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Table 9-7 Top Products of Prospect Companies
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Company G B C Total Similar Sales companies $1,992,001 6 $ 889,445 8 $ 102,004 20 34 Best-selling Average product annual buys Hoses $889,554 Switches $545,667 Pipe $ 37,253
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H e l p i n g B u s i n e s s C u s t o m e r s t o B e c o m e P r o f i ta b l e
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each company. He also sees a list of the 10 best prospects, along with the product that he should sell to them. By clicking on each prospect, he gets the name and address of the company, the contact information, company data, and so on. Let us suppose that this particular agent has already canvassed some of these prospects and knows that he cannot win them over. They are already locked into the competition for some solid reason. The agent needs more prospects. No problem; he clicks on a Do Not List column opposite each of these prospects, and then clicks on a Refresh button. The top 10 prospect software goes back to the prospect pool and redoes the analysis, dropping the Do Not List companies and adding new companies from the pool. Within a few minutes, the agent has a refreshed list of top 10 prospects. He can do this as often as he wishes, working his way down all the prospects reported by D&B within his territory. This is an example of the way modern database analysis supports business-to-business marketing and sales. The information shown in these tables could not have been developed without a customer marketing database. Once you have that database, the software to produce next best product and top 10 prospects is complex, but achievable.
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Bene tMall.com is an online exchange for buyers, sellers, and providers of employee bene ts for companies with 100 employees or less. Through Bene tMall, insurance brokers can access active accounts and receive accurate real-time quotes, reducing the total sales cycle from more than a month to less than an hour in most cases. As well as the Internet, Bene tMall also supports brokers with traditional in-market call center and sales support. The Mall s quote engine lets brokers quote multiple carriers and multiple products in a single proposal. There is a large community of brokers in the United States that focuses on providing insurance and bene ts contracts to small businesses. Also, there are many companies that offer small-company bene t packages, with many different product offerings. The challenge for a broker is to be able
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to access information on a variety of options in a timely fashion. Since brokers usually have small budgets and cannot afford sophisticated systems, they nd companies like Bene tMall to be quite helpful. Going directly to a carrier can require a lot of paperwork and take weeks to process. By the time it s all completed, the client may have chosen to purchase from some other broker. Bene tMall s director of marketing, Patty Harris, asked SmartDM Chief Analytics Of cer Bill Franks to help the company make a major expansion of both its online and its of ine capabilities and staff. With more than 50,000 brokers in the company s database, they decided to launch a program to increase the share of business obtained from brokers. The goals of the initiative were to Set up a new Web-based database marketing system across several channels, including direct mail, email, fax, and Web surveys. Provide regular updates to broker segment classi cations and respond rapidly to changes in the broker pro le. Begin marketing to brokers through programs based on the type and frequency of business that they have done with Bene tMall. Allow easy collection of broker information, including communication preferences, demographics, and customer satisfaction feedback. Have responses tracked and results quanti ed so that the effectiveness of marketing efforts can be rapidly determined and the efforts adjusted as needed. Personalize broker communications with the data collected and the name of the broker assigned to each broker. To set up the new system, there were several obstacles to be overcome: The Bene tMall database was not marketing-friendly. Getting data out of it was dif cult. Bene tMall had not done sophisticated database marketing in the past. It was dif cult to sell the concept to management. There was no historical information that could be used to benchmark the response to new programs.
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