Top 10 Prospects

A manufacturer of industrial components made 40,000 different products and had $400 million in sales through 1800 independent agents. While $400 million may sound like a lot, it was less than 1 percent of the $87 billion in annual sales in this business category. The independent agents handled the products of many other manufacturers in addition to this firm’s. Some agents were very good at pushing this firm’s brand. Others were not so good. The company wanted to stand out from the crowd. It turned to Scott Brostoff of CSC Advanced Database Solutions to come up with a method of helping its agents succeed.

Each agent had a unique territory. The firm had compiled a list of hundreds of thousands of ultimate customers based on their deliveries over 3 years. CSC decided to take this database and add Dun & Bradstreet data to it, showing for each customer the SIC code, annual sales, number of employees, and actual products that the customer had purchased.

What types of data are available from Dun & Bradstreet for appending to a business-to-business file? Some different types are listed in Figure 9-1.

Dun & Bradstreet Appended Business Data

Company name and address

Business family linkage

SIC codes

Line of business

Territory covered

Number of employees

Trends

Year business started

Bank relationships

Contact names

Net worth

Annual sales

Financial stress scores

Energy demand: electricity, gas

Telecom demand

Number of PCs

IT spending

Propensity to lease

Market penetration

Figure 9-1: Dun & Bradstreet Data

Scott decided to feed the D&B information into a model that showed which products in which quantities customers in each SIC code bought. Armed with that information, CSC decided to use the D&B data to identify prospects in each agent’s territory that were not currently buying the firm’s products. The model was designed to score each prospect in order to determine the 100 prospects in each agent’s territory that would be most likely to consume the largest amount of the firm’s products. The resulting prospect database of 180,000 companies was designed to list the top 10 prospects in each agent’s territory and the particular products that these prospects were most likely to buy. The model was also designed to predict the next best product for each of the existing customers in each agent’s territory. The next best product was the product that the customer was not now purchasing that the model predicted was most likely to be purchased and to have the highest sales.

This was information that the agents did not have and could not easily develop without CSC’s help. CSC’s next job was to get the agents to buy into the idea and subscribe to the top 10 prospects and next best product information. Experience in other situations had convinced CSC that agents will use valuable information like this only if they pay for it. If it is offered free, they will consider it worthless. How do you get 1800 independent agents to sign up for such a service and pay for it?

Scott decided to develop a major campaign to sell the agents. He started with the few agents who he knew would buy into the idea from the start. Their success in using the top 10 prospects could be used to convince the others that they could profit by signing on.

The delivery system for the names was interesting. CSC decided to install Epiphany software on the database which could be accessed by the 1800 agents through the Web. Each participating agent received an ID and a PIN so that its employees could sign onto the database and see a Web page that was designed just for them and that featured information about their sales and commissions, their top 10 prospects, the next best product for each of their existing customers, and a chart that compared their sales with those of other agents in their region and in the nation. A contest was designed for the agents who made the most effective use of the top 10 prospects, with prizes for the winners. By making its independent agents profitable, the manufacturer would increase its sales and profits.