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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).

Once a company accepts the need for a true database marketing system and has introduced the framework within which the data can be organized, its time to begin the implementation stages.

Step #1Inventory. Take a complete inventory on current thinking and efforts around content, audience, offer, contact, channel, creative, infrastructure and performance metrics. This includes answering questions about channel utilization, audience identification for a campaign and offer optimization.

Step #2Best Practices. Identify the best practices associated with each enabler, constraint and profit driver. This is where a database marketing expert is needed to consult and guide you on the best practices. Examples of best practices are highlighted below:

Content Best Practice. Data is only valuable if it improves predictive, descriptive or business value. Data that does not fall into these categories should be removed from your infrastructure.

Audience Best Practice. Utilize models to segment your prospect universe based on previous events such as response, conversion and profitability.

Contact Best Practice. Capture contact history on all your prospects and utilize it for future targeting.

Step #3Gaps. Identify the gaps between your current inventory and DBM best practices.

Step #4Options. Identify the different options available to help close the gaps identified in Step #3. Examples include building a prospect value model or conducting a data evaluation analysis to determine the predictive power of your data.

Step #5Priorities. Determine the priority of each option. Identify which option will have the biggest impact and which options are easy to implement. It is imperative that a clear distinction is made between options vs. priorities. That is, options are things that can be done (but may not support current goals), while priorities are things that must be done in order to meet current goals.

Step #6Analysis and Decision-making. Conduct the analysis around the priority and decide whether you should implement the strategy.

Step #7Implement. This approach will often yield 75 to100 options. The key is to manage these options and identify the top priorities. This process is dynamic with new options being identified and priorities changing.

Developing a data-driven prospecting strategy is a powerful marketing tool in our highly competitive insurance arena. Companies who have the vision and knowledge to embark on this journey will be our future market leaders.

is vice president of data solutions for Merkle Direct Marketing Inc. This article is based on his presentation at the Professional Insurance Marketing Associations MarketTech conference. He can be reached at via e-mail at [email protected].

Reproduced from National Underwriter Edition, March 3, 2003. Copyright 2003 by The National Underwriter Company in the serial publication. All rights reserved.Copyright in this article as an independent work may be held by the author.


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