Patent Application
20200334767
The present disclosure relates to systems and methods for using information obtained through time-limited events among quick service restaurants, and, for example, for improving service metrics of the quick service restaurants.
Quick service restaurants are known. Measuring how long it takes to provide service to individual customers at quick service restaurants is known. Comparing performance measurements between, e.g., employees, is known.
One aspect of the present disclosure relates to a system configured for using information obtained through time-limited events among quick service restaurants. The system may include one or more hardware processors configured by machine-readable instructions. The processor(s) may be configured to obtain sets of values of one or more service metrics that are related to service durations at a first quick service restaurant. Individual sets of values may correspond to individual periods (e.g., day parts, days, weeks, months, years, etc.) during which the values may have been determined at the first quick service restaurant. The sets may include a first set, a second set, and/or other sets. The first set may correspond to a first period (e.g., a week or a month) that occurred before a first time-limited event (e.g., a week-long contest). The second set may correspond to a second period that occurred after the first period (e.g., the week or month after the contest). The processor(s) may be configured to analyze the obtained set of values to determine one or more effects that are attributed to holding the first time-limited event (e.g., a decrease in the average service duration for vehicles in a drive-thru). Individual ones of the one or more effects may correspond to one or more changes in the values of the one or more service metrics between the first set and the second set. The processor(s) may be configured to determine a first recommendation for a future time-limited event to be held (e.g., a different contest). Participants of the future time-limited event may include the first quick service restaurant. The first recommendation may include first event information that characterizes the future time-limited event (e.g., the different contest may be longer or shorter, use different awards, etc.). The first event information may include a first event objective for the future time-limited event. Determination of the first event objective may be based on the determined one or more effects. The processor(s) may be configured to effectuate a first presentation to an event administrator (e.g., prompt the event administrator to hold another event, such as the different contest). The first presentation may include information based on one or more of the determined first recommendation, the first event information, the determined one or more effects, the first event objective, and/or other information.
Another aspect of the present disclosure relates to a method for using information obtained through time-limited events among quick service restaurants. The method may include obtaining sets of values of one or more service metrics that are related to service durations at a first quick service restaurant. Individual sets of values may correspond to individual periods during which the values may have been determined at the first quick service restaurant. The sets may include a first set, a second set, and/or other sets. The first set may correspond to a first period that occurred before a first time-limited event. The second set may correspond to a second period that occurred after the first period. The method may include analyzing the obtained set of values to determine one or more effects that are attributed to holding the first time-limited event. Individual ones of the one or more effects may correspond to one or more changes in the values of the one or more service metrics between the first set and the second set. The method may include determining a first recommendation for a future time-limited event to be held. Participants of the future time-limited event may include the first quick service restaurant. The first recommendation may include first event information that characterizes the future time-limited event. The first event information may include a first event objective for the future time-limited event. Determination of the first event objective may be based on the determined one or more effects. The method may include effectuating a first presentation to an event administrator. The first presentation may include information based on one or more of the determined first recommendation, the first event information, the determined one or more effects, the first event objective, and/or other information.
As used herein, any association (or relation, or reflection, or indication, or correspondency) involving servers, processors, client computing platforms, timing information, service durations, events, periods, times, dates, contests, challenges, participants, service metrics, values for service metrics, ranking orders, user interfaces, presentations, representations, durations, completions, indicators, indications, persons, vehicles, results, awards, notifications, changes, recommendations, models, and/or another entity or object that interacts with any part of the system and/or plays a part in the operation of the system, may be a one-to-one association, a one-to-many association, a many-to-one association, and/or a many-to-many association or N-to-M association (note that N and M may be different numbers greater than 1).
As used herein, the term “obtain” (and derivatives thereof) may include active and/or passive retrieval, determination, derivation, transfer, upload, download, submission, and/or exchange of information, and/or any combination thereof. As used herein, the term “effectuate” (and derivatives thereof) may include active and/or passive causation of any effect, both local and remote. As used herein, the term “determine” (and derivatives thereof) may include measure, calculate, compute, estimate, approximate, generate, and/or otherwise derive, and/or any combination thereof.
These and other features, and characteristics of the present technology, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. As used in the specification and in the claims, the singular form of “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise.
In some implementations, system 100 may include one or more servers 102. Server(s) 102 may be configured to communicate with one or more client computing platforms 104 according to a client/server architecture and/or other architectures. Client computing platform(s) 104 may be configured to communicate with other client computing platforms via server(s) 102 and/or according to a peer-to-peer architecture and/or other architectures. Users may access system 100 via client computing platform(s) 104, one or more user interfaces 132, and/or one or more other components of system 100.
