Why Predictive Ads May Be Doing More Harm Than Good

The Effectiveness of Predictive Advertising in Multifamily

Predictive advertising has been touted as “the next big thing” in digital marketing — promising marketers smarter targeting, better-qualified leads, and cost-effective ad spend. 

Other companies have championed the technology, claiming that predictive analytics can help forecast future renter behavior based on past data, making ad targeting more precise and impactful. But is predictive advertising really the game-changer it claims to be? Does it deliver “said results?”

After digging into recent research and analyzing industry results from the last year, the evidence suggests that the effectiveness of predictive ads, especially in multifamily marketing, may not be as clear-cut as proponents would like us to believe. 

In fact, we’ve found that more transparent strategies can often deliver better results, which is why we’ve steered away from predictive ad services.

Let’s explore why predictive ads fall short and why Criterion.B takes a more straightforward approach to multifamily marketing.

What Is Predictive Advertising?

If you have spent time working on digital ads, you know the cost has risen significantly in recent years. Meta’s cost per thousand (CPM) has increased 61% yearly, and Google’s programmatic display CPMs increased 75% in the same period, according to The Drum. Meanwhile, many brands are tightening their budgets due to economic uncertainty, yet consumers are expecting better ad targeting, personalization, and overall messaging than ever before.

So how have advertisers and agencies attempted to combat this? Well, they’ve been dabbling in predictive ads (or trying).

“We rushed into [predictive advertising], hoping for big returns but ended up spending too much with little to show for it,” said James Heartquist, owner at Modern Property Solutions. “Our main problem was not using our budget wisely, which led to few sales.”

Predictive analytics uses artificial intelligence (AI) and data from past campaigns to forecast future behaviors. In theory, it allows marketers to target potential renters before they know they’re in the market for an apartment. The idea is simple: by using data like browsing habits, previous searches, demographics, subscriptions, and demographic information, you can predict who is most likely to convert and target your ads accordingly.

Predictive advertising is a subset of predictive analytics that uses historical data, machine learning techniques, and algorithms to target audiences and optimize ad copy and media spending accurately.

In essence, predictive advertising enables brands to build highly targeted audience segments based on millions of behavioral signals and customer attributes. There are several key techniques used in predictive advertising, each offering different levels of sophistication and impact:

• Look-alike modeling: This technique identifies new users who resemble an existing customer group. It’s a simple approach often built into platforms like Facebook and LinkedIn.
• Classification modeling: Similar to look-alike modeling but with an additional layer of filtering, this method excludes users who aren’t ideal targets.
• Click-based optimization: By analyzing user intent, AI predicts the likelihood that a customer will take specific actions, such as clicking on an ad or making a purchase.
• Uplift modeling: This advanced technique predicts how an ad or intervention will affect a customer’s likelihood of purchasing, giving brands even deeper insight into campaign effectiveness.

The Predictive Ads Promise vs. Reality

Companies promote predictive advertising as a way to get more qualified multifamily leads at a lower cost. According to them, this technology helps to refine Google Ads targeting and reach potential renters earlier in their decision-making process. 

While this sounds great in theory, industry research shows the results aren’t always as successful as predicted. The goal is to create a more personalized and timely ad experience, but predictive ads can struggle with accuracy as they rely on assumptions about behavior that can be hard to validate. As multifamily experts highlight, the unpredictable nature of renter decision-making makes it difficult for these systems to consistently deliver better conversion rates.

Recent Conversion Logix data showed that while predictive ads could improve click-through rates, the conversion rates (lead-to-lease) weren’t significantly better compared to more straightforward advertising methods. 

Predictive Ads Struggle With Accuracy

One of the significant flaws with predictive ads is their reliance on historical data to forecast future behavior. Companies without access to substantial, high-quality data sets may find it challenging to create accurate predictive models.

“A primary challenge for multifamily and real estate marketers in predictive advertising is navigating its complex processes,” said Zach Shepard, principal at Braddock Investment Group Inc. “It involves collecting large amounts of data from multiple sources, analyzing it accurately, and using it to make predictions about potential customers or tenants. This can be a daunting task for those who are not familiar with data analysis and predictive modeling.”

Shepard believes many marketers may lack the necessary tools or resources to implement a successful campaign, which could result in inaccurate predictions or an ineffective strategy that does not yield the desired results.

