What do age, income, and geography tell franchised businesses about consumer buying behaviors? They are long-held staples of audience targeting strategies — and don’t get us wrong, they hold valuable information — but this information alone often falls short of indicating what a consumer will actually do.
Today’s insurance market highlights a broader real-time challenge for marketers, especially those who must appeal to consumers making complex decisions during times of economic strife.
Television advertising hasn’t been a meaningful—or accessible—marketing strategy for many credit unions. The reasons why are plenty, including the expense, difficulty to measure, and overly broad targeting.
For many businesses, narrowing the time between when a customer conducts online research and makes a purchase offline has long been a challenge. Consumers can spend weeks browsing, comparing, and researching before they ever pick up the phone or walk into a store. The question isn't whether or not they're interested. It's how to identify them at the right moment and deliver a message that moves them from research to action.
As the world becomes increasingly digital, we’d like to challenge a common misperception that geography has become irrelevant. The reality is more nuanced. The shift to digital didn’t erase the importance of location. In fact, in many ways, it intensified it.
Most businesses today have invested in CRMs and marketing automation, yet, they are still working with incomplete data. At a glance, the gaps look small — a missing email address here, or an outdated phone number there. Those minor details alone can add up to costly inefficiencies—but there’s still a whole other universe outside your CRM, filled with incredibly relevant data that you don’t have. Think total household income, lifestyle factors, and spending patterns.
Whether it’s forecasting future demand or segmenting high-value audiences, marketers are increasingly turning to predictive models to help them make smarter decisions. But here’s the hard truth: even the best predictive model is only as good as the data you feed it.
Every click, swipe, and search leaves a trail of digital breadcrumbs. But, not all of it points toward actual buying intent. In the midst of an abundance of data, there’s a real challenge in quickly and confidently separating high-value prospects from those who are just browsing.