Server(s) 102 may be configured by machine-readable instructions 106. Machine-readable instructions 106 may include one or more instruction components. The instruction components may include computer program components. The instruction components may include one or more of metric component 108, analysis component 110, recommendation component 112, presentation component 114, event component 116, prediction component 118, model component 120, statistical component 122, similarity component 124, and/or other instruction components.
Metric component 108 may be configured to determine and/or obtain sets of values of one or more service metrics that are related to service durations at quick service restaurants 134, e.g., through aggregation, averaging, derivations, etc. In some implementations, metric component 108 may be configured to determine and/or obtain sets of values of one or more performance indicators that are related to the operation and/or performance of at quick service restaurants 134. For example, performance indicators may be monetary indicators and/or other business indicators. In some implementations, service metrics may be based on service timing information. In some implementations, service metrics and/or service timing information may be based on service durations for individual instances of service being provided at quick service restaurant 134.
Service durations may be defined by the time between a (service) start time or begin time and a (service) stop time or end time. In some implementations, an individual quick service restaurant 134 may be a drive-thru restaurant. In some implementations, the start time may be defined as the moment a particular vehicle enters the drive-thru (e.g., passes a particular point on the road surface of the drive-thru). In some implementations, the start time may be defined as the moment people in the particular vehicle begin or complete their order, or pay for their order. In some implementations, the end time of a service duration may be defined as the moment particular vehicle exits the drive-thru (e.g., passes a particular point on the road surface of the drive-thru). In some implementations, the end time may be defined as the moment people in a particular vehicle receive their order, or pay for their order. Start times and end times for different customers may be interleaved, such that individual service durations partially overlap with other service durations. Service durations may include a first service duration, a second service duration, a third service duration, and so forth. Vehicles may include a first vehicle, a second vehicle, a third vehicle, and so forth. In some implementations, individual instances of service being provided at a particular quick service restaurant 134 may include a first instance of service being provided to a first person in the first vehicle, a second instance of service being provided to one or more people in the second vehicle, and so forth. In some implementations, sets of values determined and/or obtained by metric component 108 may correspond to periods (e.g., day parts, days, weeks, months, years, etc.) that occurred before, during, or after events, such as time-limited events.
Events may include a first event, a second event, a third event, and so forth. In some implementations, events may include one or more contests, challenges, and/or other competitions. Events may be defined by event information. In some implementations, the event information may include one or more of event timing information, event participant information, event objective information for the event, event award information, and/or other information related to one or more events. Event timing information may specify one or more of an event start date, an event stop date, an event start time, an event stop time for the event, and/or other information related to event timing. By way of non-limiting example, the event timing information for an individual event may specify an event start time and an event stop time for the event, thereby defining an event duration between the event start time and the event stop time. In some implementations, the event timing information may specify an event start date and an event stop date, thereby defining an event date range. For example, the first event may be associated with a first event duration, the second event may be associated with the second event duration, the third event may be associated with the third event duration, and so forth. In some implementations, the event duration may be defined as a duration between 2 and 4 hours.
In some implementations, individual events may span multiple days. For example, a particular event may last a week, a month, or another multi-day period. In some implementations, a particular event may include individual rounds of competition occurring on different days. For example, a first contest may span every Friday from 11 a.m. to 2 p.m. for 3 months. For example, a second contest may span every Monday through Thursday from 6 a.m. to 10 a.m. for 2 months. For example, a third contest may span every Saturday and Sunday from 9 a.m. to 11 a.m., between an event start date and an event stop date that are about 10 weeks apart. In these examples, the portion of the contest that falls on a single day may be referred to as a round, or a daily round.
Event participant information may identify individual quick service restaurants that participated in one or more events. For example, a particular event may have included a particular quick service restaurant 134 and one or more other quick service restaurants (e.g., operated by the same franchisee, located in the same geographical region, owned by the same owner, and/or otherwise having one or more characteristics in common). For a particular event, the set of quick service restaurants that participated in the particular event may be referred to as the participating quick service restaurants or as the set of participating quick service restaurants.
Event award information may specify and/or identify one or more awards that can potentially be earned by and/or awarded to the individual quick service restaurants participating in a particular event. Some awards may be solely based on one or more service metrics for a single quick service restaurant. Some awards may be based on comparing one or more service metrics among multiple quick service restaurants (e.g., all participating quick service restaurants). Some awards may require a combination of two or more (sequential and/or contemporaneous) accomplishments.
Event objective information may specify one or more service metrics on which individual ones of the participating quick service restaurants competed during a particular event. In some implementations, event objective information may specify a service metric that was used to rank individual ones of the participating quick service restaurants during or after a particular event. In some implementations, service metrics may be based on service timing information. In some implementations, service metrics and/or service timing information may be based on service durations for individual instances of service being provided at quick service restaurant 134.
