Predictive Leasing: The Next Frontier in Multifamily Marketing

For multifamily and senior living marketing managers, the stakes have never been higher. Budgets are tighter, competition is growing, and the pressure to fill units faster means that every marketing dollar must be spent wisely. Yet, many marketing strategies overlook the full journey a prospect takes before converting, leading to misallocated resources and missed opportunities.

This is where tools like Unified Attribution and Predictive Leasing can make a real difference. By using predictive AI, innovative analytics solutions like Predictive Leasing can enable you to understand which strategies are most likely to result in conversions and help you decide where to focus your budget for maximum impact.

The Costly Mistake of Incomplete Marketing Attribution

A common pitfall many marketers fall into is focusing too much on channels that appear at the end of the customer journey while underinvesting in brand awareness channels that shape early-stage decisions. The challenge with traditional attribution models is that they often fail to credit those initial touchpoints, leading marketers to double down on what seems to work at the final stage—while ignoring the vital role that earlier interactions play.

“What many marketers don’t realize is that the channels they trust most might not be delivering the results they think,” says Martin Stein, Chief Analytics Officer at Conversion Logix. “If you’re only looking at last-touch channels—the final point before conversion—you’re missing the reality of how modern consumers make decisions. Today’s prospects engage with multiple touchpoints across various channels before making a decision, and if you’re not tracking those interactions, you’re leaving crucial insights on the table.”

“If you’re only looking at last-touch channels—the final point before conversion—you’re missing the reality of how modern consumers make decisions.”

Martin Stein, Chief Analytics Officer at Conversion Logix

In our previous blog post, we explored the importance of making data-driven decisions and the need for better attribution data to provide a solid foundation for marketing strategies. We introduced the idea that many marketers today rely on flawed attribution data, often missing the bigger picture when it comes to measuring the true impact of their campaigns. 

That’s why at Conversion Logix, we’re building Unified Attribution into our soon-to-be-released marketing operating system to give you the data you need to make better decisions. With a better understanding of how each marketing channel contributes to conversions, Predictive Leasing goes a step further by helping you forecast future outcomes. As Stein explains, “We’re gaining visibility into the future— identifying the key touchpoints that are most likely to lead to leases, so you can allocate your budget where it truly matters.”

From Data to Prediction: How It Works

“When we sought to build a predictive model for making marketing decisions in the multifamily industry, we knew that data quality was going to be an important factor. Without quality data, you can’t make quality predictions,” says Stein. “We invested a lot of resources in building a strong data infrastructure.”

“Without quality data, you can’t make quality predictions.”

Martin Stein, Chief Analytics Officer at Conversion Logix

Building the Infrastructure

To build predictive leasing, we focused on three objectives:

• Processing the highest quality data
• Moving beyond snapshot data to customer journey data
• Unifying awareness and conversion data into one model

These objectives were essential to ensuring that the predictions we provide are reliable and impactful. 

We took several critical steps to lay the foundation for accurate predictions:

• Tracked user journeys across all of a property’s ad campaigns and measured the related website conversion activities. We collected data through Google Analytics, ad channels, and our proprietary analytics solutions, leveraging millions of industry-specific customer journeys. You can learn more about it here. 
• Integrated impressions, clicks, and journeys into a unified model since click-based metrics alone don’t capture impression and awareness-based advertising.
• Analyzed these journeys to determine the likelihood that a prospect will convert based on patterns observed in past data. 

So, how do we leverage all of this data to predict future behavior? It all starts with creating customer journeys.

Creating Customer Journeys

Building customer journeys is key to unified attribution modeling. These journeys utilize a custom session model and even a custom user model, allowing us greater flexibility in measuring the impact of advertising. This approach captures the realistic time frame it takes for a customer to make a decision, from viewing a property to potentially signing a lease, providing more accurate insights into advertising effectiveness.

Measuring customer journeys is important and it is the basis for our probabilistic channel attribution model. Let’s say you have an advertising campaign that includes the following channels: 

• Facebook 
• Google Text Ads
• Email

Let’s take these four user paths as an example to help illustrate how we will use user journey data to understand which of these channels is the most effective:

Simply tracking these paths might make you think that certain channels are more or less effective based on how often they appear before a conversion. For example, you might wonder:

• Is Email a powerful channel because it is in multiple paths leading to conversions?
• Is Facebook influential since it often appears at the start?

