Abstract:
A computer method that is used to predict when recipients of mail pieces will contact a call center in response to information contained in the mail pieces. The method involves, utilizing previous mailing campaign data to determine when the mail piece arrives in the home and when a call center is contacted in response to information in the mail piece; and predicting call volumes based initially on previous campaign data and as the mailing campaign progresses updating call center predictions based on current mailing campaign data.

Description:
[0001]     This Application claims the benefit of the filing date of U.S. Provisional Application No. 60/663,027 filed Mar. 18, 2005, which is owned by the assignee of the present Application.  
       CROSS REFERENCE TO RELATED APPLICATIONS  
       [0002]     Reference is made to commonly assigned co-pending patent application Docket No. F-986-O1 filed herewith entitled “Method For Predicting When Mail Is Received By A Recipient” in the name of John H. Winkelman and Kenneth G. Miller, Alla Tsipenyuk and James R. Norris, Jr. Docket No. F-986-O2 filed herewith entitled “Method For Controlling When Mail Is Received By A Recipient” in the names of John H. Winkelman Kenneth G. Miller, John H. Winkleman, John W. Rojas, Alla Tsipenyuk and James R. Norris, Jr. Docket No. F-986-O4 filed herewith entitled, “Method for Dynamically Controlling Call Center Volumes,” in the names of Alla Tsipenyuk, John H. Winkleman, John W. Rojas, Kenneth G. Miller and James R. Norris, Jr. Docket No. F-986-O5 filed herewith entitled, “Method for Determining the best Day of the week For a Recipient to receive a mail piece” in the names of John H. Winkleman, John W. Rojas, Kenneth G. Miller, Alla Tsipenyuk and James R. Norris, Jr. 
     
    
     FIELD OF THE INVENTION  
       [0003]     This invention relates to making predictions based upon in-home mail volumes and more particularly to predicting call center volumes based on predicting in-home mail volumes.  
       BACKGROUND OF THE INVENTION  
       [0004]     Companies have used the mail to sell products to customers for almost as long as there has been mail. Responses from these solicitations happen over multiple channels such as by phone, mail, fax, internet, email. Etc. Response volumes are tied to the mail volumes of direct marketing campaigns. Response volumes associated with a direct marketing campaign will usually have peak and the peak happens at some period of time after the direct marketing campaign has been mailed. Response peaks that happen via mail, fax, internet and email can be handled over multiple days. Response peaks that happen through calls can not, they must be handled in a timely manner or else the caller will hang up. Sometimes peaks in response volumes will overwhelm a call center and the call will not be handled in a timely manner. When this happens potential orders are lost.  
         [0005]     A direct marketing campaign is divided into two parts. The first part is the planning, creation and execution of the campaign and the second part is handling the responses and orders associated with the campaign. On the other hand there is normally a strong coupling between the response and order data from a previous campaign and the planning of the current campaign. There is normally a weak coupling between the execution of the campaign and the handling of the responses for that campaign. This weak coupling is partly due to there not being accurate data that can determine when response volumes associated with a direct marketing campaign will happen. Usually rules of thumb are used to tie response volumes to mailing drop dates, but the problem is that responses are more closely associated with when the recipient receives the mail piece, instead of when the mailing is dropped. Thus, the direct marketer is not able to confidently determine when the recipient who receives the mail piece will respond.  
         [0006]     A mailing drop date is when the mail leaves the mail production facility to be shipped to the USPS. The mail can be shipped to the USPS facility nearest to the production facility (local induction) or to the USPS facilities closest to where the mail is to be delivered (drop ship induction). The time delay is 1 day for local induction and 1 to 8+ days for drop ship induction. Once the USPS accepts the mail, either through local induction or through multiple drop ship inductions, the time to process and deliver can be from 1 to 10+ days. So mail in a direct marketing campaign will be arriving in home for a period of 1 to 18+ days in some seemingly random pattern to the direct marketer. Since the in home delivery patterns for the mailing are seemingly random, the call volumes associated with the mailing will be impossible to determine. Thus, the mailer is reacting to call center volumes by itself. Hence, the mailer may have staff sitting idle or staff being over-whelmed with too many phone calls.  
         [0007]     Another disadvantage of the prior art is that a mailer is unable to predict when the mail will be delivered to a recipients home or place of business henceforth the mailer may have the appropriate staff at a call center to take orders or answer questions at the time when the recipient places the call.  
       SUMMARY OF THE INVENTION  
       [0008]     This invention overcomes the disadvantages of the prior art by predicting when a recipient will receive a mail piece and determining an expected and actual recipient response to a call center. The foregoing is accomplished by: determining the mail in home volumes by day for the duration of the mailing using mail prediction algorithm; determining the expected and then actual delay from when a mail piece arrives to when a call response is received for previous and the current campaign using the response delay algorithm; determining the expected and then actual call response rate for the campaign for previous and the current campaign; and predicting call volumes based initially on previous campaign data and as the campaign progresses updating prediction based on current campaign data.  
         [0009]     An advantage of this invention is that it allows the call center management to dynamically allocate sufficient staffing resources, based on call response prediction.  
         [0010]     An additional advantage of this invention is that it allows a call center to handle the call volumes for each day of a campaign. On peak days this can be done either by hiring temporary resources or taking resources from other areas, such as staff tasked with placing is doing follow up calls. On slow days call response staff can be allocated to other areas of the call center.  
