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Speedeon Data Blog
One of the common challenges facing today’s marketers is an abundance of customer data but the inability to translate that data into actionable insights. This is especially critical when deciding on which customers to focus your marketing resources. For most businesses, acquiring customers is at or near the top of their priority list. Acquiring new customers is not that difficult though. Acquiring profitable, long term customers is the real challenge. But where to start?
If you’re like most marketers, you’re probably trying to get greater visibility into the key factors that drive purchase decisions and impact customer behavior for your brand or product. This can be a challenge though as most of our purchasing behavior is habitual in nature. Shopping habits are likely to shift, however, while consumers are experiencing major life events such as college graduation, marriage, the birth of a child or purchase of a home. Ads featuring timely and relevant marketing messages can impact a person’s shopping habits for years to come.
Marketers have quickly learned that “Big Data” isn’t enough to improve overall ROI. Instead marketers must take insights from derived data and utilize them to drive personalized marketing communications.
Insurance purchases are often triggered by key life events, such as when customers are move, get married, have children, or get divorced. Life stages are key drivers for retaining current policyholders and for upselling or cross-selling new insurance products.
With life comes change and with change comes opportunity. Getting married, buying a new home, having children and even becoming “newly single” are some of the important milestones in all of our lives. As our circumstances change, needs change dramatically.
Are you aware that Modeled Data can be more accurate, powerful and complete than traditional demographic data? Modeled Data has many advantages over traditional demographic data, and can be used in a wide variety of direct marketing, customer segmentation, and data modeling applications.