who watch video on their phone (the device identified in the analysis as that most commonly used for watching online video) are just 50% more likely to be cord-cutters. Millennials are more likely to be cord-cutters We found that households with an adult under the age of 35 are almost twice as likely to be cord-cutters. Throw a Netflix or Hulu account into the mix and the rate of cord-cutting among young adult households jumps to nearly one-in-four. Given these surprising stats, many Millennials may be cord-cutters without ever having “cut” a cord. And that’s an important trend to watch since it means a significant portion of this generation will never pay for TV. Millennials are also the most device-agnostic, with over a third saying they don’t mind watching video on a portable device even if it means a smaller screen. That’s more than double the rate of those ages 35 and older. This decentralized viewing can create headaches for marketers who need to start a relationship with Millennials during this stage of their lives when they’re most open to trying out new brands and have yet to settle down. On the plus side, marketers who do manage to reach this audience will find them much more open to advertising than average. In fact, Millennials are more than four times more likely to say that video ads that they view on their cell phone are useful. So while the challenge is big, so is the potential reward.
Published in AdExchanger. “Data-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media. Today’s column is written by Tom Manvydas, vice president of advertising strategy and solutions at Experian Marketing Services. The proliferation of connected electronics has spurred new interest in device-recognition technologies even though they have been in use since the 1990s. As we enter the “Internet of Things” era, device recognition will significantly impact the ad tech ecosystem. Many network advertising technologies are becoming obsolete as cookie blocking grows and the Internet becomes more mobile and device-centric. Device recognition will be yet another technology challenge for marketers but has the potential to overcome many key tracking, measurement and privacy issues with which data-driven marketers have struggled. By leveraging device recognition technologies, marketers can protect their investments in Web 2.0 ad tech, like multitouch attribution, and improve their overall digital marketing programs. Device Recognition Vs. Cookies Device recognition attempts to assign uniqueness to connected devices. By focusing on the device, you are able to “bridge” between browsers and apps, desktop to mobile and across OS platforms like iOS and Android. Device-recognition IDs function like desktop cookies for devices but with four important differences: 1. Coverage: Device-recognition methods are largely immune from cookie limitations. About half of mobile engagements on the Web do not involve cookies, while third-party blocking impacts up to 40% of desktop engagements. 2. Persistency: Device-recognition IDs can be more persistent and less fragmented than most desktop cookies. For example, Apple’s UDID or Android ID are permanent, and network node IDs like MAC addresses are near-permanent. Proxy IDs such as IDFA are persistent but can be updated by the device owner or ID provider. 3. Uniqueness: Devices are unique and cookies are fragmented. The digital media industry incurs substantial overhead cost and loss of efficiency when dealing with fragmented profiles and obsolete data caused by cookie churn. However, device-recognition methods are limited in their ability to recognize multiple profiles on shared devices. 4. Universality: Device-recognition technologies are universal and generally work across devices and networks. However, interoperability issues across device operating systems, such as iOS and Android, can limit the universal concept. There are many types of device-recognition technologies but two basic approaches to device recognition: deterministic and probabilistic, each with their pros and cons. Deterministic Approach: Accurate And Persistent But Complicated Deterministic device recognition primarily uses the collection of various IDs. While the mobile developer is familiar with the variety of IDs, it’s important that marketers become better-versed in this area. Examples include hardware IDs (including serial numbers), software-based device IDs (such as Apple’s UDID or the Android ID), digital data packet postal codes or proxy IDs (such as MAC addresses for WiFi or Bluetooth, IDFA for both iOS and Android and open-source IDs). Deterministic methods improve the accuracy of tracking, targeting and measurement over current cookie-based methods. They can improve the ability to more persistently manage consumer opt-outs. But the proliferation of device types limits the universality of deterministic device recognition. Without uniform standards across platforms, marketers need to account for multiple ID types. Also, deterministic device-recognition methods are not well developed for desktop marketing applications. The lack of interoperability across deterministic device IDs makes execution too complicated. Deterministic device IDs were meant for well-intentioned uses, such as tracking the carrier billing for a device. However, they present privacy