Semcasting

Qualified Prospects

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Semcasting On-Demand Targeting

Qualified Prospects with Every Marketing Campaign

Predictive Modeling to Target Your ProspectsSemcasting’s On-Demand Targeting makes professional quality predictive analytics and prospect targeting a practical option for companies in any industry and with any size budget.  On-Demand Targeting streamlines the hands-on data preparation process and automates the analytic process, reducing the cost and specialized expertise that often limits predictive modeling use today to only large-scale marketing campaigns.

The On-Demand Targeting platform produces professional-looking demographic profile reports for any current or lapsed customer list, builds automated response models without any specialized statistical skills, and creates highly targeted mailing lists that can improve response rates by 7.5 to 15%. Read More  

 

The Process

On-Demand Targeting’s simple step-by-step process will eliminate the need for list selection guesswork.

Step 1: Upload

The process begins by matching your customer list with Semcasting’s compiled universe in order to append over 500 demographic data elements to the file.

Step 2: Profile

The customer information is automatically sorted and summarized, generating a comprehensive profile report that can be used in planning and supporting campaign decision making.

Step 3: Model

In a matter of minutes the patented automated modeling process compares your customer list to non-customers in the Semcasting database.  The model runs for hundreds of generations to identify which attributes are unique and predictive about your customers.  The attributes that make someone more likely to respond are measured and then derived into an equation.  This equation is later used to score households in the Semcasting consumer universe.

Step 4: Model Report

The platform generates an executive summary report that clearly explains the scoring model results.  The report contains a traditional lift chart and a summary of the attributes that the model pulled as being predictive for a successful direct marketing campaign.

Step 5: Build List

By applying regional selects and business rule filters from other fields a scored list is created.


  • Case Studies

  • FAQ

 

6Identifying Qualified Reverse Mortgage Candidates
A successful, local market reverse mortgage company wanted to expand its customer base to other regions.  On-Demand Targeting produced better lists for the company, allowing for an increase in response rate and an estimated $16 million increase in profits. Learn More

 

 

 

 

7Non-Profit Battles List Fatigue
A major, national non-profit organization was looking to locate new donors that it had not yet exhausted through previous mailings.  In the first four months of using On-Demand Targeting, the non-profit received over $150,000 from new acquisition donations. Learn More

 

 

8Cleaning Up with Direct Mail Targeting
A major residential cleaning service franchise was looking to improve its current broad-targeting and simple geography based mailing lists, while increase leads.  On-Demand Targeting mailing lists achieved a 105% increase in ROI. Learn More

 

 

 

9Targeting Retail Store Trade Areas
A multi-location furniture retailer was embarking on its first direct marketing campaign and needed to identify who was most likely to make a large furniture purchase at each of its four store locations.  On-Demand Targeting was able to select 600,000 names and deliver $7.2 million in sales. Learn More

 

 

10Targeting Online Educational Campaigns
A online university was looking for a better way to target potential students for many online class offerings.  On-Demand Targeting analysis was able to increase ROI for most university programs by at least 20%. Learn More

 

 

11Getting In Shape with Direct Mail Targeting
One of the largest fitness chains was looking for a more effective way to recruit prospective members for both new and existing facilities. The first month’s mailing using On-Demand Targeting lowered acquisition costs by 42%, and yielded 134% more members than the previous mailings. Learn More

 

 

12Targeting Retunes Direct Mail Campaign
A major metropolitan symphony orchestra wanted to reach out to prospects who were likely to purchase tickets to symphony events.  Targeted lists created by On-Demand Targeting showed an increased response rate of 115% over previous mail campaigns. Learn More

 

 


13ROI Drives Auto Warranty Campaigns
One of the leading auto warranty providers wanted to improve its targeted outreach and increase its direct mail ROI. Targeted mailing lists created by On-Demand Targeting increased ROI by 20%.  Learn More



How does Semcasting get their data and from where?

 

Semcasting’s data is derived from actual customer data in over 270 consumer categories.  Semcasting’s Data Universe is the most accurate and comprehensive multi-sourced consumer data solution available, with over 110 million households and 209 million individuals. Each household includes multi-channel contact information (postal, email, phone, online and social footprints) and over 500 demographic, affluence and behavioral variables.

 

What do I need to provide for Semcasting to use On-Demand Targeting?

 

Only two things are needed: a client file that has customers who have made a purchase of the product or service.  The file should be in tab-delimited format with the columns FIRST_NAME, LAST_NAME, ADDRESS, and ZIPCODE.   The geographic target for the mailing is also needed.

 

What will Semcasting provide after the On-Demand Targeting process is performed?

 

Semcasting will provide the following:
Profile Report - Summarizes the demographic data from the uploaded client list
Model Report - Provides a scored model that identifies the attributes that make someone more likely to respond to your direct marketing campaign
Targeted Lists/Counts - Contact information for the prospects found during the modeling and geo-targeting process

 

What is a model?

 

A model is the basis for predictive analytics and is created by analyzing hundreds of variables that can be attributed to the households in a responder file.  Those attributes are the used to find other households in the database that are most likely to respond to a given campaign.  The models are used to score every household in a prospect universe on a scale of 1-100, with 100 being those who are most likely to respond.

 

What is the prospect universe?

 

The prospect universe shows the potential number of prospect names that may be available for selection for a list once all the models and any selection criteria have been applied.

 

 

 

 

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