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The questions that keep retail marketers up at night have evolved significantly over the last decade. It wasn’t long ago that marketers would spend their time debating which highway to place their billboard on, whether or not their next TV commercial should be comical or heart-tugging, or even what the optimal time of day was to blast an email campaign to their entire customer list. In 2024, retail marketing has new challenges on the radar.
The rise of omnichannel retailing
The modern, digital-savvy customer expects a flawless and interconnected shopping experience across touchpoints — one of the many reasons omnichannel marketing is on the rise. Research shows that over half of B2C consumers engage with between three and five channels whenever they make a purchase. For businesses, omnichannel engagement is a lucrative opportunity; McKinsey reports that customers who engage across channels shop nearly twice as much as those using a single channel and usually spend more money.
However, the rise in omnichannel engagement also presents several retail marketing challenges, such as the complexity of managing vast amounts of data and piecing together an accurate picture of consumer behavior.
Data and identity-related retail marketing challenges
Today’s data-driven environment has turned the retail marketing landscape on its head, and businesses have a whole new set of struggles that revolve around identity and data. We identified the top five retail marketing challenges and how to solve them.
1. Knowing what data to capture
In the omnichannel era, online and offline data is abundant. When a customer shops at a physical store, they create data points like:
- What items they purchased
- What time they visited
- How long they were there
When the same customer shops online, they create a whole new set of data points, such as:
- What device they used
- Which items they browsed but didn’t purchase
- How long they spent on specific pages
The vast available data can overwhelm retailers and make it a challenge to determine which data points to prioritize. Start by identifying the challenge you’re addressing. By defining your problem, you can better decide which data is most relevant. For instance, if you’re optimizing the timing of incentives, analyze when customers shop most frequently and customize offers based on individual behavior patterns.
How Experian’s Activity Feed can help
Experian Activity Feed connects online and offline data to promote precise targeting and measurement across mobile, web, connected TV (CTV), and more. We provide addressable insights that work across all channels by integrating real-time device IDs, cookies, and IP addresses. Our case study with Cuebiq, found here, discusses how we used Activity Feed to deliver in-store lift analyses to Cuebiq’s clients.
Because our impressive breadth of addressable data works across channels, we’re perfectly positioned to be your comprehensive identity solution, as we’re capable of addressing the entire U.S. population. With access to over 250 behavioral and demographic attributes per individual, our data fills in audience gaps to help you create a complete customer profile.
2. Understanding customer behavior
The complexity of modern consumer behavior is growing, and one of the biggest retail marketing challenges is merging all this information into a single unified customer view. With consumers moving seamlessly between devices like tablets, mobile phones, and laptops, retailers face the grueling task of keeping up with their fragmented journey.
For instance, a customer may spot a pair of shoes in-store, add them to their cart via mobile due to long cashier lines, and finish the purchase later from their laptop. However, if they cannot be recognized across these touchpoints, they may abandon the purchase out of frustration. Retailers need solutions that link offline customer relationship management (CRM) and purchase data with a customer’s online activity, regardless of channel or device.
This is where Experian identity resolution and Graph come into play.
How Experian’s Graph and identity resolution can help
Experian’s identity solutions help brands resolve disparate data by merging fragmented identifiers into a singular customer profile for a 360-degree view. We ensure each touchpoint is connected, whether the interaction happens online or offline, across mobile apps, or in-store. This enables retailers to recognize the same customer across various devices and enhances the customer experience by keeping items in their cart and personalizing their journey across platforms.
With Experian’s identity graph, brands can further enrich these customer profiles with digital identifiers that span hashed emails (HEMs), cookies, mobile device IDs (MAIDs), IP addresses, universal IDs, and CTV IDs to create a more accurate, actionable view of consumer behavior. We rebuild the graph weekly, which ensures persistent and refreshed connections between households, individuals, and their devices. This ongoing linkage allows for precise targeting and measurement over time and aligns with privacy standards and compliance obligations.
By organizing identity into households and device IDs and enriching them with marketing data, brands can gain deeper customer insights, addressability across devices, and the ability to measure the impact of their retail marketing strategies.
3. Building trust between consumers and your brand
Trust is the foundation of online relationships, and consumers who trust your brand are likelier to share their data. To establish this trust, retailers must collect customer data transparently and respectfully.
According to Experian data, 80% of consumers believe more transparency around the use of their information fosters greater trust in a business. Additionally, the same data revealed that 56% of companies plan to invest more in transparency initiatives, such as consumer education, clearer terms of communication, and consumer control over personal data.
