By Susan Saldibar

I’m convinced that those senior living operators who achieve and maintain a 90+% occupancy rate in the next 5+ years are going to be the ones who latch on to this new generation of digital advertising tools. With AI and machine learning skyrocketing, this stuff is just so amazing that it doesn’t surprise me how quickly it’s being deployed by savvy marketers across industries. It’s that big of a deal. 

I’m also convinced that one of the reasons senior living isn’t as quick on the uptake is that it’s hard to find good explanations of how it all works, let alone its benefits. Maybe that’s why we are seeing the adoption of lightweight iterations of digital advertising that, while they may keep an organization in the mix, limit their ROI. 

That’s also why I was particularly glad to see a piece on the G5 (a Senior Living Foresight partner) website that breaks down the latest digital advertising models in a way that’s super easy to understand. (If you want to cut to the chase, you can access it here.) 

Omni-channel personalization. Is this the secret sauce to digital advertising?

The premise of the article isn’t a new one: The biggest mistake marketers can make is to stop their marketing activities when occupancy goes down and budgets get tight. Any marketer knows this is a dangerous thing to do, and yet it happens all the time. That said, what’s good about these new digital advertising models is that you don’t need to (nor should you) turn them off. They can be adjusted easily and keep you in front of your target prospects regardless of what the market is doing. 

I spoke recently with Celena Canode, Marketing Campaign Manager for G5. She talked about the ability of digital advertising (when done correctly) to map a marketing campaign almost perfectly to the journey that prospective residents and families are taking. One of the main reasons for this is a move away from earlier versions of “connected multi-channel integrated” advertising to something much more sophisticated, called “omni-channel personalization”. It really marks an evolution to marketing attribution models that have enabled a much deeper understanding of how and when people make “take action” decisions. The four most common attribution models are:

  • First Interaction Attribution: Assigns credit to the first touchpoint in the buyer’s journey. This model is useful when you simply want to identify how customers initially come into contact with your brand.
  • Last Interaction Attribution: Often referred to as last-click or last-touch attribution, this model assigns full credit for a conversion to the last touchpoint in the buyer’s journey. It is most effective for determining which channels work best at converting leads.
  • Linear Attribution: Unlike first or last-click attribution models, linear attribution evenly distributes credit to each touchpoint a customer engaged with before converting. Although easy and straightforward to understand, this model lacks the nuance of time decay attribution.  
  • Time Decay Attribution: Time decay attribution is similar to linear attribution in that it also gives credit to each touchpoint in the customer journey, although not equally. Instead, it takes into consideration when an interaction occurred and gives more weight to the interaction that happened closest to conversion. This is an exciting new model that is helping marketers better target prospects where they are in the process of making a decision. 

Now that you understand these four models, there are three ways to proceed: Automation, Artificial Intelligence (AI), and Machine Learning.

  • Automation: In a nutshell, an automated system will do exactly what you tell it to. A specific set of tasks normally conducted by people, now automated to save time.  
  • Artificial Intelligence: Goes further by adding the ability to “decide” certain actions to take based on available information and a set of conditions set forth in the program. So, it adds a layer of intelligence, which makes it more valuable. 
  • Machine Learning: This is where it gets interesting. Machine learning, as G5 explains it, gets you to a level of programming that not only enables it to make decisions based on available information, but it also “remembers” actions taken by prospects and interprets the result so that it can make related decisions in the future. 

It isn’t hard to see how using more sophisticated attribution models along with machine learning all but eliminates human trial and error. (Think of not having to abandon a campaign thousands of dollars later because you misread your demographic cues, and you get the idea of its potential value.)

Warning: Don’t try this at home

It probably goes without saying that few marketers are digging into this level of digital advertising alone. It requires working with someone who not only knows what they’re doing, but who is staying ahead of the curve in terms of where digital advertising is headed, because, as G5 points out, future models will build off of AI and machine learning principles. So, if you’re not already doing this, you’ll be starting from scratch. That means you’ll have a longer journey to catch up to where you need to be. 

Again, I suggest you read the entire article, which you can access here. For more information about G5, please visit their website