         [0011]     A further advantage of this invention is that by having sufficient staff on peak days all calls can be handled in a timely manner thereby eliminating dropped calls. Since more calls will be placed and many calls lead to orders this will lead to an increase in orders, order rate and hence will reduce the cost per order.  
         [0012]     A still further advantage of this invention is that on slow days it increases call center productivity by not having staff sitting idle. Increased productivity of call center staff directly correlates to an increase in profits. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0013]      FIG. 1  is a flow chart of a prior art direct mail marketing process;  
         [0014]      FIG. 2  is a flow chart showing how to predict recipient delivery distribution for a mailing;  
         [0015]      FIG. 3  is a flow chart that generates the actual mail shipment induction date and triggers a prediction update.  
         [0016]      FIG. 4  is a flow chart that loads facility conditions and status information and triggers prediction updates if changes are detected.  
         [0017]      FIG. 5  is an actual vs. predicted in-home curve for controlled mailing.  
         [0018]      FIG. 6  is a drawing showing the predicted vs. partial actual in-home curves for a controlled mailing.  
         [0019]      FIG. 7A  is a mailing facility condition plant report.  
         [0020]      FIG. 7B  is a mailing facility loading plant report.  
         [0021]      FIG. 8  is a flow chart showing how to compile historic USPS container level delivery data.  
         [0022]      FIG. 9A  is a drawing showing curves generated for the Dallas Tex. BMC.  
         [0023]      FIG. 9B  is a drawing showing curves generated for the Denver Colo. BMC.  
         [0024]      FIG. 9C  is a drawing showing curves generated for the Los Angles Calif. BMC.  
         [0025]      FIGS. 10A-10F  is a table showing sample mail piece historic delivery times for the North Metro facility which is used to create container level data shown in step  1580  ( FIG. 8 ).  
         [0026]      FIGS. 11A-11D  depicts sample data,representative of the mailing container level data shown in step  1580  ( FIG. 8 ) in tabular form.  
         [0027]      FIG. 12  is a flow chart showing how to determine the in-home date for a mail piece.  
         [0028]      FIGS. 13A-13B  is a table of drop shipment appointment close out dates.  
         [0029]      FIG. 14A  is a flow chart of a Process for controlling a mailing campaign.  
         [0030]      FIG. 14B  is a flow chart of an algorithm for controlling the mail.  
         [0031]      FIG. 15  is a flow chart showing how to determine the best shipment induction date as used by the algorithm in  FIG. 14B .  
         [0032]      FIG. 16  is a flow chart showing how to predict daily call center volumes for a mailing.  
         [0033]      FIG. 17  is a flow chart showing how to control daily in-home mail volumes in order to achieve daily call volumes.  
         [0034]      FIG. 18  is daily response curve showing call center response delays associated with in home mail pieces.  
         [0035]      FIG. 19  is a table showing the information in  FIG. 18  in tabular form.  
         [0036]      FIG. 20  is a table showing how the historical response delay curve is applied to the in home volume for each day in the mailing campaign.  
         [0037]      FIG. 21A and 21B  depicts an offset in the data in  FIG. 20  and then sums the in-home quantities and multiplies the sum by the response rate, which obtains the predicted calls per day. 
     
    
     DETAILED DESCRIPTION OF THE INVENTION  
       [0038]     Referring now to the drawings in detail and, more particularly, to Prior Art  FIG. 1 , the process begins in step  100 , where the direct mail marketer plans the campaign. Inputs into campaign planning include planning the creative, i.e., the design of the mail piece, offer and incentive in step  130  and acquiring mailing lists in step  120 ; then selecting prospects in step  112  by comparing respondent profiles in step  111  from different marketing tests, i.e., previous campaigns in step  110 . Once the marketer has created the artwork, selected the prospects to be mailed from the lists available, the campaign is actually created in step  200 . Step  200  involves having the various components of the mailing campaign printed, assembled and printing the addresses on the mail pieces and the address presorted. From there, the direct mail marketer mails, i.e., drop ships the mail to the appropriate USPS facility, the offer to all prospective customers in step  300 . Once the prospective customers receive the offer, some prospects place orders in step  400 . When the prospect orders, the direct mail marketer captures order processing data in step  410  and correlates the data with demographic information. That data is fed back into the order history database in step  110  and used to profile prospective customers for upcoming campaigns.  
         [0039]      FIG. 2  is a flow chart showing how to predict recipient delivery distribution for a mailing. The process begins in step  1180  where the mailing prediction process begins and goes to retrieve shipments in mailing step  1000  or the process may also begin if it is triggered by the update prediction of step  1190 . The anticipated induction date of the mailing from step  1200  is used with the retrieve shipment level data in step  1020  and with the mailing container level data from step  1220  by step  1210  to obtain the mailing shipment level data. Step  1020  uses mailing shipment level data from step  1210  including the anticipated induction date in step  1200  and the induction facility to prepare a prediction for a shipment. In step  1040  the containers in the shipment are retrieved.  
         [0040]     In step  1050  the process iterates through each container in the shipment and in step  1060  the process retrieves the container level data. Then the process will go to step  1070  to retrieve a historical container level delivery curve from step  1230 . Then in step  1080  the container delivery distribution is calculated based upon the historical delivery curve by applying the container piece count for each day in the distribution and using Sundays, holidays and other postal delivery processing exceptions. Then in step  1090  the information from step  1080  and the drop ship appointment facility condition data from step  1240  is utilized to retrieve container induction and processing facility condition. Step  1091  determines whether or not the information from step  1240  is available. If step  1091  determines the information is available the next step in the process is step  1100  to calculate facility condition offset. If step  1091  determines the information is not available the next step in the process is step  1120 .  
