How AI is Transforming Contextual Targeting

AI is revolutionizing the way ads are matched with online content, transforming contextual targeting from basic keyword matching to a sophisticated understanding of user intent and content relevance. This evolution highlights the shift towards more dynamic, personalized advertising strategies that leverage AI to enhance privacy and precision in reaching audiences.

Executive Summary:

  • At the onset of contextual targeting, the human-led analysis of keywords was mired with campaign scaling issues and imprecise targeting due to the lack of semantic signals resulting in engagement with users in an irrelevant context. (source)
  • With advancements in AI technology, namely natural language processing, contextual targeting has become a precise and privacy-centric targeting solution.
  • Identity deprecation has driven users to not only want, but expect personalized brand engagement. Contextual targeting can create a tailored one-to-one experience by seamlessly integrating brand messaging with the content users are actively engaging with.
  • Contextual 2.0 enables content analysis beyond text-based signals – AI can now efficiently analyze the content and context of video, audio, and images, and metadata in real-time. Advancements in natural language processing improve the accuracy of contextual targeting through improvements in sentiment, semantics, and theme analysis.
  • The future of contextual will see continuous improvements in semantic analysis accuracy and unprecedented scalability, especially as marketers increasingly incorporate contextual targeting as a core element of their brand strategies amidst cookie deprecation.

LinkedIn is Removing Lookalike Audiences: What Marketers Need to Know

Starting February 29, 2024, you will no longer be able to create new lookalike audiences or refresh and edit current lookalike audiences. Lookalike audiences were designed to help expand audience reach by targeting individuals with similar characteristics to the base audience. Active lookalike audiences will continue to deliver as they appear until the end of February. After 30 days, if a lookalike audience is not active in a campaign, it will be archived.  

LinkedIn has launched predictive audiences, a new tool designed to enhance your campaign’s effectiveness by offering:

  • The ability to reach perfect-fit prospects by matching them with your existing customer base’s characteristics
  • AI-powered precision to identify high-intent buyers most likely to convert.
  • An effortless setup by simply combining your data with LinkedIn’s algorithm to automatically generate a custom audience tailored to your needs.
  • Lead generation gold: campaigns see a significant 21% reduction in cost-per-lead.

So what is the difference between the two? Lookalike audiences rely on historical data to mirror past behaviors of customers, assuming these behaviors will persist. In contrast, predictive audiences use AI to forecast future actions based on recent interactions, like website activity and email engagement, allowing for more tailored and anticipatory campaign personalization. This forward-looking approach enhances the experience by accurately predicting customer interests and potential actions. 

Practical Use

Predictive audiences can be created within the audience tab, found in the same location as lookalike audiences. After you name your audience you’ll be able to choose the source. All predictive audience sources require a minimum number of 300 members. Lastly, you’ll choose the location and size of the audience.

Options for expanding LinkedIn audiences in the future:

  • Predictive Audiences – expand your campaign’s reach by creating an audience that mirrors the traits of your existing data source who are more likely to convert.
    • Create a more targeted audience with minimal data sources, focusing exclusively on LinkedIn-specific inputs such as lead gen forms, contact lists, or conversions through the Insight Tag.
  • Audience Expansion: Target groups that share key characteristics with your primary audience, broadening your reach while maintaining relevance.
    • Attributes specifically excluded from your target audience will not be included in audience expansion.
    • When creating the campaign’s targeting, the audience count will not include members from audience expansion.
    • Audience expansion is not available when predictive audiences are selected.
  • LinkedIn Audience Network – deliver ads beyond the LinkedIn feed to members on third-party apps and sites.
    • The same targeting parameters, bid type, and budget created for your campaign applies.
    • This is only available for the following ad formats: single image, carousel, document, and video ads.

Social Commerce & The Future of Social Shopping

The big picture: The concept of social commerce took root during the early days of eCommerce. But it was the mobile revolution, plus the meteoric rise of social media titans like Facebook, Instagram, and Pinterest, that enabled this approach to truly disrupt digital shopping.  

Social commerce focuses on convenience and relationship-building, tapping into unplanned discovery moments as consumers scroll through inspirational content. Watching real people interact with products helps shoppers understand and visualize them better, building confidence around online purchases.

For marketers, this shift toward social platforms reflects new expectations set by Gen Z and millennial shoppers who increasingly make purchases directly via social apps. It is an opportunity to maximize reach and nurture lasting brand relationships by organically integrating into the customer journey. Platforms now orchestrate a seamless, trust-based shopping experience where inspiration can instantly lead to purchase.

Why it matters: Despite expectations that eCommerce will surpass 8 trillion dollars by 2027, consumers are becoming increasingly wary of how much of their personal data they share with online marketplaces and brands. Marketers may accidentally push these hesitant consumers further away by pushing hard-sell messaging to audiences who are still considering their product. These tactics are the online equivalent of the over-eager salesperson peppering you with corporate scripts about deals and asking you to open a store credit card when all you were looking to do was casually browse. They’re annoying. 

