Segmentation is about identifying smaller groups with distinct needs and preferences. While members of the smaller groups may share characteristics with larger ones, there’s something unique about them. This uniqueness can determine whether they’ll resonate with specific marketing messages and be drawn to one product over another.
While the premise behind segmenting audiences isn’t a secret, the fact the practice is moving up a notch may be. Predictive data is turning the method into segmentation 2.0. It’s smarter and comes with numerous benefits, including higher audience engagement. If you’re unfamiliar with how it all works, here’s a preview.
Probabilities
Conventional practices say various demographics have similarities. White-collar professionals are more likely to consume wine and cocktails. Those in blue-collar jobs gravitate toward beer and whiskey. Although there may be something to this behavior, correlation doesn’t always translate to causation. Conventional segmentation practices alone may lead to market stereotyping, which could drive down audience engagement.
What if you could segment based on the probability smaller groups will behave in certain ways? Well, you can if you build predictive audiences using criteria like purchase and churn probability. The data that goes into these audience models relies on behaviors instead of traditional benchmarks like age or income.
As a result, you can segment based on the likelihood someone will convert in the next seven days. A separate group could consist of people most likely to leave your brand in the next week. Your messaging to these different segments will speak better to their pain points or purchase stage. It will be more on point, prompting engagement instead of ignored outreach.
Preferences
When you get product suggestions from a brand you frequently shop with, are they spot on? Is it somewhat uncanny how technology anticipates your needs? It’s based on a mix of your past and real-time behaviors. Machine learning algorithms analyze your actions to foresee what you might be interested in next.
Content apps run entirely on this premise. Once you set up your profile and start streaming, algorithms in the background get to work. Soon you’ll see suggestions for shows, movies, and playlists related to what you’ve consumed. You’ll probably recognize some of the recommendations while others will be brand new to you.
Whether you decide to take the plunge will be taken into account. You’ll probably get more suggestions like the ones you’ve embraced. Predictive personalization based on machine learning isn’t perfect. However, a survey of 5,000 global consumers revealed that 80% still want and expect personalization despite inaccuracies. Personalized experiences tend to increase engagement and conversions because of the intangible value consumers get.
Motivations
Once you understand what motivates different consumers in your market, you can target them precisely. The effectiveness of your communication skyrockets. Audiences feel heard and seen. They don’t debate as long when making decisions to reach out or buy. Your brand has tapped into their why.
Predictive data helps you find how your market’s motivations differ between segments and individuals. You can discover under-the-radar insights about what drives people. Surveys may reveal some of these, but methods like online polls have shortcomings. Consumers won’t remember every detail. Nor will they always be willing to lay everything bare.
With predictive data, you’re getting the deets on why there’s lackluster results amongst one segment and rave reviews from another. Your company can address underperformance head-on without the guessing game. Save your team the frustration and the heartache.
Likewise, you can leverage a well-performing segment or one with signs of promise. Tapping into audiences’ motivations gives you the opportunity to feed their enthusiasm. A higher level of engagement can eventually turn brand loyalists into brand ambassadors, creating a spill-over effect. Word will get out, potentially increasing your audience size and scope. This boost will only add to the predictive data you can mine and gather helpful insights from.
Patterns
Say your company identifies a segment you’d like to take to the next level. The customers have remained loyal to your brand for at least five years. They access their online accounts weekly and have an average email open rate of at least 30%. More importantly, the segment also visits your website and engages with your social media pages monthly.
On the surface, it seems like you should target every member of this segment with an incentive to bundle more services. But what if there were clues you shouldn’t? Predictive data allows you to get precise enough to reach out to the right members at the right time.
Would a telecom company want to send an incentive to bundle VoIP service with internet to the entire segment? The same goes for a bank trying to get more members of a promising audience to convert to the next checking account tier. A message about VoIP isn’t going to resonate with people who don’t see a need for it. Simultaneously, high balance requirements for a premier checking account won’t sit well with someone who doesn’t have the funds.
Predictive data looks at precise patterns so you don’t send the wrong message at the wrong time. By seeing who’s engaging with what content, you can make better forecasts about who’s going to respond favorably to your efforts. Meanwhile, you’ll send relevant messaging to the remainder of the audience to keep the relationship alive. It could provide additional education about their existing products or incentives for maintaining their current services.
Smart Segmentation
The use of AI-driven predictive analytics is high, with 95% of companies reporting the adoption of these tools. But just because usage is abundant, it doesn’t mean understanding is. Marketers often struggle with data-driven decisions because interpretation of the results can be tricky.
In this sense, reading predictive data is like interpreting tarot cards. Individual biases and aspirations can get in the way of what’s really being revealed. If you can set those aside, you’ll find predictive data will segment your markets more accurately. In turn, smart segmentation will engage who your brand is meant to.