         [0041]     Then step  1120  adds the container delivery curve to the shipment prediction curve. Then if step  1130  determines that there are no more containers in the shipment, the process goes to step  1140  to add a shipment prediction curve to a mailing prediction curve. If step  1130  determines that there are more containers in the shipment the next step will be step  1050 . Now if step  1150  determines that there are no more shipments in the mailing the next step will be step  1160  to save the mailing prediction. If step  1150  determines that there are more shipments in the mailing the next step will be step  1010 . Step  1170  ends the predict mailing process.  
         [0042]      FIG. 3  is a flow chart that generates the actual mail shipment induction date and triggers the prediction update. The process begins at step  1400  via an automated or user driven request. Two independent events are detected, in step  1410 , mail arrives at a USPS facility as a Drop Shipment and in step  1415 , mail arrives at a USPS facility for local induction. Step  1411  follows step  1410  where the USPS scans Drop Shipment Form  8125  and produces an Entry Scan. Step  1416  follows step  1415  where the USPS scans Local Entry Form  3602  and also produces an Entry Scan. The Entry Scans are stored in Step  1420  by the USPS Confirm System for later retrieval. In addition, step  1410  is also followed by step  1430 , where the Drop Shipment Appointment System stores information associated with the drop shipment, such as the truck arrival, status, load time, etc. Step  1420  and step  1430  are followed by Step  1440 , where the Actual Induction Date is calculated using the best possible date from the entry scan or the drop shipment information that is available (If both sets of data are available, the appointment data is used). Then in step  1450  the Actual Induction Date is stored and in step  1460  a trigger is generated to update the mailing campaign prediction.  
         [0043]      FIG. 4  is a flow chart that loads facility conditions and status information and triggers prediction updates if changes are detected. The process begins at step  1300 , via an automated or user driven request. The facility conditions are then loaded in step  1315  from step  1310  and stored in step  1317 . At the same time, Facility Loading data is loaded in step  1316  from step  1311  and stored in step  1317 . Step  1320  follows step  1315 , where changes to the facility conditions are detected. In a similar fashion, step  1322  follows step  1316  and detects changes to the facility loading data. In either case, if changes are detected, steps  1320  and  1322  will trigger a Prediction Update in step  1330 .  
         [0044]      FIG. 5  is an actual vs. predicted in-home curve for controlled mailing.  
         [0045]      FIG. 6  is a drawing showing the predicted vs. partial actual in-home curves for a controlled mailing.  
         [0046]      FIGS. 5 and 6  illustrate the variability encountered when dealing with high volume direct mail marketing campaigns through the standard approach of controlling drop dates (the date that the mail leaves the facility that created it).  
         [0047]     In the case of  FIG. 6  the mailer elected to create the mail all at once then drop the 4.5 million or so pieces over 3 days. The result was a elongated bell curve. The resultant impact was that the inbound call center, where the prospect called to order the item, could not handle the call volume. To remediate the situation, the mailer decided to go to a 4 week induction schedule, targeting Tuesday, Wednesday and Thursday for receipt of most of the mail for each week as shown in  FIG. 5 , where the mailer elected to drop the mail over a four (4) week period. The expected result was that ¼ of the mail would arrive each week for a period of four weeks. The mail control module was used to create the induction plan and the result was as seen in  FIG. 5 . By knowing the daily in-home piece count for the mail and understanding the likely response to those volumes the mailer was able to staff the call center correctly and the result yielded a higher order conversion rate for each inbound call.  
         [0048]      FIG. 7A  is a mailing facility condition plant report. Block  20  is the legend block for the report. Spaces  21 ,  22  and  23  indicate the code used in the report. Space  24  indicates the condition represented by the code indicated in space  21  and space  25  indicates the condition represented by the code indicated in space  22 . Space  26  indicates the condition represented by the code indicated in space  23 . Space  27  indicates when the report was last updated. Column  28  indicates the facility name and column  29  indicates the condition of the facility indicated in lines  31  shown in rows  30  at the date indicated at the top of the column.  
         [0049]      FIG. 7B  is a mailing facility loading report that shows facility appointments over a date range. This report provides information on the amount or quantity of mail processed by a specific facility over time and the amount of mail that is scheduled to be processed by a facility in the near future. Space  900  is the header for the search criteria, including space  901  which is the Facility name header and space  902  which is the facility name. Space  903  is the Date Range header and space  904  is the date range for the report.  
         [0050]     The data for the report is defined as follows. Space  905  is the column header for the Date and space  906  is date for each row of data.  
         [0051]     Space  907  is the row where the Totals are tallied for each column.  
         [0052]     Space  908  is the header for the Total Scheduled Appointments, and space  909  is the total appointments for each date, and space  910  is the total scheduled appointments for the facility over the date range specified in space  904 , Date Range above. Space  911  is the header for the columns related to Pallets scheduled and space  912  is the column header for the total count of pallets containing parcels scheduled and space  913  is the count of pallets containing parcels scheduled for each day. Space  914  is the total count of pallets containing parcels scheduled for all days and space  915  is the column header for the total count of pallets containing bundles scheduled. Space  916  is the count of pallets containing bundles scheduled for each day and space  917  is the total count of pallets containing bundles scheduled for all days.  