“In some cases, the negative experience with predictive advertising may stem from unrealistic expectations,” said Shepard. “Marketers may expect immediate and drastic improvements in their marketing efforts without considering the time and effort required to collect and analyze data for accurate predictions.”

Even when sufficient data is available, predictive algorithms often overlook critical variables such as weather changes, shifting moods, or personal relationships — factors that can dramatically influence purchasing decisions in ways that a computer cannot easily anticipate. 

“One of the primary issues that marketers face with predictive advertising is inaccurate targeting,” said Robert Fausette, owner of Revival Homebuyer. “Predictive algorithms use data analysis to predict which individuals are most likely to take a certain action, such as clicking on an ad or making a purchase. However, this data is not always accurate and can lead to targeting the wrong audience. This results in wasted advertising budget and lower conversion rates.”

Multifamily Executive reported that renter behavior can be highly volatile, especially in uncertain economic times, making predictive analytics less effective. Renters’ priorities shift based on changing market conditions, economic factors, and personal circumstances that predictive algorithms struggle to account for. 

Additionally, predictive models are not static and must be updated continuously to remain relevant. As customer behavior evolves, a model that once worked may quickly become obsolete, leading to inaccurate predictions and wasted ad spend. A perfect example is the 2008 financial crisis, where models predicted mortgage customers would repay loans but failed to account for the collapse in U.S. housing prices. 

“Our biggest challenge was getting good leads that actually turn into customers,” said Dino DiNenna, real estate broker at Hilton Head Realty Sales. “We tried a big ad campaign for our fancy apartments, and while lots of people clicked, not many ended up applying. It was frustrating to see all those clicks but so few real leads.”

In multifamily marketing, relying too heavily on outdated models can result in poor targeting, ineffective ad spend, and ultimately, lower ROI. While predictive advertising offers potential, its limitations make it less reliable compared to real-time data-driven approaches that adapt to renters’ immediate needs and behaviors.

In contrast, real-time data like live pricing updates and real-time availability are far more accurate because they reflect the current state of the market. BestEverCRE found that renters are more likely to convert when presented with up-to-date, transparent information such as pricing, unit availability, and immediate offers. This real-time approach provides actionable data, allowing for a more direct path to conversion without relying on speculative forecasts.

The Case for Simplicity: Live Pricing and Availability

One of the most compelling arguments against predictive ads is the simplicity and effectiveness of using real-time data. MultifamilyBiz highlighted that real-time availability and pricing updates directly impact conversion rates because they offer renters the information they need when needed. 

By focusing on strategies that prioritize transparency — like showing what units are available right now or offering dynamic pricing — you create a more immediate sense of urgency and trust. Renters are far more likely to act when they know exactly what’s available, at what price, and when they can move in.

Ashley Gawley LRE, founder at Launch Real Estate, advises balancing data with understanding the hyperlocal market, property, and target audience. “For multifamily, look at lease-up rates, turnover, and demand for specific unit types — not just broad demographics,” Gawley said. “Consider hosting focus groups to gain qualitative insights into what’s really driving people’s housing decisions.”

Michael Yerardi, owner of Turning Point Home Buyers also faced challenges with predictive ads when the qds were not matching up with how residents felt about living at the properties. “We listened to what our residents had to say and made big changes,” Yerardi said. “For example, we promised to fix things within 24 hours, which made people much happier. By focusing on making life better for our residents — not just on flashy ads — we got 20% more people to stay with us.”

At Criterion.B, we’ve seen firsthand that simpler methods can outperform more complex, algorithm-based systems like predictive ads. Instead of trying to guess what renters might want in the future, we focus on delivering relevant, real-time information that speaks directly to their current needs.

The Fallacy of Predictability

Predictive advertising often promises that with enough data, marketers can forecast consumer behavior with precision. However, this is where the fallacy of predictability comes into play. 

The desire for predictability can lead brands to rely on data and past performance to the point of stifling creativity and innovation. While predictive tools analyze everything from location to preferences, purchases, and search history, they ultimately fail to account for the messiness of human behavior. They can’t account for the emotional and personal factors influencing renter behavior. 

Advertising has never been an exact science, nor should it aim to be. The idea that past consumer actions can reliably predict future behavior is flawed, and companies relying too heavily on predictive analytics will likely experience diminishing returns. Past performance does not predict future results. The repetition of predictable tactics inevitably leads to stagnation.

