Multi-touch Attribution Powers Predictive Marketing
Marketing attribution is key to understanding a prospect’s online buying journey. We know that consumers today consult up to 20 online sources when making a purchase decision. Without a solid attribution strategy in place, it can be hard to know whether any one of those touchpoints had more influence on a decision to purchase than any of the others. Most marketers use single-touch attribution to get a sense of how their marketing efforts are working. While single-touch models are useful for understanding what drives prospect interest, they are limited in the insights they provide about the entire buyer’s journey. With a last-touch model, you might know which source is immediately driving prospects to reach out. But, you would be in the dark about other sources that prospects consult and that weigh into their decision about leasing from you.
On the other hand, multi-touch attribution helps connect multiple touchpoints on the buyer’s journey to give marketers better insight into which of their efforts are working. Multi-touch attribution (MTA) takes into account these multiple touchpoints in the buyer’s journey, supporting a more nuanced view of human behavior than a single-touch model. It tracks the entire online journey from the first click to the last call. Multi-touch attribution is not a new concept, but it is one that is often misunderstood. While MTA models do consider the entire buying journey, linear, time-decay, and U-shaped models make broad assumptions about prospects’ behaviors that could have a major impact on results.
The Online Renter’s Journey and MTA
Let’s look at how this comes together. On average, the online journey is 21.9 days for multifamily prospects; 16.7 days for senior living; 12.4 days for self storage.
In that time, a prospect might see half a dozen ads, visit a property website 3-4 times, and consult a variety of additional online resources to help make their decision. With single-touch attribution, a marketer might assume that only one of those touchpoints matters. We know that with a complex online buying journey, that just isn’t the case. The error with many multi-touch attribution models is that they assign a weight somewhat arbitrarily to sources, depending on when they happened in the journey. In fact, without more robust modeling, we cannot take guesses about which touchpoint in the complex journey weighed more in the buying decision than others.
Probabilistic MTA considers all interactions or “touches” and then computes the likelihood that each interaction will result in a conversion. This model examines each digital touchpoint in relation to others, giving a more realistic view of buyers’ journeys within separate markets. This data is important because it identifies which touchpoints are fundamental to driving marketing conversions.
If you want to understand which marketing sources contribute to qualified traffic and better leads, you’ll need a probabilistic MTA model in place, which considers the value of each touchpoint along a specific journey and computes the likelihood of a conversion. In this way, each touchpoint is given a different weight, depending on when it happened on a journey and what happened before or after it.
Multi-Touch Attribution Drives Predictive Marketing
Probabilistic MTA is the crux of predictive marketing because it accounts for shifts in renters’ journeys and provides the data needed to continually adjust tactics. It takes into account the performance of each marketing source and determines the probability of it leading to a conversion across the entire renter’s journey. With simpler attribution models, we are only able to say which campaigns had an impact at the renter’s first or last stage. With multi-touch attribution, we can consider the entire online experience and prioritize campaigns that are most likely to have an impact on performance. This allows marketers to consider each campaign in relation to others and make decisions based on how those campaigns will drive more leads to a property.
With today’s complex online renter’s journey, predictive marketing is even more important to real estate operators. Each property can have dozens of campaigns running at any given time and each campaign might have thousands of data points associated. The sheer volume of data generated across an entire portfolio is impossible for a human to analyze performance and make adjustments daily. With probabilistic MTA, marketers gain better insights into what’s driving qualified leads to their properties.
How G5 Optimizes Campaign Performance
Probabilistic MTA is the basis for G5’s Cross-Channel Spend Optimizer, a predictive marketing technology that automatically and continuously optimizes campaigns. This technology predicts where to spend the next advertising dollar to maximize conversions by automatically allocating daily investments across networks (Google, Microsoft, and Facebook) and channels (social, search, display, and remarketing). It assigns a priority to campaigns based on their recent performance and then allocates spend across channels to achieve the best return.
Cross-Channel Spend Optimizer chooses the best campaigns based on performance and then shifts spend in a way that spends budget evenly throughout the month. This technology makes ongoing, effective, data-driven marketing decisions by computing volumes of data that humans can’t keep up with. This alone has increased conversions by up to 25% for the same budget.
Additionally, G5 Ad Optimization works in tandem with Cross-Channel Spend Optimizer to optimize campaigns based on qualified leads. It uses G5’s Call Scoring technology to determine whether each caller had purchasing intent, and if so, designates that call as qualified. We call this the “intent to rent.” Armed with this data, Ad Optimization automatically redirects spend to the campaigns that deliver more qualified leads.
Predictive marketing is the future of marketing efficiency – and it can have an immediate impact on your portfolio.
To learn more about how G5 uses predictive marketing to find and connect with in-market renters, schedule a demo.