         [0053]     Space  918  is the column header for the total count of pallets containing trays scheduled and space  919  is the count of pallets containing trays scheduled for each day. Space  920  is the total count of pallets containing trays scheduled for all days. Space  921  is the column header for the total count of pallets containing bundles scheduled. Space  922  is the count of pallets containing bundles scheduled for each day and space  923  is the total count of pallets containing bundles scheduled for all days. Space  924  is the column header for the total count of pallets scheduled and space  925  is the total count of pallets scheduled for each day. Space  926  is the total count of pallets scheduled for all days and space  927  is the header for the columns related to cross docked mail scheduled. Space  928  is the column header for the total count of cross docked mail containing parcels scheduled and space  929  is the count of cross docked mail containing parcels scheduled for each day. Space  930  is the total count of cross docked mail containing parcels scheduled for all days and space  931  is the column header for the total count of cross docked mail containing bundles scheduled. Space  932  is the count of cross docked mail containing bundles scheduled for each day and space  933  is the total count of cross docked mail containing bundles scheduled for all days. Space  934  is the column header for the total count of cross docked mail containing trays scheduled and space  935  is the count of cross docked mail containing trays scheduled for each day. Space  936  is the total count of cross docked mail containing trays scheduled for all days and space  937  is the column header for the total count of cross docked mail containing bundles scheduled. Space  938  is the count of cross docked mail containing bundles scheduled for each day and space  939  is the total count of cross docked mail containing bundles scheduled for all days. Space  940  is the column header for the total count of cross docked mail scheduled and space  941  is the total count of cross docked mail scheduled for each day. Space  942  is the total count of cross docked mail scheduled for all days. Space  943  is the header for the columns related to bed loads scheduled and space  944  is the column header for the total count of bed loads containing parcels scheduled. Space  945  is the count of bed loads containing parcels scheduled for each day and space  946  is the total count of bed loads containing parcels scheduled for all days. Space  947  is the column header for the total count of bed loads containing bundles scheduled and space  948  is the count of bed loads containing bundles scheduled for each day. Space  949  is the total count of bed loads containing bundles scheduled for all days and space  950  is the column header for the total count of bed loads containing trays scheduled. Space  951  is the count of bed loads containing trays scheduled for each day and space  952  is the total count of bed loads containing trays scheduled for all days. Space  953  is the column header for the total count of bed loads containing bundles scheduled and space  954  is the count of bed loads containing bundles scheduled for each day. Space  955  is the total count of bed loads containing bundles scheduled for all days and space  956  is the column header for the total count of bed loads scheduled. Space  957  is the total count of bed loads scheduled for each day and space  958  is the total count of bed loads scheduled for all days.  
         [0054]      FIG. 8  is a flow chart showing how to compile historic USPS container level delivery data. The process begins at either step  1500  or step  1510 . If the process began at step  1500  where the USPS scans drop shipment form  8125 . Drop shipment form  8125  is used by the USPS for registering when the drop shipment arrives at a USPS facility. If the process began at step  1510  the USPS scans entry form  3062 . Drop shipment form  3062  is used by the USPS for registering when mail is locally inducted by the USPS. In step  1530  the USPS confirm system is utilized. The confirm system receives the information scanned by the USPS from the mail piece in step  1520  and the information from steps  1500  and  1510 . Then entry scan data from step  1530  is sent to step  1570  mailing shipment level data and planet code data is sent to step  1590  as mail piece level data. In addition drop shipment close out data is sent from the USPS Drop Shipment Appointment System (DSAS) to step  1570  as mailing shipment level data. In step  1580  mailing container level data is correlated from shipment level data tied in  1600  and mail piece level data tied in step  1610 .  
         [0055]     Step  1560  utilizes mailing container level data from step  1580  to compile historical mailing delivery data. Step  1550  utilizes historical mailing delivery data from step  1560  to produce historical container level delivery curves. Step  1540  stores the historical delivery data for predicting and/or controlling mailings  
         [0056]      FIGS. 9A-9C  show example curves generated for BMC&#39;s and SCF&#39;s in three different regions: Dallas Tex., Denver Colo., and Los Angeles, Calif. The curves show the high variability of in home mail distributions, both volumes and timing, across BMC and SCF in the same region. Furthermore, the figures also show the high variability across different BMC&#39;s and/or SCF across different regions.  
         [0057]     Each of the  FIGS. 9A-9C  shows graphs for a specific facility, displaying average distribution of in home mail volumes from the day of induction to the day of delivery, over a 10 month period, January to October 2004. In each chart, the x axis is the number of days since induction and the y axis is the percentage of the mail delivered on that day.  
         [0058]      FIGS. 10A-10F  is a table showing sample mail piece historic delivery times for the North Metro facility which is used to create container level data shown in step  1580  ( FIG. 8 ).  
         [0059]     In  FIG. 10A  the shipment ID, i.e., the identification of the mailing shipment is shown in column  43 . The city and state that the shipment is delivered to is respectively shown in columns  44  and  45 . The three digit zip code is shown in column  46 . The zip code and the zip code plus four are respectively shown in columns  47  and  48 . The carrier route for the shipment is shown in column  49 . The delivery point code (DPC) is shown in column  50  and the cell i.e., identifies mail with different creative formats within a mailing is shown in column  51 . The mail sequence i.e., internal/identifier for each mail piece is shown in column  52 .  
