As marketers embrace the age of AI, a new way to identify prospective customers has emerged. Predictive analytics goes beyond educated guesses to identify and evaluate customer patterns, interactions, and demographic details to predict user behavior.
If there’s one thing AI does well, it’s to recognize patterns quickly, with great depth and accuracy. By leveraging AI to create predictive audiences, brands can scale their performance marketing results beyond what was previously possible through other tactics.
What Are Predictive Audiences?
Predictive audiences are groups of people online that AI algorithms identify as likely to take the same actions as your current customers. That is, AI uses past marketing data to predict future (or, in the case of loyalty and retention campaigns, repeat) customers.
How Do Predictive Audiences Work?
Once marketers collect data on their current customers, AI algorithms, powered by machine learning models, identify audiences likely to behave the same way. This enables marketers to reach their best prospects through the channels where they are most likely to take action, whether that’s on social media platforms, through search, or on the Web. Predictive audience models can also function on e-commerce sites to identify upselling or cross-selling opportunities to foster customer loyalty.
How Do Predictive Audiences Differ from Lookalike Models?
As recently as two years ago, marketers touted the advantages of “lookalike” audiences, or the ability to create a new target audience based on shared characteristics of past or current customers. This marketing strategy required a significant leap of faith, though, and a huge assumption that people with similar characteristics or in similar demographics would all show interest in the same brands, products, and content.
Predictive audiences take the guesswork out of performance marketing, as they focus on user intent — that is, making predictions based on customer behavior, rather than simply demographics — to attract the customers most likely to convert. Dialing into these audiences can help marketers place the right creative on the right platform at the right time.
Why Are Predictive Analytics Important?
Consumers are becoming less willing to share data with brands or platforms, according to the latest Coveo Commerce Relevance Report, so marketers must rely more on first-party data, while extracting more knowledge out of whatever data they collect. Using AI analysis to create predictive audiences speaks to that pain point by delivering more accurate results than lookalike audiences.
According to the Coveo report, 53% of consumers are willing to share data in exchange for better deals and offers, while 58% said they are “happy to share data when shopping online with brands I trust.” That element of trust makes it easier to collect first-party data from your loyal customers, rather than trying to glean insights from other sources.
5 Ways Predictive Audiences Will Grow Your Performance Marketing Results
Predictive audiences can help at any stage of the sales funnel, but the results show incredible impact in the performance stages, i.e., consideration and action. Here are five reasons predictive analysis stands out — and helps your brand stand out.
1. Hyper-Personalized Messaging = Better Results
Have you ever read an article, seen a TikTok video, or even looked at a graphic with a caption and thought, “That sounds just like me!” How did it make you feel? The creative may resonate since it calls out your biggest problems or fears, makes you laugh because you realize you’re not alone in the way you think, or just reminds you of either struggles or good times.
With predictive analysis, your creative team can generate that type of content and deliver it seamlessly to the exact audience who will “get” it. This increases your ROI and ROAS through higher conversion rates. Plus, it can help your brand form a deeper bond with your target audience.
2. Optimized Channels
Impactful creative is just step one in an effective performance marketing campaign. Predictive analytics can also help you identify where you will find your target audience — and when. Whether you’re running Google or Facebook ads or reaching out through channels across the open web with no restrictions, predictive analytics can help you narrow down parameters to reduce ad spend and get more bang for your buck.
Since predictive models can factor in seasonality and market trends that lookalike campaigns neglect, you can proactively shift ad dollars to channels that will have the most impact before your ROI starts diminishing.
3. Upsells and Cross-sells Bring in More Revenue Per Ad Spend
Predictive analytics can show customers the right companion products when they’re ready to purchase. This can increase the average order value. Impactful ads on the right platforms, along with relevant deals or discounts, also drive new customers back to your website to build an ongoing relationship.
Nearly three quarters (73%) of customers say they expect better personalization as technology has advanced, according to research from Salesforce. Predictive analytics provides the tool to meet those expectations.
4. Higher Win-Back Rates
Using predictive analytics in your performance marketing campaigns can also build customer loyalty to reduce churn rates. When ad creative across multiple channels, emails, or SMS recommendations indicate you understand the buyer’s needs, it strengthens the relationship, leading to repeat customers, even if they’ve already been eyeing your competitors for alternatives to your solution or products.
5. Smarter Budget Allocation
When you’re using technology that predicts a user’s intent, rather than focusing exclusively on lookalike modeling, your budget dollars go further. Predictive analytics can reduce ad spend at the beginning of a campaign when you’re testing, to ascertain which creative and which platforms yield the best results. It also shortens the sales funnel by reaching audiences most likely to convert.
Key Takeaways
If you can use artificial intelligence and data analytics to achieve more accurate ad targeting, why wouldn’t you? Predictive targeting takes marketers beyond lookalike audiences by using machine learning to determine which users on a platform are most likely to act on your creative.
Predictive analysis doesn’t just guess that a user with interests or characteristics the same as your current customers might convert: It uses extensive data to anticipate user actions, yielding better results. Marketers can use predictive audiences to create more engaging, personalized content, reach customers through the right channels at the right moments, and improve customer satisfaction.
Frequently Asked Questions (FAQs)
What is predictive targeting?
Predictive targeting using artificial intelligence, machine learning, modeling, and audience data to determine customer intent.
What tools in the market enable advertisers to use predictive audience models?
Large Language Models (LLMs), machine learning, big data analytics, and advanced AI algorithms combine to help advertisers create and deploy predictive audience models. Tools like Realize, for example, simplify performance marketing for advertisers through advanced technology.
Advanced AI capabilities for precise targeting, engagement optimization, and budget simulation, to maximize advertiser ROI. realize:
What is the AI tool for prediction?
There are several tools available to marketers, including the performance marketing platform Realize, which uses advanced AI algorithms based on nearly two decades of first-party data for predictive analytics, helping marketers place ads where they’ll have the most impact — on social platforms, in SERPs, and across the open web.
