Millions of consumers visit websites every day, leaving a trail of behaviors related to their wants, needs, desires. These research behaviors include comparing brands to determine who can fulfill their needs, weighing relevant product offerings, or searching for the most competitive rates.
As these consumers shop for major-life purchases (MLPs)—things like cars, homes, loans, insurance, or education—they invest even more energy to insure these big-ticket items are right for them. In doing so, most visit multiple websites and the vast majority (87%) comparison shop on every purchase.
We know they also invest a lot of time in making these decisions. We see from our data that mortgage transactions take an average of 171 days, and insurance is in the 3-month range. Some MLP shoppers can take much longer.
Each one of these points in the MLP buying journey is an opportunity for a brand to connect with the consumer, at the right time with relevant messaging that can help her. But how do you go about doing that?
To understand the ideal customer profile (ICP), organizations have historically focused on what the customer looks like based on demographic data. The challenge with this approach is that demographic data doesn’t tell us what the customer wants or needs right now. So, demographic data might help us form a theoretical ICP, but it cannot help us connect with consumers at the right time with the right message to drive the most meaningful and fruitful engagements.
The highest performing organizations are adding behavioral data sets to their customer workflows to help them deliver better customer engagements. Ultimately, connecting to known consumer wants, desires and needs drives better engagement and higher acquisition, growth, and retention.
Having a rich understanding of what data is available within (and from outside of) your organization, and what data is needed, is critical. The right data will provide a broad view of the ideal customer and you can engage with her at the ideal place and time.
The process involves continuous testing and implementing different data models to ultimately find the data mix that will work best for your business. Some steps in the process include:
- Adjust the data as market conditions change or new data sources become available.
- Test and experiment with the data mix and optimize how and where it is applied.
- Evolve the data mix.
Advancements have been made by data-as-a-service (DaaS) vendors who can deliver unique insights, providing a wider view of shopping behaviors. These insights enable smarter and safer engagements to the right consumer with the right message while they are most receptive to receiving it.
DaaS organizations analyze and transform the data to deliver solutions that help brands answer questions about their prospects and customers. Access to this behavioral data signaling where the consumer is in their shopping journey—especially those that inform you when customers are actively shopping on comparison and lead generation websites—has moved the top of the funnel even higher.
Today’s customers have spoken, and they demand superior shopping experiences. It’s essential for brands to stop marketing to consumers and start making real connections. MLP marketers are now able to do just that, which will lead to better consumer experiences and better outcomes for the brands marketing to them.