         [0060]     In  FIG. 10B  the CLASS of mail is shown in column  53 . Column  54  is the name DMLAYOUT_TABLE, the name of the table holding the address information for this mail piece. Column  55  (IND_FACILITY_NAME) holds the name of the induction facility. Column  56  (IND_FACILITY_TYPE) holds the type of facility, i.e. BMC, SCF, etc. Column  57  (IND_FACILITY) holds the zip code for the induction facility, and column  58  (FIRST_IND_DATE) is the time stamp of the first scan that occurs in the induction facility. Column  59  (LAST_IND_DATE) is the optional time stamp of the last scan that occurs in the induction facility.  
         [0061]     In  FIG. 10C  column  60  (DS_SCHEDULE_DATE) is the date when the shipment was scheduled for drop shipment. Column  61  (IND_REC_PK) is a foreign key to the shipment record for this mail piece and column  62  (FIRST_SCAN_FACILITY) is the zip code of the facility where the mail piece was first scanned—after induction and column  63  (FIRST_SCAN_DATE) is the time stamp of the first scan at the processing facility. Column  64  (FIRST_OP_NO) is the operation that was performed on the mail piece during the first scan, i.e. first pass sort, second pass sort, etc. and column  65  (LAST_SCAN_FACILTY) is the zip code of the facility where the mail piece was last scanned.  
         [0062]     In  FIG. 10D  column  66  ((LAST_SCAN_DATE) is the time stamp of the last scan at a processing facility and column  67  (LAST_OP_NO) is the operation that was performed on the mail piece during the last scan. Column  68  (NUMBER_SCANS) is a count of the total number of planetcode scans (or operations) detected on the mail piece and column  69  (IN_HOME_DATE) is the calculated in home date for the mail piece, see  FIG. 12 . Column  70  (IND_FIRST_SCAN_HRS) is the number of hours between the FIRST_IND_DATE and the FIRST_SCAN_DATE and column  71  (IND_LAST_SCAN_HRS) is the number of hours between the FIRST_IND_DATE and the LAST_SCAN_DATE.  
         [0063]     In  FIG. 10E  column  72  (FIRST_LAST_SCAN_HRS) is the number of hours between the FIRST_SCAN_DATE and the LAST_SCAN_DATE and column  73  (REC_ID_PK) is the primary key for this mail piece record. Column  74  (PROBLEM_DATA) is used to flag if there is problem data for this mail piece and Column  75  (IND_FIRST_SCAN_DAYS) is the IND_FIRST_SCAN_HRS represented as days. Column  76  (IND_LAST_SCAN_DAYS) is the IND_LAST_SCAN_HRS represented as days and column  77  (PALLET) identifies the pallet the mail piece is in for the mailing. Column  78  (BAG) identifies the bag the mail piece is in for the mailing.  
         [0064]     In  FIG. 10F  column  79  (BUNDLE) identifies the bundle the mail piece is in Column  80  (TIER) i.e., C=carrier route, P=presort 3 or 5 digit, R=residential and column  81  (AUTO_NON_AUTO) indicates if the mail piece has an automation compatible post-net code, where A=zipcode plus 4 plus 2 and N=zip code. Column  82  (PRESORT_TYPE) is the presort order assigned to the mail piece and column  83  (PRESORT_ZIP) is the zip code for the specific presort type in column  82 . Column  84  (MODELED_IN_HOME_DATE) is the calculated in home date, see  FIG. 12 .  
         [0065]     Mail piece level data ( FIGS. 10A-10F ) is combined or aggregated into container level data and tabulated as shown in  FIGS. 11A-11D .  
         [0066]      FIGS. 11A-11D  depicts sample data representative of the mailing container level data shown in step  1580  ( FIG. 8 ) in tabular form. In  FIG. 11A  the location of the induction facility for the mailing shipment is shown in column  85 . Each row in  FIGS. 11A-11D  is representative of an aggregation of containers of mail pieces represented in rows in  FIGS. 10A-10F  (belonging to the container). The location of the processing facility of the mailing shipment is shown in column  86 . The type of induction facility i.e., BMC, Auxiliary Sectional Facility (ASF) or SCF is shown in column  87 . The sort level performed on the mail pieces, i.e., Enhanced Carrier Route (ECROLT), three digit sort level (AUTO**3-Digit), Auto Carrier Route (AUTOCR), five digit sort level (AUTO**5-Digit) are shown in column  88 . The induction date of the shipment for the container is shown in column  89 . The induction day of week (DOW) is shown in column  90 .  
         [0067]     In  FIG. 11B  is the induction tour when the shipment was inducted Foreign Key (FK) for the container is shown in column  91  and the induction Day Of Week (DOW) for the container is shown in column  92 . The induction MOY month of year (MOY) for the container is shown in column  93  and the induction year-FK for the container is shown in column  94 . The mail piece count for the shipment is shown in column  95 . The percentage of the container mail pieces that arrived on the induction day (Day0) In home is shown in column  96 .  