In some implementations, one or more service metrics may include one or more of average service duration per instance of service provided at an individual quick service restaurant, percentage of the instances of service provided for which the service duration was at or below a service duration goal, number of instances of service provided at an individual quick service restaurant, and/or percentage reached of a goal number of instances of service being provided at an individual quick service restaurant. In some implementations, one or more service metrics may be based (at least in part) on information from the one or more points-of-sale (e.g., total sales, average sales per instance of service, etc.). Service metrics that combine service duration and information from a point-of-sale (POS) are envisioned within the scope of this disclosure. Determining the values of the one or more service metrics may be performed (e.g., by individual quick service restaurants) during the event duration, during a predetermined time period, at the completion of the event duration, and/or at the completion of the predetermined time period. For example, the average service duration per instance of service provided at a particular quick service restaurant 134 for the first contest (described above) may have been determined by adding any service durations for instances of service provided on a Friday between 11 a.m. and 2 p.m., and dividing this total duration by the number of these instances.
In some implementations, metric component 108 may obtain multiple sets of values of service metrics corresponding to periods before, during, and/or after particular time-limited events for a particular quick service restaurant. For example, a first set of values may include the average service duration in the month prior to a first event, a second set of values may include the average service duration during the first event, and a third set of values may include the average service duration in the month after completion of the first event. Analysis of these three sets (e.g., by analysis component 110) may reveal short-term effects of holding the first event (e.g., a 20% decrease in average service duration during the first event), and longer-term effects of holding the first event (e.g., a 10% in average service duration when comparing the month before the first event to the month after the first event). A collection of multiple sets of values may be referred to as a superset of values.
In some implementations, metric component 108 may obtain multiple supersets of values of service metrics corresponding to periods before, during, and/or after multiple time-limited events for a particular quick service restaurant. For example, a first superset may include average service durations before, during, and after a first event, a second superset may include average service durations before, during, and after a second event, and so on. Analysis on multiple supersets (e.g., by analysis component 110) may reveal different effects that correspond and/or correlate to different characteristics of the particular events. For example, in some instances (for some quick service restaurants) week-long events may have little or no effect a month after the event, whereas month-long events may have longer-lasting effects, and 90-day-long events may have less long-term results than month-long events. Analysis of multiple supersets for different quick service restaurants may indicate a restaurant-specific preferable duration for events. For example, a first quick service restaurant may respond best to 10-day events, whereas a second quick service restaurant responds best to 3-week events. Accordingly, future events may be customized to use preferable restaurant-specific event characteristics. A collection of multiple supersets of values for different quick service restaurants may be referred to as a cluster-set of values, or a mega-superset of values.
By way of non-limiting example,
Referring to
By way of non-limiting example,
Referring to
Recommendation component 112 may be configured to determine recommendations for future events to be held. The recommendations may be based on information from analysis component 110, statistical component 122, and/or other components of system 100, including but not limited to one or more preferable restaurant-specific event characteristics. The recommendations may include event information such as event timing information, event participant information, event objective information, event award information, and/or other information related to events.
Presentation component 114 may be configured to effectuate presentations to users, including but not limited to event administrators. For example, a first presentation may include information based on a determined performance boost (due to a previously-held event). For example, a second presentation may include information based on a determined recommendation (by recommendation component 112), and/or any of the corresponding event information included in a recommendation. For example, a presentation may include one or more preferable restaurant-specific event characteristics as determined by system 100.
By way of non-limiting example,
Referring to
Prediction component 118 may be configured to predict expected sets of values of one or more service metrics for future events at quick service restaurants 134. In some implementations, an expected set of values may correspond to a new potential event as recommended (by recommendation component 112). Predictions may be based on a model for one or more quick service restaurants.
Model component 120 may be configured to create and/or modify a model for one or more quick service restaurants. In some implementations, modifications may be based on comparisons between expected sets of values and actual (i.e. measured) sets of values. In some implementations, models may be specific to individual quick service restaurants.
Statistical component 122 may be configured to determine one or more differences and/or distinctions between different effects that are attributed to holding different events. In some implementations, determining the one or more particular differences and/or distinctions may include quantifying an effect that is attributed to ranking order (or any other event-specific result) at completion of different events. For example, ranking second may have better long-term effects than ranking first for some quick service restaurants, or vice versa. In some implementations, multiple input variables (such as particular selections for event information) may interact in producing (performance) results for a particular quick service restaurant. In some implementations, multiple input variables may be independent of each other.
