FIELD OF THE INVENTION
This invention relates to mailing mail pieces and, more particularly, to controlling the day of the week when a mail piece is delivered to a recipient.
BACKGROUND OF THE INVENTION
Direct marketers have used the mail to sell products to customers for almost as long as there has been mail. For direct marketers the USPS is viewed as a black box where the time required to process and deliver the mail is based on guess work and rule of thumb. Where First class mail has delivery standards associated with it, Standard class mail has less stringent delivery standards associated with it. For most of the country First class mail will be processed and delivered within three days. Once the USPS accepts Standard mail the time to process and deliver the mail will be from 1 to 14+ days. Direct marketers have learned to live with this lack of real knowledge when a mailing will be delivered in home or place of business.
A disadvantage of the prior art is that direct marketers use rules of thumb to determine in home or place of business or place of business date range for a mailing, which is not very accurate. One of the methods used is to base in home or place of business volumes on when the mailing was shipped from the mail production facility to the USPS induction facility, i.e. when the mailing is dropped. In home or place of business volumes would be so many days after the mailing dropped, such as from 1 to 10 days from the mailing drop date.
Another method used is to add seeds to the mailing to determine when the seeded mail is delivered and assign that delivery date to all the mail going to that destination city, state or all the mail in the tray the seed is in. Seeding involves sending a mail piece to a known address of a service firm and having the firm date stamp the mail piece and send the mail piece back to the direct mail marketer. A large number of seeds would be 200 or so which is not enough to cover the 350 USPS Destination Sectional Control Facilities in the United States. The direct mail marketer then infers the in-home dates for the mailing as a whole by correlating the shipment date of the mail (when it leaves the letter shop) and when the seed indicated that they received the mail piece. The direct mail marketer then assumes that all mail going to the area that the seed is in arrives on the same day or on some window around the seed date.
Another disadvantage of the prior art is that a mailer is unable to control when the mail will be delivered to a recipients home or place of business. A further disadvantage of the prior art is that a mailer does not know when the mail piece arrived at the recipient's home or place of business.
SUMMARY OF THE INVENTION
This invention overcomes the disadvantages of the prior art by controlling when a direct marketing prospect will receive a mail piece. The foregoing is accomplished by establishing when to induct mail at each of the many Destinations Bulk Mail Centers (BMC) in the United States; establishing when to induct mail at each of the many, i.e., 350 Destination sectional Control facility (SCF) in the country; establishing the achievable service level—percentage of mail that can be expected to arrive in the desired in-home window that the direct mail marketer is trying to achieve.
An advantage of this invention is that it accounts for seasonal variability in mail delivery performance based upon USPS staffing and system loading.
An additional advantage this invention is that it accounts for the sortation density of all trays of mail within the mailing.
A further advantage of this invention is that it accounts for where the mail is going in terms of destination zip codes and USPS performance against those zip codes.
A still further advantage of this invention is that it accounts for and adjust expected in home or place of business curves for non-controllable circumstances such as natural events or national security issues.
This invention also takes into consideration: the impact that private logistics companies have on trucking, storing and ultimately inducting standard ‘A’ mail; the impact that when the USPS will actually accept truck loads of mail from high volume mailers; the shape, weight and format of the mail; and the conformance of the mail to USPS automation processing standards.
This invention overcomes the disadvantages of the prior art by determining when the prospect receives the offer; determining the day of week or day of month that produces the highest response rate; and determining prospect behavior in terms of gap between receiving the offer and acting on it.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a flow chart of a prior art direct mail marketing process;
FIG. 2 is a flow chart showing how to predict recipient delivery distribution for a mailing;
FIG. 3 is a flow chart that generates the actual mail shipment induction date and triggers a prediction update.
FIG. 4 is a flow chart that loads facility conditions and status information and triggers prediction updates if changes are detected.
FIG. 5 is a actual vs predicted in-home curve for controlled mailing.
FIG. 6 is a drawing showing the predicted vs partial actual in-home curves for a controlled mailing.
FIG. 7A is a mailing facility condition plant report.
FIG. 7B is a mailing facility loading plant report.
FIG. 8 is a flow chart showing how to compile historic USPS container level delivery data.
FIG. 9A is a drawing showing curves generated for the Dallas Tex. BMC.
FIG. 9B is a drawing showing curves generated for the Denver Colo. BMC.
FIG. 9C is a drawing showing curves generated for the Los Angles Calif. BMC.
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).
FIGS. 11A-11D depicts sample data representative of the mailing container level data shown in step 1580 (FIG. 8) in tabular form.
FIG. 12 is a flow chart showing how to determine the in-home date for a mail piece.
FIGS. 13A-13B is a table of drop shipment appointment close out dates.
FIG. 14A is a flow chart of a Process for controlling a mailing campaign.
FIG. 14B is a flow chart of an algorithm for controlling the mail.
FIG. 15 is a flow chart showing how to determine the best shipment induction date as used by the algorithm in FIG. 14B.
DETAILED DESCRIPTION OF THE INVENTION
Referring now to the drawings in detail and, more particularly, to Prior Art FIG. 1, the process begins in step 1, 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 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 printing the addresses 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.
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.
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.
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.
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.
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.
FIG. 5 is an actual vs predicted in-home curve for controlled mailing.
FIG. 6 is a drawing showing the predicted vs partial actual in-home curves for a controlled mailing.
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).
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.
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.
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.
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.
Space 907 is the row where the Totals are tallied for each column.
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.
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.
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.
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
FIGS. 9A-9C show example curves generated for BMC's and SCF'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's and/or SCF across different regions.
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.
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).
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.
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.
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.
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.
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.
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.
Mail piece level data (FIGS. 10A-10F) is combined or aggregated into container level data and tabulated as shown in FIGS. 11A-11D.
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 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.
In FIG. 11 B 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 location of the processing facility of the mailing shipment is shown in column 86. 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.
In FIG. 11 C 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.
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).
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.
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.
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.
FIGS. 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 35a, the name of the facility where the shipment is scheduled to arrive. Space 36 is the header for space 36a, the date and time when the truck arrived. Space 37 is the header for space 37a, the date and time when the truck started to be unloaded.
Space 38 is the header for space 38a, the date and time when the truck completed unloading. Space 39a is the header for Space 39a, the Trailer Number, identifying the truck that delivered the mail.
FIG. 14A is a flow chart of a Process for controlling a mailing campaign. In FIG. 14A, 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 MDC 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 or 14 Jan., 2005. The system shall accept inputs spanning one or more desired in-home days or dates.
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 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'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.
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.
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.
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.
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's in step 510 future capability to accurately determine when mail is to be inducted to achieve desired delivery dates.
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 2005 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.
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.
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.
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.
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.
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.
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.
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's residence or place of business.
The above specification describes a new and improved method for enabling a mailer to control when mail will arrive at a recipient's home or place of business on a given date. 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.
Patent Application
20070192258
