Data Optimization Retail Bank

A National Retail Bank Uses Data Optimization To Improve New Customer Prospecting Campaigns

 

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The Problem

A large retail bank’s teleservices group was experiencing unremarkable results with regard to their new account prospecting campaigns. The bank needed to improve top line results, such as new customer conversions and sales. At the same time, it needed to improve key campaign metrics, including: contact rates, presentation rates, and operator talk/ wait times, through better management of data records and outbound telemarketing resources. 

The bank turned to Speedeon Data, who was able to provide comprehensive customer contact data along with a suite of innovative data services, including: real-time reverse phone append, modeled demographic data append, data optimization, and new mover programs.

 

Customer Data Records are Optimized

The Solution

Speedeon Data recommended that the bank “Data Optimize” their customer prospect files. 

Data Optimization segments customer contact data into key “optimization” categories. The optimized data can then be used to make better business process decisions regarding how to best manage verified and corrected records based on the quality of match, and how to handle disconnected and unverified records. Data Optimization enables customers to gain more usable prospect data, to better understand and utilize their data, and to maximize return-on-investment by decreasing expenses while increasing response rates.

 

Favorable Topline and Bottom Line Results

The Results

Data Optimization verified 76% of the banks prospect records, appended correct phone numbers to 39% of the verified records, and identified 23% of their prospect records as either “disconnected” or “unverified”. As a result, the bank was able to eliminate 115,000 unproductive leads, and achieve the same level of sales with 700,000 fewer outbound calls and 5,085 fewer hours of operator time. “Contacts Per Lead”, “Sales Per Lead”, and “Sales Per Dial” all increased by 30%.

By repriortiizing or eliminating low quality/unverified records, and by contacting only valid prospects, the bank was able to reduce operator times and decrease costs. Further, by eliminating inaccurate records, the bank increased response rates, contact rates and presentation rates. Data Optimization provides actionable intelligence regarding customer data, so companies can make better direct marketing decisions and improve campaign results.