By way of non-limiting example,
Referring to
In some implementations, server(s) 102, client computing platform(s) 104, and/or external resources 126 may be operatively linked via one or more electronic communication links. For example, such electronic communication links may be established, at least in part, via one or more networks 13 such as the Internet and/or other networks. It will be appreciated that this is not intended to be limiting, and that the scope of this disclosure includes implementations in which server(s) 102, client computing platform(s) 104, and/or external resources 126 may be operatively linked via some other communication media.
A given client computing platform 104 may include one or more processors configured to execute computer program components. The computer program components may be configured to enable an expert or user associated with the given client computing platform 104 to interface with system 100 and/or external resources 126, and/or provide other functionality attributed herein to client computing platform(s) 104. By way of non-limiting example, the given client computing platform 104 may include one or more of a desktop computer, a laptop computer, a handheld computer, a tablet computing platform, a NetBook, a Smartphone, a gaming console, and/or other computing platforms.
External resources 126 may include sources of information outside of system 100, external entities participating with system 100, and/or other resources. In some implementations, some or all of the functionality attributed herein to external resources 126 may be provided by resources included in system 100.
Server(s) 102 may include electronic storage 128, one or more processors 130, and/or other components. Server(s) 102 may include communication lines, or ports to enable the exchange of information with a network and/or other computing platforms. Illustration of server(s) 102 in
Electronic storage 128 may comprise non-transitory storage media that electronically stores information. The electronic storage media of electronic storage 128 may include one or both of system storage that is provided integrally (i.e., substantially non-removable) with server(s) 102 and/or removable storage that is removably connectable to server(s) 102 via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). Electronic storage 128 may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. Electronic storage 128 may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and/or other virtual storage resources). Electronic storage 128 may store software algorithms, information determined by processor(s) 130, information received from server(s) 102, information received from client computing platform(s) 104, and/or other information that enables server(s) 102 to function as described herein.
Processor(s) 130 may be configured to provide information processing capabilities in server(s) 102. As such, processor(s) 130 may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. Although processor(s) 130 is shown in
It should be appreciated that although components 108, 110, 112, 114, 116, 118, 120, 122, and/or 124 are illustrated in
In some implementations, method 200 may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices executing some or all of the operations of method 200 in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of method 200.
An operation 202 may include obtaining sets of values of one or more service metrics that are related to service durations at a first quick service restaurant. Individual sets of values correspond to individual periods during which the values may have been determined at the first quick service restaurant. The sets may include a first set and a second set. The first set may correspond to a first period that occurred before a first time-limited event. The second set may correspond to a second period that occurred after the first period. Operation 202 may be performed by one or more hardware processors configured by machine-readable instructions including a component that is the same as or similar to metric component 108, in accordance with one or more implementations.
An operation 204 may include analyzing the obtained set of values to determine one or more effects that are attributed to holding the first time-limited event. Individual ones of the one or more effects correspond to one or more changes in the values of the one or more service metrics between the first set and the second set. Operation 204 may be performed by one or more hardware processors configured by machine-readable instructions including a component that is the same as or similar to analysis component 110, in accordance with one or more implementations.
An operation 206 may include determining a first recommendation for a future time-limited event to be held. Participants of the future time-limited event may include the first quick service restaurant. The first recommendation may include first event information that characterizes the future time-limited event. The first event information may include a first event objective for the future time-limited event. Determination of the first event objective may be based on the determined one or more effects. Operation 206 may be performed by one or more hardware processors configured by machine-readable instructions including a component that is the same as or similar to recommendation component 112, in accordance with one or more implementations.
An operation 208 may include effectuating a first presentation to an event administrator. The first presentation may include information based on one or more of the determined first recommendation. The first event information, the may be determined one or more effects, and/or the first event objective. Operation 208 may be performed by one or more hardware processors configured by machine-readable instructions including a component that is the same as or similar to presentation component 114, in accordance with one or more implementations.
In some implementations, a system as described in this disclosure may be used for customer-oriented businesses that are not quick service restaurants, provided there are defined moments an instance of service being provided starts and ends. Stores, pharmacies, medical offices, and/or other types of customer-oriented businesses may measure service durations and used these measurements to define service metrics and/or other metrics, which may in turn form the basis for the definition of time-limited events, ranking orders, one or more user interfaces similar to the user interfaces described above, one or more awards, and/or any other entity or object described herein.
Although the present technology has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred implementations, it is to be understood that such detail is solely for that purpose and that the technology is not limited to the disclosed implementations, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present technology contemplates that, to the extent possible, one or more features of any implementation can be combined with one or more features of any other implementation.
