Getting close and personal with customers has become a primary goal for marketers. After all, McKinsey notes that 71% of people expect personalized engagement from brands. But trying to give each customer individualized attention is quite a tall order, especially at scale. However, AI-driven solutions like predictive audience models are making this process more efficient and highly effective.
What’s a predictive audience? According to Nativo, it’s a segment of customers who are expected to take a specific action in the future. For instance, a predictive audience could be all those who are at risk of unsubscribing to your email newsletter. Or it could be anyone likely to make a purchase within the coming week. The possibilities are endless. When you know what customers are likely going to do, you have the closest thing to a crystal ball in your hands.
It’s important not to confuse predictive audiences with lookalike audiences, which are created solely based on past behaviors. Unfortunately, what customers did in the past aren’t always good predictors of their future actions. Plus, as Nativo explains, lookalike audiences require tons of data—including third-party data (which is going out of fashion). Consequently, though it was once cutting-edge, lookalike audience modeling is becoming outdated.
In contrast, predictive audiences can be formed without third-party data and with less data, allowing organizations of any size to use them. Additionally, these models take other factors aside from past customer behavior into consideration, leading to greater efficacy.
Predictive audiences in action
Consider this scenario: You’re trying to reduce customer churn rates. That’s why you’ve been sending “we want you back” emails to all of your customers who haven’t made a purchase in 90 days. Why 90 days? It’s a timeframe you’ve used for years.
There’s just one issue: Retention rates haven’t improved.
The problem with this common approach might be that the 90-day mark is too broad and doesn’t work for all customers. Depending on a customer’s personalized buying cycle, sending an email after 90 days could be too early or too late. If it’s too early, the customer might get annoyed and search for a different brand. (“Why are they sending me this?”) If it’s too late, the customer might already be gone. (“I wish they had sent this a month ago.”)
If you use a predictive audience tool instead, you can determine exactly when a customer moves into an at-risk category. The software is capable of constructing a unique buying cycle for each buyer in your database, providing you with the ability to look holistically at your customers and engage with them at the right moment.
Predictive audience-friendly marketing strategies
Are predictive audiences powerful? Without a doubt. However, getting the most out of them requires you to implement a few strategies.
1. Adopt a more proactive marketing mindset
The marketing world has traditionally been based on reacting to customer behaviors. For example, you might deploy the same discount email to all customers 30 days after they make their first purchase. But this cadence is simply reacting to the action they took. Narrowing your customers into predictive audience segments enables you to be more personalized and proactive.
There are numerous benefits to adopting a proactive mindset as a marketer. For one, staying proactive gives you the chance to nudge customers at precisely the right moment. It also helps your customers feel like your company knows them well, boosting your marketing’s customer engagement potential.
2. Automate as many workflows as possible
Automation can help your team take greater advantage of predictive audiences. Without automation, it would be nearly impossible for you to deploy individualized campaigns. But if you’ve invested in the right type of software, you can put every customer journey on autopilot.
This doesn’t mean you won’t have to create a flow of original content. You will. You just won’t have to babysit the process of deploying the content. Plus, you can program your automated software to add personal touches—such as the customer’s name or their last item purchased—to make the experience more individualized and engaging.
3. Capture more zero-party and first-party data
Predictive audience models are fed with zero-party and first-party data, not third-party data. In fact, the more data predictive audience software receives, the higher its real-time predictive capabilities will be. However, many companies fail to collect massive amounts of those types of data, even though 88% of marketers told Acquia that gathering first-party data was becoming more critical than ever.
How can you collect more zero-party and first-party data? Ask for customer feedback through surveys. Give customers discounts in exchange for providing you with insights and information about themselves. Just get inventive—because every piece of data is valuable.
4. Explore a variety of predictive audience segments
Marketers have the ability to determine how they want their predictive audience software to segment customers. This means you have a lot of freedom when it comes to tailoring predictive audiences to your marketing objectives.
Let’s say you have a goal of keeping customer unsubscribes to a minimum. You could ask your model to construct a predictive audience of customers who are probably going to unsubscribe soon based on a number of attributes as well as their past actions. After this audience has been built, you can send out marketing messaging aimed at keeping them engaged (and stopping them from opting out).
AI and machine learning are enabling marketers to give customers a level of personalized service that simply wasn’t possible before. If you’ve been trying to engage more with your buyers, predictive audiences could be the ingredient your marketing’s been missing.