While data can provide insights, the art of advertising lies in its ability to engage, surprise, and break through the ordinary. It’s not about being predictable but about showing up in unexpected places and finding the human connection that resonates with people on a deeper level. Effective advertising is about understanding human behavior — not just the data sets it generates — and creating campaigns that reflect society’s complex, ever-changing nature. 

Brands that rely solely on predictive ads risk becoming part of the ignorable 85% of ads that go unnoticed. By rejecting the predictable and embracing creativity, companies can produce advertising that stands out, breaks norms, and becomes part of the cultural conversation.

“We invested in a campaign aimed at prospective renters based on data analytics, but the audience we reached often didn’t align with our ideal tenant profile,” said Justin Landis, founder of The Justin Landis Group. “Many of the leads we received were unqualified, leading to a low conversion rate and wasted marketing dollars. We also faced challenges with the technology itself. The predictive models sometimes failed to account for local market nuances, resulting in ads that felt generic and disconnected from our community’s needs. This misalignment made the campaign feel less personal, and potential renters often overlooked our offerings.”

“The negative experience was frustrating, as we had high hopes for improving our lead generation,” Landis continued. “To pivot, we shifted our focus from purely data-driven advertising to a more integrated approach that included community engagement and personalized messaging. We began leveraging social media to connect with potential renters directly and gather feedback.” This approach enhanced the brand image of The Justin Landis Group. It allowed the company to tailor its offerings to meet audience needs better, ultimately leading to more successful marketing outcomes.

In the multifamily space, where renters’ needs and expectations evolve quickly, it’s essential to stay agile. Predictive ads, with their focus on past data, can leave marketers blind to new opportunities or shifts in renter behavior. Instead of leaning on predictive analytics, focusing on live data, real-time updates, and human-driven insights will allow brands to remain relevant and adaptable in a constantly changing market.

“We learned the hard way that keeping up with what residents want is crucial,” said Nick Stoddard, CEO at KC Property Connection. “We once ran ads aimed at young professionals, but it flopped because we didn’t consider families looking for bigger homes. After tweaking our approach to include families, we saw a big jump in leads and a 15% increase in occupancy rates. This experience taught us that regularly updating our marketing strategies is key to staying on top of changing resident needs.”

The Human Element: Why Predictive Ads Miss the Mark

While artificial intelligence (AI) is growing in digital marketing, its use in long-form content and predictive advertising isn’t without challenges. AI tools used in predictive analytics often simplify complex behaviors into data points, missing out on the nuances of human decision-making. 

AI-driven predictive ads are not always effective in capturing the attention of renters seeking personalized experiences. Renters prefer authentic and relevant brands, which AI-driven, predictive models often struggle to achieve. Psychology Today reported that 70% of consumers spend more with authentic brands.

“The overreliance on AI in predictive advertising presents a challenge for us,” said Ryan Whitcher, founder and CEO of Harmony Home Buyers. “We initially embraced AI for our ad campaigns but soon realized it led to biased targeting and a 30% drop in qualified leads over three months. To address this, we implemented a hybrid approach, combining AI with human oversight, resulting in a 25% increase in qualified leads within two months and teaching us the importance of balancing technology with human expertise in our marketing strategies.”

Although 77% of advertisers have a favorable opinion of AI, consumer sentiment tells a different story. A 2024 report from the Edelman Trust Institute revealed that trust in AI among U.S. consumers has dropped by 15% over the past five years, declining from 50% to just 35%.

Also, a recent Neil Patel study found that content marketing incorporating personalization and emotional engagement outperforms data-driven, predictive ad strategies by 94.12%. Renters want to feel connected to the brand they’re considering, and predictive ads often fail to create that connection.

At Criterion.B, we prioritize building trust and emotional engagement through tailored content, real-time updates, and personal interactions. This approach builds lasting relationships, which is something predictive ads can’t do.

Why We Don’t Offer Predictive Ads

At Criterion.B, we’ve done our research. While predictive advertising may have its place in certain industries, the multifamily market demands a more dynamic, real-time approach. The unpredictable nature of renter behavior — combined with the increasing need for transparency and trust — means that predictive ads often fall short.

Rather than relying on speculative technology, we focus on proven methods: real-time pricing, availability updates, tailored content, and building genuine connections with renters. This approach doesn’t just drive traffic — it builds relationships. So while the allure of predictive ads is tempting, we’re committed to more personable (and reliable) strategies.