Category: Data Insights

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.

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.