However, these observations alone can be misleading. We need a more sophisticated method to truly understand how each channel contributes to conversions. This is where we apply Markov Chains, which help us lay the groundwork for probabilistic attribution.

Using Markov Chains for Attribution

Markov Chains reveal how prospects transition between marketing channels. A Markov Chain is a mathematical model that helps us to understand systems that move from one state to another, where each move depends only on the current state. These “states” can be represented by different marketing channels/campaigns, and transitions can represent “movements” from one channel to another. Conversions can be measured by recording key events indicative of leasing intent.

A Markov Chain is like following someone’s path through a shopping mall. Each store they enter depends on which store they’re in now, not where they started. In the same way, we track how a prospect moves from one marketing channel to the next, helping us predict where they’re most likely to end up—whether that’s an application to lease an apartment or no conversion.

To answer the question of which channels have the most impact on conversions, we move to the next step, which involves measuring the occurrences of each transition between touchpoints. 

• Start ➔ Facebook
• Start ➔ Email
• Facebook ➔ Email
• Facebook ➔ Google Text Ads
• Email ➔ Google Text Ads
• Google Text Ads ➔ Conversion
• Any channel ➔ No Conversion

Next, we use this data to assign probabilities. 

In this scenario, we calculate how likely a user is to move from one channel to another. If out of 4 paths, 3 start with Facebook, then the probability of Start ➔ Facebook is 75%. 

Now we can create something called a Transition Matrix that contains all the probabilities of moving from one state to another. 

From Facebook, 66% move to Email and 33% move to Google Text Ads. This matrix helps in understanding user behavior, such as which paths lead to conversions, or how users move between different marketing channels. 

Two more steps help us to create the first part of our Unified Attribution model: 

• Simulating User Journeys: We utilize the transition probabilities we calculated to simulate how users would move through channels toward conversions.
• Calculating the Removal Effect: By removing one channel at a time, we can observe how the removal affects the overall conversion rate. Let’s say we remove Email and recalculate the conversion probability. A decrease would indicate what the Email attribution is.

As we pointed out above, the key to Unified Attribution is combining the Markov-based channel attribution with a reward-based system for calculating the impact of channel impressions and channel clicks. We leverage a data pipeline that merges data for each client and each month to calculate the combined impact on conversions. 

As a result, our Unified Attribution model has a much better handle on reporting channel attributions for awareness and lead-generating campaigns.

The approach we outlined above describes the process behind one of the most powerful features of Predictive Leasing: how we measure the impact of each channel. By understanding the likelihood that a channel or path of channels will create conversions, we can provide predictions that can help clients realize unparalleled campaign results.

“We repeat this process of gathering journey data and modeling it with over 30 campaign sources, over a million simulations per property, and 20 million journeys per month” states Stein, “this is what enables us to confidently assess and predict channel impact using this approach”.

Turning Insights Into Action: Campaign Optimization

The real power of Predictive Leasing lies in how these insights translate into actionable strategies. Once we know which channels or combinations of channels are most likely to lead to conversions, we can guide our clients on how to optimize their campaigns.

For example, if our data suggests that shifting $500 from Paid Search to Display Ads will increase your conversions by 20%, you have the opportunity to make proactive adjustments to your media spend. This isn’t just about measuring what happened in the past—it’s about forecasting future results and adjusting your strategy in real-time to maximize impact.

Why This Matters for Multifamily Marketers

For multifamily marketers who deal with longer buying cycles and prospects making high-stakes decisions, having the ability to predict leasing outcomes is a game-changer. Traditional attribution models may provide some clarity, but they rarely give you a clear answer to the question, “What will happen if I invest more in one channel or reduce spend in another?”

Our Predictive Leasing solution will address this gap, allowing you to optimize your budget with confidence. Instead of guessing which channels are most effective, you’ll have a clear, data-backed picture of which strategies are most likely to drive leases.

Step Into a Data-Driven Future

Predictive Leasing marks a significant leap forward in how multifamily and senior living marketers can plan and optimize their campaigns. By leveraging the data collected through Unified Attribution, we’re providing clients with the tools to predict future outcomes based on real-time insights.

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