and data rights challenges, leading to blocking or limited access by companies that control IDs. Probabilistic Device Recognition: A ‘Goldilocks’ Solution Probabilistic device recognition may be the ideal solution for a connected world that does not rely on cookies nor wants to use overly intrusive deterministic device recognition. Probabilistic device recognition is not a replacement for deterministic IDs. Instead, it complements their function and provides coverage when they are not available. The probabilistic approach is based on a statistical probability of uniqueness for any single device profile. This approach creates a unique profile based on a large number of common parameters, such as screen resolution, device type and operating system. This process can uniquely identify a device profile with 60% to 90% accuracy, compared to 20% to 85% accuracy for cookie-based identification methods. Probabilistic IDs are more persistent than cookies with better coverage, but less persistent than deterministic device IDs. The natural evolution of the device takes place over time and prevents persistent identification. Probabilistic device recognition can be universal and is not impacted by interoperability issues across platforms — the technology used to generate a probabilistic ID on one network can be the same technology on another network. Unlike some deterministic device recognition approaches, there is no device fingerprinting. Probabilistic device recognition accurately identifies profiles in aggregate, rather than a single device. That’s the inherent beauty of probabilistic device recognition: It can generate more accurate targeting results than cookie-based methods without explicitly identifying single devices. This is more than good enough for most marketers and significantly better than what’s available today. Another benefit is the absence of any residue on the device — no cookie files, flash files or hidden markers. Probabilistic methods can work on devices that block third-party cookies or connect to the Web without using any cookies. For example, you might have a hard-to-reach but valuable audience segment. Probabilistic device recognition could effectively increase your reach on this segment by 40% to 50% and increase the overall targeting accuracy by two times. Let’s say the actual population for this segment is 100,000 members. The typical cookie-based approach might reach 28,000 members but the typical probabilistic device-recognition approach could reach 65,000 members. A Decline In Hardware Entropy If you take a close look at the emitted data from today’s devices, it is not easy to analyze it for device identification. That’s because the data footprint of one device looks a lot like another. Device recognition augmentation methods can address this, such as device usage profiles, geo location clustering, cross-device/screen analytics or ID linkage for first-party data owners. In the short term, device-recognition technologies, particularly probabilistic methods, can greatly improve today’s digital marketing programs. Marketers should become fluent in their use cases and benefits. If 2013 was the year of mobile, I think we’ll see a surge in marketing applications based on device-recognition technologies in 2014. Follow Experian Marketing Services (@ExperianMkt) and AdExchanger (@adexchanger) on Twitter.
According to Experian Marketing Services’ 2014 Digital Marketer: Benchmark and Trend Report, social media Websites are playing an increasingly important role in driving traffic to other Websites, including retail sites and even other social networking sites, at the expense of search engines and portal pages. For instance, as of March 2014, social media sites account for 7.72 percent of all traffic to retail Websites, up from 6.59 percent in March 2013. Further, Pinterest, more than Facebook or YouTube, is supplying the greatest percentage of downstream traffic to retail sites. According to the Digital Marketer Report, more retailers are directing their customers to social media within their email campaigns. In fact, 96 percent of marketers now promote social media in their emails, and it shows. In 2013, for instance, email Websites generated 18 percent more clicks to social networking pages than the year prior. Social drives more traffic to other social Websites Social media Websites are driving more and more traffic to other social sites. In 2013, 15.1 percent of clicks to social networking and forum sites came from other social networking sites, up from a 12.5 percent click share reported in 2012. Despite driving the greatest share of traffic to social networking sites with 39.1 percent of clicks, search engines’ share of upstream traffic to social declined a relative 13 percent year-over-year. Among the other top referring industries to social, only the portal front pages industry — which includes sites like Yahoo!, MSN and AOL and is closely affiliated with search engines — showed a drop in upstream click share providing further evidence that increasingly all (or most) roads lead to social. To learn more about key trends in social media traffic, including downstream traffic from social sites and the share of consumers accessing social media across multiple channels, download the free 2014 Digital Marketer: Benchmark and Trend Report.