Optimizing Marketing Effectiveness With A Data-Driven Prospecting Strategy

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Over the last several years, the idea of focusing on customers has caused a small renaissance in insurance marketing philosophy and tactics. An entire industry has been created around the vision of having a complete and single view of every customer, and allowing the customer to interact with companies through their desired channel.

This customer-centric strategy has become known as Customer Relationship Management (CRM). But while it is difficult to find fault with CRM philosophy, it typically falls short when dealing with a companys lifeline to long-term sustained growth–new customer acquisition.

So, while CRM is a key component in well-rounded marketing, it is just as imperative that insurance companies simultaneously develop, implement and maintain a Prospect Relationship Management strategy to drive customer acquisition for viable long-term results.

Prospect Relationship Management (PRM) is a strategy similar to CRM except that in PRM, the primary objective is to gain a complete understanding of prospects across all channels, providing for the acquisition of the greatest number of customers at the lowest possible cost. Now more than ever, a good database marketing strategy is critical to achieving superior results.

The Knowledge Gap. The primary barrier in keeping marketers from optimizing the performance of their prospecting program is having the ability to utilize all the information and technology available to them. The Gartner Group refers to this phenomenon as the Knowledge Gap.

The amount of data that is available to marketers is increasing at a faster rate than their ability to utilize it and make it actionable. Marketers who understand this gap and develop methods for closing it will thrive in the ever-increasingly competitive insurance arena.

Database marketing (DBM) is one of the most misused terms in the direct marketing industry. In its simplest form, the database is the repository for all customer information–information that drives your marketing program. A more specific definition is a data-driven process using specific, actionable strategies that allow marketers to measure, analyze, predict, maximize and optimize overall marketing effectiveness. DBM by definition is not a project, but a process. Chart #1 illustrates the key steps of the DBM process.

To develop a data-driven prospecting strategy, insurance companies must move through this process. With the goal of giving the right offer to the right prospect at the right time through the right channel, true DBM isnt accomplished overnight and insurance companies need to take incremental steps over long periods of time. But these are steps that too many companies are unwilling to take and time that too many companies are reluctant to commit.

One approach in helping marketers close the Knowledge Gap is to create a framework in which marketers can understand and solve the marketing challenges facing them. This framework revolves around three areas: Enablers, Profit Drivers and Constraints.

Enablers allow marketers to capitalize on the availability of data and to use it to help close the Knowledge Gap. The primary enabler in DBM is contentdata contained within an environment that is accessible, flexible and actionable.

Profit Drivers in database marketing are the tactical components that influence program results. There are five primary profit drivers in direct marketing: Audience, Offer, Contact, Channel and Creative.

Constraints keep marketers from employing the DBM techniques that can help to close the Knowledge Gap. The primary constraint in DBM is infrastructure–the database environment. Without an actionable database environment, many DBM strategies are impossible to implement.

What follows is a framework a company can use to develop a data-driven prospect strategy (see Chart #2).