         [0068]     In  FIG. 11C  the percent of mail pieces that are in the home one day after postal induction is shown in column  97  and the percent of mail pieces that are in the home two days after postal induction is shown in column  98 . The percent of mail pieces that are in the home three days after postal induction is shown in column  99  and the percent of mail pieces that are in the home four days after postal induction is shown in column  100 . The percent of mail pieces that are in the home five days after postal induction is shown in column  101  and the percent of mail pieces that are in the home six days after postal induction is shown in column  102 . The percent of mail pieces that are in the home seven days after postal induction is shown in column  103  and the percent of mail pieces that are in the home eight days after postal induction is shown in column  104 .  
         [0069]     In  FIG. 11D  the percent of mail pieces that are in the home nine days after postal induction is shown in column  105  and the percent of mail pieces that are in the home ten days after postal induction is shown in column  106 . The percent of mail pieces that are in the home eleven days after postal induction is shown in column  107  and the percent of mail pieces that are in the home twelve days after postal induction is shown in column  108 . The percent of mail pieces that are in the home beyond the second week of postal induction is shown in column  109  and the ready for training flag shown in column  110  indicates when the record can be used as historical container level delivery curves as shown in step  1550  ( FIG. 8 ).  
         [0070]      FIG. 12  is a flowchart indicating how the In Home Date is calculated for a mail piece, and saved in space  69 , IN_HOME_DATE, in  FIG. 10D  and is also used to calculate MODELED_IN_HOME_DATE in space  84  in  FIG. 10F .  
         [0071]     The process is applied to each mail piece that is scanned and starts in step  3000  and is followed by step  3020 , where the last scan for the mail piece is loaded from step  3010 , Mail piece Last Scan Date from USPS Confirm System. Next, step  3030  initializes the In Home Date for the mail piece as the Last Scan Date and then if step  3040  determines if the mail piece scan occurred after the delivery cut-off time for that facility, step  3050  will add  24  hours to the in home date, since the mail piece will not be delivered on the same day. Next if step  3060  determines that the In Home Date falls on a no-delivery date, such as a Sunday, Holiday, or exception date, etc, step  3070  will use the next available delivery date is used as the In Home Date for the mail piece.  
         [0072]     The process continues at step  3080  where the calculated In Home Date is saved to space  69  in  FIG. 10D , as shown in step  3090 . Finally, the process ends in step  3095 .  
         [0073]      FIG. 13A and 13B  is a table of drop shipment appointment close out data, which is used to calculate the actual mail shipment induction date as described in  FIG. 3 . Space  33  indicates the shipment confirmation number and space  34  indicates the appointment status of the shipment, with states of Closed, No Show, or Open, etc. Space  35  indicates the header for space  35   a , the name of the facility where the shipment is scheduled to arrive. Space  36  is the header for space  36   a , the date and time when the truck arrived. Space  37  is the header for space  37   a , the date and time when the truck started to be unloaded  
         [0074]     Space  38  is the header for space  38   a , the date and time when the truck completed unloading. Space  39   a  is the header for Space  39   a , the Trailer Number, identifying the truck that delivered the mail.  
         [0075]      FIG. 14A  is a flow chart of a Process for controlling a mailing campaign.  
         [0076]     In  FIG. 14 A , the customer provides mailing campaign data file in step  500  describing the mail pieces in each shipment of the mailing campaign. A mailing campaign consists of one or more shipments. Each shipment consists of a number of trays or containers of mail sorted to some density for instance 3-digit zip code level, 5-digit zip code level, or AADC level. Further, each shipment is to be inducted at a specific BMC of Sectional Control Facility (SCF). Each tray or container consists of one or more mail pieces. Of those mail pieces, one or more mail piece in each tray are uniquely identified with a bar code or bar codes uniquely identifying that mail piece. Those bar codes are in a format that is scanned and stored by the USPS. The mail campaign data include may custom formats such as a comma delimited flat file or an XML formatted data file, or may follow an industry standard such as Mail.dat. The customer also inputs to the system the desired days that the recipient is to receive the mail piece in step  530 . The recipient target interval may be specific days of a week or specific dates. For instance, the recipient population is to receive the mail piece on a Tuesday or Wednesday or the recipient is to receive the mail piece on the 13 th  or 14 th  of January, 2005. The system shall accept inputs spanning one or more desired in-home days or dates.  
         [0077]     The induction planner in step  510  using a model of the processing pattern of all facilities in the system determines the best day of the week to induct the mail at each of the target facilities. Step  510  is described in more detail in  FIG. 14B . The system also accepts manual or automated exception event inputs containing postal holidays in step  575  and in step  570  catastrophic events that may shut down or seriously impede the postal system&#39;s ability to process mail. In step  580  the data is stored in an exception data file or database and accessed by the induction planner. Further, the system takes as an input the logistics schedule of the shipping provider for the mailer in step  550  and stores that data in step  560  using a method that allows access by the induction planning software. The logistics schedule of the shipping provider is the route schedule for that transportation firm. The system, is able to plan the induction schedule for the mail around the dates that the logistics provider actually inducts mail with the destination facility or facilities. It is not uncommon for the logistics providers to take mail to some facilities daily and some other facilities as infrequently as once per week.  
         [0078]     Given all of the inputs, the system calculates an induction plan in step  510  containing the date to induct the mail for each destination facility within the USPS. Further, the system outputs an anticipated arrival curve for each container or shipment or the mailing campaign as a whole or a part of the campaign. The anticipated arrival curve provides the mailer with a realistic idea for when the mail will arrive with the recipient population given logistics constraints, postal processing variability, postal holidays and catastrophic events.  
