The foundation of successful customer acquisition is prospect data that targets consumers who are in the market to buy a specific insurance product. Too often, insurance marketers rely on simple targeting criteria such as age, income and gender. The use of this commodity prospect data results in a higher effective cost per lead, fewer policies sold, and higher lapse rates. Fortunately, the use of life event trigger data can help you target individuals who are highly likely to purchase an insurance product in the near future.

The foundation of successful customer acquisition is prospect data that targets consumers who are in the market to buy a specific insurance product. Too often, insurance marketers rely on simple targeting criteria such as age, income and gender. The use of this commodity prospect data results in a higher effective cost per lead, fewer policies sold, and higher lapse rates. Fortunately, the use of purchase propensity data can help you target individuals who are highly likely to purchase an insurance product in the near future.

Suppose you are driving your car on a dark and curvy mountain road, on a rainy night, with a very foggy front windshield, dim headlights, and a perfectly clear rear view mirror to guide your path forward? It’s not a comforting scenario. Surprisingly, this is how many companies still operate when attempting to understand and improve their customers’ experiences and the resulting customer satisfaction (or JD Power/Net Promoter) ratings. They know a great deal about the past but very little about the future; they know a lot about the average customer but very little about the individual customer.

When it comes to life insurance leads, too many companies are wasting their marketing dollars. In fact, their cost per lead is 2-3 times too high. 

A cursory glance at today's market research news makes it immediately clear that big data and advanced analytics are hot topics So why all the hoopla? What is big data in the context of market research and what are some working examples? What is the real promise of big data market research (Big Data MR)? And most importantly, what immediate next steps can you take to harvest some near-term Big Data MR benefits?

On average, about 10% of your Medicare Advantage members churn each year (Kaiser Family Foundation). Let’s assume that you have 100,000 members and you average $800 per member per month (PMPM) in Medicare payments and premiums to your health plan. At a 10% churn rate, you are losing 10,000 members and $96 million in plan reimbursements!

Conjoint market simulators are powerful tools that help you: Maximize market share Maximize revenue Maximize profit In this new demonstration video, Dino Fire (our Chief Data Science Officer) shows you how to measure your brand strength vs. competitors in your market. This can be invaluable to see where you stack up and, more importantly, where you can look to improve. Sometimes our biases can cloud our judgements as to how customers really think about both our brands and the brands of our fiercest competitors. We can't let these biases affect our judgements and critical decision making!

Executive Summary Many CPG manufacturers are losing the battle for shelf space and margin growth unnecessarily. Changes in consumer preferences and retailer innovations are impacting this battle, and they're not keeping up. Successful CPG manufacturers are driving demand with insights from category research, product innovation research, and market simulation.