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A nonprofit organization needed to reduce their acquisition costs without sacrificing the number of new donors acquired or revenue generated.
A Merge Purge Optimization Analysis showed that records that scored in the bottom three segments of the model performed at 64 percent below average on response rate and 76 percent below average on gross dollar per piece mailed.
Based on this analysis, the mailer opted to drop the 49,000 records that scored in the bottom three segments of the model.
By eliminating these poorly scored records, the client was able to reduce their acquisition costs, boosting their net revenue by $26,000 (including the optimization cost). While there was a slight decrease in the number of new donors acquired (3 percent), the overall response rate increased by 12 percent and their campaign Return on Investment increased by 16 percent.
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CASE STUDY: MERGE PURGE OPTIMIZATION CUTS ACQUISITION COSTS
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A nonprofit organization needed a cost effective way to grow their donor file and increase their acquisition revenue.
Merge Purge Optimization was used to identify 90,000 netfile records that were predicted to perform poorly. Those names were replaced with 50,000 of the highest scoring names available on the co-op database.
Replacing the lowest scoring netfile names with higher scoring prospect names resulted in more donors and incremental campaign revenue. The revenue increased by $11,000 (net after optimization and replacement costs), the overall response rate increased by 19 percent and the gross dollars per piece mailed increased by 21 percent.
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CASE STUDY: MP OPTIMIZATION COST EFFECTIVELY GROWS DONOR FILE AND INCREASES ACQUISITION REVENUE
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One of our Fortune 100 clients asked us if we could help them identify and suppress names that fit their custom definition of chronic non-responders as part of our merge-purge process. This client wanted to step down their volume in a controlled way over a 12-month mailing campaign and needed reports developed and analyses run in order to do so without eliminating good prospects at the same time.
MMI Direct researched past mailings and response patterns for both house and acquisition files, and using this information, created files and a matrix that allowed each subsequent mailing to be run against data files that identified when a non-responder became “chronic” and should be removed.
The client was able to decrease their volume without sacrificing overall performance. The identification and removal of chronic non-responders in phases meant that the natural decline encountered when repeatedly mailing the same pool of names was largely offset and thus income numbers improved.
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CASE STUDY: DROP CHRONIC NON-RESPONDERS TO OPTIMIZE MAILING
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A list manager client of ours came to us with a list of 1.5 million email addresses. What she wanted to do for her client, who publishes a very large ezine, was to append their list of email addresses with postal addresses along with other demographic and behavioral purchasing data. This would enable her to develop new markets and create additional revenue for her client.
MMI Direct set out a detailed plan, complete with flow charts of procedures and processing to help her accomplish the goal. The email list was appended with postal addresses. Then the list went through a series of hygiene processes including NCOA, PCOA, address enhancement, de-dupe, deceased, etc. to achieve the most current deliverable postal addresses for this email list.
Finally, demographic and behavioral data were added to the file such as age, gender, hobbies, sports enthusiasts, travel, donors, mail orders buyers, etc.
Our client was able to transform the list of email addresses into a valuable asset that could not only be used for more targeted email campaigns, but greatly increased its value by making it usable for direct mail campaigns as well. MMI Direct’s process transformed the email list into a complete postal and email list which now generates additional product sales as well as additional list rental revenue for her client.
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CASE STUDY: COMPANY DOUBLES USE OF EZINE LIST