Originally appeared on Total Retail
Retail media networks (RMNs) continue to demonstrate how they can be a powerful monetization driver for retailers, creating a win-win-win for everyone involved. Retailers can monetize their valuable first-party data as well as their online and in-store inventory, while customers benefit from timely, relevant content that enhances their shopping experience. At the same time, advertisers can reach highly targeted audiences at critical moments near the point of purchase
Achieving this type of success requires overcoming challenges around fragmented and incomplete first-party data, which can limit a retailer’s ability to organize and use their data effectively. Additionally, many RMNs lack the analytical capacity to generate customer insights, build addressable audiences, and accurately measure success. To realize the full potential of their platforms, RMNs need partners that provide complementary data, strong identity solutions, and the expertise to transform insights into actionable strategies. This allows RMNs to drive winning outcomes for themselves, marketers, and their customers.
Here are the five steps an RMN should consider when selecting the right partner.
1. Build an identity foundation
First, the right partner needs to be able to organize and clean customer data. Given the millions of customer records and data points that a retailer has, RMNs need to make sure their data is highly usable. Whether it is a known customer record or an unknown customer with incomplete data, partners should fill in missing information and connect fragmented customer records to a single profile. For example, RMNs need to know that a purchase made in-store is by the same customer who bought online. The best partners will then organize those profiles into households since targeting (and purchasing) is often done at the household level. Without a strong identity foundation future steps of segmentation, insights, audience creation, and activation will not be successful.
Experian identity
Experian’s identity solutions provide RMNs with a comprehensive and accurate view of their customers across both offline and digital environments. We clean an RMN’s first-party data and organize their customer records into households since targeting is often done at the household level and purchases are made at the household level.
Using Experian’s Offline and Digital Graphs we work with the RMN to fill in the missing information they have on their customers (e.g. name, address, phone number or digital IDs like hashed emails, mobile ad IDs, CTV IDs, Universal IDs like UID2 or ID5 IDs). This ensures that the retailers’ entire customer base can be reached – and measured – across devices and channels.
2. Segment your customers
An RMN’s ability to segment its customer base and derive insights depends on the availability and usability of their data assets – not to mention some serious analytical chops. Some RMNs will split their customers into different product segments based on what’s relevant to an advertiser. For example, a home improvement retailer may segment customers by who is buying DIY supplies versus improvement services. Other RMNs may develop custom segments from their customer data and third-party data sources, so that advertisers can personalize their marketing based on life stage, age, income level, geography, and other factors. Either approach is effective but requires working with a partner who has high quality data and deep analytical expertise to develop those segments.
Segment with Experian
Experian Marketing Data helps an RMN learn about their customer beyond their first-party data. With access to 5,000 marketing attributes, RMNs can fill in the holes in their understanding of a customer. We provide them with demographic, geographic, finance, home purchase, interests and behaviors, lifestyle, auto data and more. RMNs can use this enriched data set to create addressable audience segments.
3. Generate actionable insights about these segments
Once the RMN determines how they will segment their customers, they can utilize demographic, attitudinal, interest, and behavioral data from a trusted partner to develop a customer profile that compares its customers against a relevant sample of consumers. Here, the RMN will gain insight that will help them answer questions about its customers. Examples include:
- What age and income groups are more likely to purchase my product?
- What is the current life stage of my customers – do they have children, are they married, are they empty-nesters?
- Is price or quality more important to customers in their decision-making process?
- What sort of activities do my customers enjoy?
- How frequently do my customers shop for similar merchandise?
- What media channels do my customers use to get their information?
Expanded insights with Experian
With Experian’s advanced customer profiling, RMNs can go beyond basic customer segmentation. We build detailed customer profiles by utilizing accurate, attribute-rich consumer data, so RMNs can gain a more comprehensive understanding of their customer’s preferences, life stages, and purchasing behaviors. Having this insight enables the RMN to:
- Design a targeted email campaign promoting home essentials to recently married new homeowners.
- Develop a social media post announcing the opening of a new hardware store to users within a specific location interested in do-it-yourself products.
- Create brochures and flyers at a local community event tailored towards parents with small children that promote equipment for youth sports leagues.
4. Create high quality lookalike audiences
The RMN now knows what distinguishes their customers from other consumers and can create audiences that enable advertisers to run personalized marketing campaigns at scale. RMNs can do this in several different ways:
- Work with a data provider who can create custom audiences for the RMN (e.g., Ages 40-49 and Leisure Travelers and past purchase of travel item)
These custom audiences are created by joining multiple first- and third-party data attributes found to be significant in the customer profile or using machine learning techniques to develop a custom audience unique to the advertiser.
Custom audiences with Experian
With an enriched understanding of their customers, RMNs can create addressable custom audience segments, including lookalike audiences, for advertisers.
5. Expand addressability of audiences and activate on multiple destinations
Once audiences are created, RMNs will want to increase a marketer’s reach across on-site and off-site channels. With the right identity graph partner, an RMN can add digital identifiers to customer records that enable activation across media channels, including programmatic display, connected television (CTV), or social. RMNs should work with identity providers that are not reliant on third-party cookies. They should select partners that offer more stable digital IDs in their graph like mobile ad IDs (MAIDs), hashed emails (HEMs), CTV IDs, and universal IDs like Unified I.D. 2.0 (UID2).
Experian powers data-driven advertising through connectivity
Using Experian’s Digital Graph, RMNs expand the addressability of their audiences by assigning digital identifiers to customer records. Marketers will be able to reach an RMNs customers onsite as well as offsite since Experian provides several addressable IDs.
Audiences can be activated across an RMNs owned and operated platform as well as extended programmatically to TV and the open web through Experian’s integrations across the ecosystem.
Maximize your RMN’s revenue potential with Experian
Organizing customer data, segmenting customers, generating insights, creating addressable audiences, and activating campaigns are all critical steps for an RMN to realize that revenue potential. RMNs should select a partner that provides the data, identity, and analytical resources to create the winning formula for marketers, customers, and retailers.
Experian’s data and identity solutions are designed to help RMNs maximize their revenue potential.
Reach out to our team to discover how we can support your path to RMN success.
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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.