         [0079]     Once the mailer instructs the shipper when to induct the shipments at each destination processing facility the system monitors the USPS system in step  590  to measure when the shipment(s) were actually inducted. Step  590  is described in further detail in  FIG. 3  and step  620  in described in further detail in  FIG. 4 . Additionally, the system monitors the DSAS system in step  620  for facility status information which may delay the processing and ultimately delivery of mail to the recipients of that mail. Periodically, the system accesses the stored induction and facility status data in step  600  and updates the anticipated in-home curves in step  610 .  
         [0080]     Once the mail is accepted, those pieces containing scannable bar codes are processed and tracked through the USPS. The USPS reports that scan information for each scannable piece. The scanned data in step  650  is downloaded to the system and tied to the customer mail piece data in step  670  through an appropriate database in step  660 . The system then uses that data to generate reports containing when the prospect population is in fact receiving the mail pieces. Further that data is used to create conformance reporting back to the mailer in step  640  demonstrating how much mail was in-homed within the desired window.  
         [0081]     The delivery results of the mailing campaign including shipment and mail piece information are then used to update the induction planning model in step  540  thus refining the induction planner&#39;s in step  510  future capability to accurately determine when mail is to be inducted to achieve desired delivery dates.  
         [0082]      FIG. 14B  is a flow chart of an algorithm for controlling the mail. The process begins in step  2000  control mailing. Then in step  2005  mailing shipments are retrieved from step  2110 . Now in step  2010  each shipment from step  2065  is processed one shipment at a time. Then in step  2020  the data associated with the make up of the shipment from step  2110  is retrieved. The retrieved data includes the induction facility and the mail piece count. In step  2030  the identity of the containers in the shipment are retrieved from step  2120  mailing container level data.  
         [0083]     Now in step  2040  each container in the shipment is processed. Then step  2050  the data associated with the make up of the container from step  2120  is retrieved. This data includes the container processing facility, destination facility, sort level, mail pieces in the container and make up of the mail piece. Then in step  2060  the historical level delivery curve associated with the container in step  2050  is retrieved from step  2130  historical delivery data. The historical delivery curve is conveyed as a proportional curve that indicates the percentage of mail pieces delivered each day.  
         [0084]     In step  2070  the mail pieces delivered per day for this container is calculated by multiplying the mail piece counts in the container by the historical container delivery curve. Then, step  2080  adds the container delivery curve calculated in step  2070  to the shipment delivery curve. Now step  2090  determines whether or not there are more containers to be processed in the shipment. If step  2090  determines there are more containers in the shipment to be processed, the next step will be step  2040 . If step  2090  determines there are no more containers in the shipment to be processed, the next step will be step  2300  to determine the best shipment induction date. Step  2300  is more fully described in the description of  FIG. 15 .  
         [0085]     Then the process goes to step  2100  to determine whether or not there are more shipments in the mailing campaign. If step  2100  determines that there are more shipments in the mailing campaign the next step is step  2010 . If step  2100  determines that there are no more shipments in the mailing campaign the next step is step  2140  which prints an induction plan for execution. Now in step  2150  the mailing control algorithm is completed.  
         [0086]      FIG. 15  is a flow chart showing how to determine the best shipment induction date as used by the algorithm in  FIG. 14B . The process begins at step  2300  determine best shipment induction date. Then in step  2310  data is retrieved for the desired in home window. At this time data is exchanged between step  2310  and step  2430  desired in home window to specify the date range when most of the mail needs to be delivered. Now in step  2320  the process builds a list of all the possible in home window locations over the shipment delivery curve, calculating the percentage of mail delivered inside the window for each window location. The in house window locations are sorted from best to worst, i.e., from most mail delivered to least mail delivered in the window.  
         [0087]     In step  2330 , the induction date is determined for each in home window location taking into account Sundays and holidays. Then step  2340  retrieves the USPS facility acceptance schedule. Step  2340  exchanges information with step  2440  USPS facility acceptance schedule. At this point the process goes to step  2350 . Step  2350  determines whether or not the USPS facility accepts mail on the induction date. If step  2350  determines that mail is accepted on the induction date, the process goes to step  2360  to retrieve the drop ship schedule. Step  2360  exchanges information with step  2450  drop shipper schedule. Then the process goes to step  2370 . Step  2370  determines whether or not the drop shipper can deliver the shipment to the induction facility on the induction date. If step  2370  determines that the shipper can deliver the shipment on the induction date the process goes to step  2400  update shipment desired induction date. The next step will be step  2460  return. If step  2370  determines the drop shipper can not deliver the shipment on the induction date or if step  2350  determines that the USPS facility does not accept mail on the induction date then, the next step is  2390 .  
         [0088]     If decision step  2390 , determines that the next highest in home window location does not exist, the process goes to step  2420 , where the shipment is flagged as there is no known induction for the specified in home window. Then the process goes to step  2460  return.  
         [0089]      FIG. 16  is a flow chart showing how to predict daily call center volumes for a mailing. The process begins in step  2501 , predict call center volumes. Then in step  2511 , the mailing prediction is retrieved from step  2581 , Mailing Prediction. The Mailing Prediction that is provided is an updated Mailing Prediction accounting for any known changes in the mailing campaign, including updated induction dates, facility status, etc. The updated Mailing Prediction is merged with the Actual In Home curve as it is determined to date; and gradually, predicted in home volumes are replaced with actual results. Therefore, the Mailing Prediction allows predicted call center volumes to be updated as the campaign progresses so that corrective action can be taken at the call center with staffing or resources if necessary. Now in step  2551 , the historical call response delay curve is retrieved from step  2601 , the historical call response behavior. The historical call response delay curve provides daily rates for responses to mail pieces arriving on a specific day; that is, some recipients will respond the day that the mailpiece arrives, others on the next day, others two days later, and so on.  
