As a marketer, you know that the digital landscape is always changing. That’s why it’s important to make sure you’re equipped with the right tools every step of the way – no matter how rapidly things change. You want to ensure your strategies and tactics stay ahead of any changes in technology or consumer behavior, so what new marketing strategies should be in your toolbox in 2023?
Discover what industry leaders from Experian, Adweek, FreeWheel, Tubi, and Instacart had to say about what should be in every marketer’s toolbox in 2023 at Cannes.
Keep reading to learn the top four new marketing strategies you need in your marketing toolbox for 2023 and beyond.
1. A plan for signal loss
The first item you should have in your marketing toolbox is a plan for signal loss.
The phasing out of third-party cookies presents both a challenge and an opportunity. This shift not only poses challenges but also opens up opportunities for alternative strategies.
On the one hand, it makes it more difficult to track users across channels and measure the effectiveness of marketing campaigns. On the other hand, it forces marketers to focus on building relationships with their customers and collecting first-party data.
Consumer behavior is changing
When we consider signal loss in a traditional sense, we think of the implementation of iOS 14, where we couldn’t track click-based data from campaigns. It’s important to reflect on the fact that the paid media ecosystem needed to adapt to new consumer realities.
Younger demographics are less likely to click on ads and instead engage in video environments. They discover brands through platforms like TikTok or Instagram. It’s crucial to understand how people behave, where they discover products, and where influence takes place. This understanding becomes even more vital when targeting a young audience demographic.
Four things to consider when planning for signal loss
There are four things you should consider when building out a plan to address signal loss and fragmentation.
Channel diversification
You need to reach your customers on the channels where they are already spending time, such as social media, email, and your own website. You should work with platforms that have first-party data to understand how your customers interact with your brand.
Data privacy
You need to be transparent about how you are collecting and using customer data. You should also anonymize data whenever possible.
First-party data
First-party data is now more crucial than ever, awakening its importance in shaping our actions. The combination of channel diversification and first-party data will be essential in the years to come. By focusing on these two areas, you can build stronger customer relationships and create more effective marketing campaigns.
Contextual targeting
Contextual targeting is emerging as a viable method to deliver more relevant content to your intended audience.
By embracing signal loss, the alternative new marketing strategies that are emerging as a result, and adopting a privacy-centric mindset, you can navigate cookie deprecation.
2. Collaboration
The second item you should have in your marketing toolbox is collaboration within the AdTech ecosystem.
To address signal loss and changes in privacy, moving toward a more collaborative, holistic marketing ecosystem is key.
Two ways we can achieve better collaboration
Here are two ways we can create better collaboration in the AdTech ecosystem.
Enable interoperability
We should aim to create an ecosystem that fosters collaboration between marketers, publishers, advertisers, ad tech companies, and more. When we enable seamless interoperability, everyone can use the best data available.
Use clean rooms
We are witnessing a growing trend of collaboration between parties, where buyers and sellers share data in these secure environments. Clean rooms can help us develop data strategies in a controlled manner.
3. Generative artificial intelligence (AI)
The third tool you should have in your marketing toolbox is generative AI.
Benefits of implementing AI
There are three main benefits to implementing AI within your marketing strategy.
Enables creativity
Although AI and machine learning have long been part of our toolbox, this moment marks an extraordinary acceleration that expands our capabilities. Copywriters can now create visuals, and art directors can write compelling copy. It’s an extension of what we’re capable of, potentially alleviating the burden of repetitive tasks and enabling more time for collaboration, creativity, and strategic thinking.
By embracing generative AI, we can preserve valuable talent, prevent burnout, and invigorate the advertising industry.
Enables more personalization
The rise of personalization with AI has significantly increased the demand for tailored experiences. People now willingly allow AI agents to read their emails, hoping for quicker and easier responses. This shift signifies a change in the previous emphasis on privacy and consumer preferences. Consumers now see the value in exchanging personal information for more targeted services.
E-commerce has already witnessed this transformation with customized ads based on individual preferences and behaviors. For instance, if a CPG brand notices you’re not purchasing meat, they won’t serve you ads for meat products.
However, it’s crucial to strike the right balance between being useful and intrusive. Users want relevant information that aligns with their needs without feeling intruded upon.