Experian’s commitment to data accuracy and transparency further strengthens this trust. Our data is ranked #1 in accuracy by Truthset, which means you can power smarter insights, targeting, and measurement using the highest-rated, most reliable data to build customer profiles.
4. Establishing customer loyalty with retail marketing
Today’s consumer has many opportunities and choices available at their fingertips, which makes it harder for retailers to build and maintain customer loyalty. Signal loss and the rise of omnichannel media consumption have made it even more of a challenge to keep loyal customers.
By using data and insights to interact with people more meaningfully, you can overcome these difficulties to provide a more personalized, relevant experience and establish loyalty. Experian’s new Digital Graph and Marketing Attributes solution makes it easier to do just that.
Experian’s Digital Graph and Marketing Attributes solution
Using our Digital Graph and Marketing Attributes, you can gain comprehensive insights into consumer behavior by combining offline and digital data through our Living Unit ID (LUID). Our Digital Graph provides robust digital identifiers like MAIDs, CTV IDs, HEMs, and universal IDs, while our Marketing Attributes offer detailed consumer insights spanning age, gender, purchase behaviors, and content consumption habits. With this data, you can create relevant messaging and informed audience segmentation to enhance your personalization and targeting efforts across all digital channels.
Using our solution can help you deliver what customers need when they need it — like winter gear before a ski trip or swimwear before a beach vacation. These personalized experiences drive additional revenue and build lasting relationships that keep customers coming back, establishing a strong foundation of loyalty in an increasingly competitive market.
5. Finding your technology solution
Retailers need to integrate technology to make their data actionable and use it to streamline the customer experience. They need to integrate data storage platforms with fulfillment and reporting solutions, such as email service providers, display networks, and marketing intelligence tools. Whether retailers are exploring the industry or gearing up to make a substantial investment in the right technology partner, it’s vital to ensure you evaluate potential partners equally and consistently.
Experian works with major platforms, marketers, and agencies, meaning we have existing partnerships across the ecosystem for you to connect with that can bring your consumer data to life and meet your needs. Our offline and digital graphs are baked into partner integrations so customers can achieve higher match rates that improve addressability.
Strategies to help you overcome retail marketing challenges
When it comes to modern retail marketing, you’ll need to take a strategic approach to handle emerging challenges. Here are five retail trends of 2024 to consider integrating into your retail marketing strategy:
- Use predictive analytics: Data can be overwhelming, but you can analyze historical purchase patterns to capture and prioritize the most relevant data for your retail marketing efforts.
- Optimize omnichannel campaigns: Cross-channel data integration can help you ensure consistent messaging, provide a seamless experience, capture a unified view of customer interactions, and improve engagement.
- Personalize experiences with AI: Utilize AI data capture across touchpoints to create personalized recommendations and tailored experiences that resonate with individual customer preferences and behaviors.
- Adopt dynamic pricing: Use real-time data to adjust prices based on customer behavior and market conditions so your pricing strategies align with current demand and maximize revenue.
- Invest in customer experience tech: Virtual fitting rooms, augmented reality, and other advanced technologies allow customers to engage with your brand across platforms, which can improve their shopping experience.
Utilize Experian’s retail media network (RMN) solution
Experian’s solution for RMNs is another tool for overcoming retail marketing challenges. We empower RMNs to better understand their customers with unified views of online and offline behavior across channels and extend their reach across environments.
Using our top-ranked identity and audience services, we can help RMNs access expanded customer insights, enhance cross-channel audience targeting, and improve real-time measurement and attribution to enable precise, streamlined, personalized omnichannel campaigns. Our solution’s integration with major platforms improves data match rates and addressability so retailers can overcome data fragmentation and optimize their retail marketing strategies.
Experian can help advance your retail marketing strategies
Experian can help retailers effectively use data and insights to interact with customers and prospects meaningfully. Our data and identity solutions help you deliver relevant, impactful messaging to ensure the customer who puts shoes in the cart at the store is the same customer who wants to finalize their transaction later that evening online.
As the holiday season approaches, it’s time to refine your retail marketing strategies and connect authentically with shoppers. With over a billion consumers preparing to shop, Experian offers 19 new syndicated audiences available for activation across major ad platforms, including TV and programmatic, to help you reach the most relevant prospects. Whether you’re targeting discount seekers, last-minute gift-buyers, or frequent travelers, our audiences align with diverse shopping styles and preferences. Choosing the right audience segments aligns your holiday marketing efforts with consumer expectations and maximizes impact.
With our tools, you can seamlessly connect with the same customer across various channels, whether they’re shopping in-store or online. Embrace the holiday season confidently, and let Experian help your retail marketing strategy shine.
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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.