But casual browsing presents an excellent opportunity for brands to humanize their eCommerce messaging. This shift toward Social Commerce allows for the ease of purchase and speed of online shopping while giving consumers the chance to window shop again. 

How it works: The goal is to remove friction when interest strikes while also organically introducing additional products based on what originally caught their eye.

  • For example, when a consumer admires a product shown by an influencer they trust, social commerce allows them to seamlessly find that item or brand’s storefront to browse and buy. They will be able to find those glasses and discover a range of other items, enhancing their shopping experience. 

Done well, the experience guides consumers through an intuitive path to purchase via content they already enjoy. This allows brands to inspire consideration and visibility of products in authentic contexts rather than disruptive ads. Social commerce puts the shopper first – their organic journey dictates the path, not predetermined funnels.

How to start:

  • Ensure your brand has ‘virtual storefronts’ across online marketplaces and social media platforms, keeping in mind that the virtual aesthetic is just as important as physical store décor. 
  • Create natively social, shoppable content and tap influencers who have an authentic tie to your brand. Small influencers are essential: the recommendations of these content creators drive 86% of purchases — just as strong as recommendations from a trusted real-life friend. 
  • Explore how to repurpose authentic user-generated content (UGC) in your marketing materials. The goal is to facilitate organic community engagement and discovery around your brand versus hard-selling. 

The bottom line: The future of brand loyalty lies in shoppable communities, where shared values and experiences fuel organic amplification. Social content creates better browsing experiences, allowing consumers to discover items at their own pace before being hit with hard-sell messaging. Social Commerce provides an authentic, engaging discovery experience — adding the human touch back to your eComm strategies.

Say No to Lazy Data – How Everyone Can Be Part of the Solution

What is Lazy Data?

Picture this: a chaotic jumble of inconsistent formats, messy categories, and a general disregard for order. Lazy data isn’t just data that prefers lounging on the couch to hitting the gym; it’s the unruly teenager of your business – wild, rebellious, and refuses to conform to a standard.

This laid-back attitude is the root of the problem – a lack of discipline that prevents your data from realizing its full potential. Lazy data isn’t a solitary problem; it triggers a domino effect. One inconsistent entry leads to confusion in reporting, which, in turn, affects decision-making.

Why Did Data Get Lazy?

In the early days of digital marketing, the ease of connecting systems with just a click led to a somewhat chaotic scenario. Imagine the digital landscape as the Wild West, with data flowing freely and connecting without much oversight. While convenient, this Wild West approach allowed lazy data to thrive, as there were minimal checks and balances. The ability to effortlessly connect systems created a false sense of accuracy, like linking two puzzle pieces together without ensuring they fit. 

Inconsistent data entry, neglecting updates, and failing to recognize the importance of standardized schemas have allowed lazy data to infiltrate our systems. The result? Confusion, inefficiency, and missed opportunities.

As privacy regulations tightened, especially in the wake of concerns surrounding user data, businesses realized they could no longer afford a laissez-faire attitude. The emergence of data clean rooms and the imperative for strict naming convention compliance, essential for enabling tools like marketing mix modeling, forced a reckoning. Lazy data, accustomed to a more carefree existence, suddenly found itself in an environment that demanded structure and order.

While the concept of standardized schemas might initially seem bureaucratic, they are, in fact, the essential foundation for ensuring data usability. Consider it as akin to a musical performance without a conductor; in such a scenario, each instrument plays its own tune, leading to dissonance.

How to Solve  

Data should not be viewed as isolated islands or individual pieces. Instead, envision your organization as a harmonious orchestra led by a master conductor. This fresh perspective brings forth a unified approach, ensuring that every fragment of data performs its role seamlessly.

View Your Business as a System: Don’t think of your business data in silos or individual platforms. Instead, adopt a holistic approach, viewing your organization as an interconnected system – an orchestra led by a master conductor. This perspective helps in creating a seamless flow of information and makes sure that every piece of data plays its part.

Embrace the Standard: Establish a standard data schema across your business. Think of it as giving your data a set of rules to live by – no more rebels without a cause – every piece of data should know its role and play it well.

Audit to Insure: Regular audits are the insurance policy for your data’s health. Ensure that the established standards are being followed and identify any areas that might need a little extra TLC. An audit is like a health checkup for your data – catch potential issues before they become major problems.