As we navigate this path, we must ensure that personalization remains beneficial and respectful of user preferences.
Helps drive impactful results and customer satisfaction
The tool is a perfect analogy for improving your job performance and business operations. Having the right data input to feed the machine is crucial, just like using the right ingredients to cook a perfect meal. Keeping the consumer in mind throughout the process is key. You can ensure customer satisfaction by putting the right ingredients in and allowing the machine to work its magic. Scaling up, repeating, and refining the process will drive impactful results.
4. First-party data
The fourth item you should have in your marketing toolbox is first-party data.
Benefits of implementing a first-party data strategy
Moving from a third-party cookie world to a first-party cookie world brings about significant transformation. Here are two benefits of implementing a first-party data strategy.
Greater accuracy
The shift to first-party cookies ensures greater accuracy, enabling us to establish critical mass through secure partnerships. This empowers us to strengthen and refine our personalization capabilities, much like Amazon’s ability to anticipate customer needs before they arise. When you can predict and understand customer behaviors with remarkable precision, you can reach your customers with tailored and creative ads.
“Building a robust first-party data strategy should be a central discussion for marketers, involving key stakeholders such as CEOs and CMOs. Quality and precise data are paramount, and while first-party relationships with consumers form the foundation, even established brands benefit from strategic partnerships. Together, we can unlock the potential of accurate and meaningful data-driven marketing.”
jeremy hlavacek, cco, experian
Identify high-growth audiences
First-party data can help you identify audiences with the greatest growth potential, ultimately optimizing marketing dollars for greater efficiency.
Watch our Cannes panel for more new marketing strategies for 2023
We hosted a panel with Adweek in Cannes that covered what should be in every marketer’s toolbox this year. Check out the full recording below to hear from leaders at Tubi, Freewheel, Instacart, Adweek, and Experian.
Check out more Cannes content:
- Our key takeaways from Cannes Lions 2023
- Insights from a first-time attendee
- Exploring the opportunities in streaming TV advertising
- The future of identity in cookieless advertising
- Maximize ad targeting with supply-side advertising
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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.
Mother’s Day may not exactly be right around the corner, but the time to send your Mother’s Day emails sure is! Based on our analysis of 186 brands that sent Mother’s Day mailings in 2013, 75 percent of email volume and 80 percent of email-generated revenue occurred between May 1st and Mother’s Day (May 12, 2013). The highest revenue-producing days were five days before the holiday (Wednesday, May 8, 2013) and Mother’s Day itself. This year, the sentimental holiday falls one day sooner than last year (May 11), but you still have more than enough time consider these quick tips for easy wins while planning and executing your campaigns. Tip 1: Give them what they’re searching for Last year, online searches five weeks before Mother’s Day were dominated by searches for the date of the holiday. As such, we recommend including the date of Mother’s Day in your email subject lines, particularly those early in the season, when customers are searching online for, and opening emails with, that information. Tip 2: Set the tone early with your subject lines A sample of early season subject lines that outperformed the overall unique open rate included: Remember Mom on Mother’s Day, May 12 Get a head start on Mother’s Day (plus a gift for you) Just arrived: Mother’s Day Gift Sets To Mother, With Love Tip 3: When it comes to timing, it’s the thought that counts Think through the timing of your emails depending on order delivery deadlines. On May 8th of last year, the largest revenue producers for email were orders for flowers and gifts placed in time to be delivered by Mother’s Day. Email subject lines on May 8th included reminders of the delivery deadlines: Last Chance: Free Shipping/No Service Charge for Mother’s Day! ENDS TODAY: Enjoy Complimentary Second Day Delivery in Time for Mother’s Day Tip 4: Let them treat themselves On Mother’s Day, the top email revenue generators were “self-gifting” (treat yourself on Mother’s Day only), Mother’s Day online sales and free shipping, as well as e-gift cards: Free Shipping Today Only! Happy Mother’s Day You deserve a treat yourself! HAPPY MOTHER’S DAY! Treat yourself to 30% off today only Last Chance: eGift Cards in Time for Mother’s Day Other email performance highlights: Note: All email performance highlights are based on comparisons to Mother’s Day mailings without the highlighted feature from matched brands. To all those in the midst of Mother’s Day campaign planning, good luck and happy sending!