         [0090]     Now the process goes to step  2561 , to calculate the predicted calls per day curve. The historical call response delay curve is applied to the mail pieces that were predicted to arrive on each day of the campaign. In other words, the mail pieces arriving each day are distributed across a range of days, based on the call response delay curve, in order to determine the call response delay distribution for that day. The predicted calls per day curve (i.e. call response delay distribution for the entire campaign) is calculated by adding the call response delay distribution for each in-home day of the campaign. See  FIGS. 21A and 21B .  
         [0091]     At this point, the predicted calls per day indicates that all of the recipients will respond to the mailing, the next step will scale the results by applying one or more historical call response rates. Now in step  2521 , the historical call response rates are retrieved from step  2591 , historical call response rates. Then in step  2541 , anticipated calls are calculated by multiplying predicted calls per day by the response rate. Next in step  2542  create calls per day prediction will merge the anticipated calls calculated in step  2541  with the daily actual call volumes measured at the center in step  2543 , by giving higher priority to the actual call results. Finally, in step  2571 , the calls per day prediction is produced, based on the merged anticipated calls and actual calls that were calculated in steps  2541  and  2543  respectively. After producing the calls per day prediction, the process ends in step  2561  end predict call center volumes.  
         [0092]      FIG. 17  is a flow chart showing how to control daily in-home mail volumes in order to achieve daily call volumes. The process begins in step  2499  call center control, then the process continues in step  2500 , retrieve desired daily call center volume (how many call center calls do you want a day). Then the process goes to step  2510 , to retrieve historical call response rate from historical call response rate, step  2580 . Now the process goes to step  2520 , divide desired daily call center volume by historical call response rate (desired responses per day). In step  2540 , the historical call response delay curve is retrieved from step  2590 , historical call response behavior. Then in step  2545 , the process sums the response delays based on the length of the desired campaign in home window. In step  2550 , the process calculates the required in home window mail volume, by dividing desired responses per day by the sum of the response delays. Now in step  2555  the mailing campaign control algorithm is executed to produce an induction plan that will generate the in home volumes that were calculated in step  2550 . Step  2555  is described in further detail in  FIG. 14B . Step  2555  will also take into account placing the in home volumes at the correct tome and date so that the required call volumes are generated when expected, i.e., if you want the call center volumes to peak on February 15 th  to February 16 th , the peak mail volumes must arrive some time before February 16 th . Then in step  2560 , the required daily in home mail volume curve is produced. Then step  2600  ends the call center control.  
         [0093]      FIG. 18  is a daily response curve showing call center response delays associated with in-home mail pieces. The curve shows. the probability of a recipient responding X days after receiving a mail piece. The X axis is the number of days after receiving the mail piece and the Y axis is the likelihood that a recipient will respond on that day. This curve is applied in step  2561  of  FIG. 16  to calculate the predicted distribution of calls for the mail pieces arriving on each one of the in-home days of a mailing. This curve can be further divided based on seasonality, day of week, geographical location, weather conditions, etc.  
         [0094]      FIG. 19  is a table showing the information in  FIG. 18  in tabular form. The table illustrates the percentage of respondents per day for mail pieces arriving in home on a given day. The historical response delay curve need not be limited to 10 days of delay, instead, it can long enough to account for a specific amount of responses, such as 90%.  
         [0095]      FIG. 20  is a table showing how the historical response delay curve is applied to the in home volume for each day in the mailing campaign. The rows in the table show the mail for each day in the mailing campaign, totaling 11 days, where 100,000 pieces arrived in home on each day. The columns in the table show the distribution of responses for each in home day, by applying the historical response delay curve. It is important to note though, that the delayed response volumes will need to be shifted based on the day when mail pieces arrived. This is explained in  FIG. 21A  and  FIG. 21B .  
         [0096]      FIG. 21A and 21B  depicts an offset in the data in  FIG. 20  and then sums the in-home quantities and multiplies the sum by the response rate, which obtains the predicted calls per day. The rows are the same as shown in  FIG. 20 , except that they have been shifted so that the response distribution starts on the day when the mail pieces arrived, for each in home day. The 21 columns represent each day when calls are predicted to arrive into the call center, and the response rate is used to calculate the predicted number of calls for each day in the predicted response curve. The table uses a sample response rate of 0.03%, but in application, the response rate can be applied based on historical analysis, for example, based on day of week, geographical location, weather, etc.  
         [0097]     It should be understood that although the present invention was described with respect to mail processing by the USPS, the present invention is not so limited and can be utilized in any application in which mail is processed by any carrier. The present invention may also be utilized for mail other than direct marketing mail, for instance, transactional mail, i.e., bills, charitable solicitations, political solicitations, catalogues etc. Also the expression “in-home” refers to the recipient&#39;s residence or place of business.  
         [0098]     The above specification describes a new and improved method for predicting call center volumes. It is realized that the above description may indicate to those skilled in the art additional ways in which the principles of this invention may be used without departing from the spirit. Therefore, it is intended that this invention be limited only by the scope of the appended claims.