Mastering the Cookieless Future: Leverage Retail Data for Unmatched Customer Engagement

In the cookieless marketing era, the decline of third-party cookies opens new doors for marketers to leverage retail data, transforming marketing with personalized, engagement-driven messaging. This shift to analyzing customer behaviors and preferences via retail data not only enhances campaign effectiveness and loyalty but also fuels predictive analytics for strategic planning in product development and inventory. As traditional reliance on third-party cookies becomes obsolete, retail data stands out as a vital tool for building deeper customer relationships and driving business success

First-party retail data guides the way for gaining strong insights into customer behaviors and preferences, which are vital for enhancing personalization and developing tailored loyalty programs. Its use breaks through the limitations of traditional marketing, allowing businesses to understand customer segments more profoundly and anticipate future trends. This level of insight is critical for crafting effective, personalized marketing strategies, shifting the focus from mere transactional interactions to building lasting, data-driven customer relationships. In a cookieless future, leveraging retail data is key to staying ahead in areas like product development and inventory management, ensuring that brands not only adapt but thrive in the new marketing landscape.

The strategic utilization of retail data is crucial, going beyond merely recognizing its value. This is supported by a national survey from The Trade Desk Intelligence, which shows 91% of U.S. advertisers plan to maintain or increase their investment in retail data, to drive overall business success.

Unlocking the Power of Retail Data: How Enhanced Insights Lead to Personalized Marketing Triumphs

In today’s marketing world, retail data is crucial for creating highly personalized campaigns that truly understand customer desires and behaviors. It involves deep analysis of various data points, from purchasing habits to browsing patterns, offering a comprehensive view of customer preferences and behaviors.

This depth of insight allows for more than just tailored marketing messages; it enables behavioral and interest targeting. Marketers can segment audiences based on their actions, such as frequent purchases of a particular product category or regular browsing of certain content. Similarly, interest targeting becomes more refined through analyzing data points like search queries, product views, and content interactions. This approach ensures that marketing efforts are not just personalized but also highly relevant to each customer’s unique interests and behaviors, leading to more effective campaigns and a stronger connection with the audience.

This approach results in marketing that feels more like a personalized conversation than a generic broadcast. This tailored strategy leads to higher engagement and conversion rates, as customers encounter content that aligns closely with their individual experiences and needs, striking a chord that transcends mere attention-grabbing. This marks a fundamental shift in how marketers connect with their audience, paving the way for campaigns that are deeply personal and rooted in a thorough understanding of customer behavior.

Forecasting the Future: How Enhanced Predictive Analytics Shape Strategic Marketing and Decision-Making

Armed with a wealth of data like historical purchases and customer interactions, marketers are not just looking at what’s happening now. The spotlight is now on retail data, especially when it comes to predictive analytics. They’re using this data to power models that predict what customers might want next. It gives marketers a peek into future consumer trends and behaviors based on solid, data-driven insights from the past.

Marketers build predictive models on the concrete foundation of past customer data to anticipate shifts in consumer preferences and market dynamics. It’s about staying one step ahead in the game. Whether it’s product development or inventory management, predictive analytics provide invaluable foresight. Marketers can now predict emerging customer needs, ensuring that new products hit the mark and inventory levels are just right – no more guessing games.

Personalized marketing, empowered by predictive analytics, revolutionizes marketing by tailoring efforts to future customer needs, enhancing relevance and engagement. This strategy not only improves marketing effectiveness but also bolsters customer loyalty and guides SEO content strategies to align with future trends. Essentially, leveraging predictive analytics with first-party data equips marketers to create more impactful, forward-looking marketing strategies.

Data-Driven Bonds: Transforming Retail Data into Lasting Customer Loyalty and Trust

Retail data is driving the creation of deeper, more impactful customer relationships. ‘Data-Driven Bonds’ represents a strategic revolution, where retail data is not just supportive but central to forging enduring loyalty and trust with customers.

At the heart of this strategy is enhancing customer experiences through the insights gathered from first-party data, ranging from purchase history to direct feedback. This approach is more than just habit tracking; it’s a deep understanding of individual customer preferences, thereby establishing a foundation of trust and strengthening brand loyalty.

The strategy goes beyond traditional, one-size-fits-all loyalty programs to personalized experiences aligned with each customer’s unique needs. This personalization elevates loyalty programs from transactional tools to key components of the customer journey, enhancing repeat business and fostering long-term loyalty.

Embracing the Future: Navigating the Cookieless Landscape with Retail Data

Retail data opens up a treasure trove of opportunities for deeper customer insights, personalized experiences, and predictive analytics. This shift is a strategic evolution, positioning brands to not just understand but also anticipate customer needs, forging stronger, trust-based connections.

In this new era of marketing, customer-centric strategies define the approach, with personalization and predictive insights actively driving engagement and loyalty. As we navigate this cookieless landscape, leveraging first-party data is not just about adapting. Marketers need to transform every customer interaction into deeper engagement and lasting loyalty. The future of marketing lies in creating not just transactions, but meaningful relationships powered by data-driven insights.

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