Patent Publication Number: US-2019180309-A1

Title: Method and system for optimzing customer engagement

Description:
CROSS-RELATED APPLICATIONS 
     This application claims priority of Indian Application Serial No. 201741043832, filed Dec. 7, 2017, the contents of which are incorporated herein by reference. 
     FIELD OF THE INVENTION 
     The present invention relates generally to the field of transportation services, and more particularly, to a method and a system for optimizing customer engagement in a transportation service. 
     BACKGROUND 
     Travel services, in particular on-demand cab services, have to optimize their revenues by balancing demands from customers and supply of the available vehicles for providing efficient services to the customers. Generally, vehicles are booked either on an individual basis or on shared basis. On individual basis, a customer or a small group of customers traveling towards the same destination book a vehicle, whereas on shared basis, multiple customers share the vehicle to reach their respective destinations. 
     With improvements in lifestyle of the customers and limited alternatives of public or private transportations, popularity of the cab services is continuously increasing. With increased demand for the cab services, various cab service providers have come into the market to provide the cab services to the customers. The cab service providers try to entice the customers with various offers, which may attract the customers to use their respective cab services. With such a healthy competition among the cab service providers, the customers are bound to split. Further, the customers are cost conscious and tend to select a cab service provider that charges the least for the cab service. In such a case, the customer may be ready to compromise on comfort level during the ride. However, from the perspective of the cab service provider, it is essential to retain the customers who may have used their cab services, and also attract the new customers. The retention of the old customers and attracting the new customers may motivate the cab service provider to continue providing the cab services to the customers. Therefore, the cab service provider may wish to engage the customers (i.e., the existing, left-over, or new customers) for a longer duration of time, which in turn can enhance their business opportunity and optimize the overall revenue. 
     In light of the foregoing, there exists a need for a technical and more reliable solution that solves the above mentioned problems and optimizes the customer engagement with the cab service provider. 
     SUMMARY 
     In an embodiment of the present invention, a method for optimizing customer engagement in a transportation service is provided. Historical travel data of a customer is extracted from a database server over a first communication network. One or more regular time intervals of travel by the customer are identified based on the extracted historical travel data. The one or more regular time intervals are identified by means of a density-based spatial clustering of applications with noise (DBSCAN) technique. Regular and random travel data of the customer are detected from the extracted historical travel data, based on the one or more regular time intervals. Regular and random capacities of the customer are determined based on the regular and random travel data, respectively. A life-time value (LTV) of the customer is determined based on at least the determined regular and random capacities. Targeted content is recommended to the customer by way of transmitting the targeted content to a customer-computing device of the customer over a second communication network. The targeted content is determined based on at least the determined LTV to optimize the customer engagement. 
     In another embodiment of the present invention, a system for optimizing customer engagement in a transportation service is provided. The system comprises circuitry that is configured to perform method as described in the foregoing. The circuitry is configured to extract historical travel data of a customer from a database server over a first communication network. The circuitry is further configured to determine weekday and weekend travel data from the extracted historical travel data. The weekday and weekend travel data are determined based on a timestamp associated with each travel data in the extracted historical travel data. The circuitry is further configured to group the weekday and weekend travel data on to a first defined timeline in a week-wise manner to identify dense time intervals by means of a density-based spatial clustering of applications with noise (DBSCAN) technique. The circuitry is further configured to identify one or more regular time intervals of travel by the customer from the identified dense time intervals. The one or more regular time intervals are identified from the identified dense time intervals based on at least one of an age of a dense time interval, a conversion rate, or a count of demand or booking in the dense time interval. The circuitry is further configured to detect regular and random travel data of the customer from the extracted historical travel data. The regular and random travel data are detected based on the one or more regular time intervals. 
     The circuitry is further configured to determine regular and random capacities of the customer based on the regular and random travel data, respectively. The regular capacity is determined based on estimated values of a pick-up location, a drop-off location, a fare, and a travel distance associated with each travel data in the regular travel data. The random capacity is determined based on a weighted mean of estimated values of the random travel data with and without recency. The random capacity without recency is determined based on a gaussian kernel weighted estimated mean of the random travel data. The random capacity with recency is determined based on recent travel data associated with a second defined timeline. 
     The circuitry is further configured to determine an engagement level of the customer based on at least the regular and random capacities and a realized demand. Based on the engagement level of the customer, the circuitry is further configured to determine a life-time value (LTV) of the customer. The circuitry is further configured to rank customers based on at least one of the regular capacity, the random capacity, the LTV, or the engagement level of each of the customers. The customers are ranked to identify high, medium, or low risk customers. The circuitry is further configured to recommend targeted content to the customers based on at least the determined LTV to optimize the customer engagement. For example, the targeted content is recommended to the customers based on the ranking of the customers. Thus, the method and the system of the present invention provide at least one of the capacities, the LTV, or the engagement level of the customers in different time intervals. The capacities, the LTV, or the engagement level of the customers can be utilized to send real-time notifications to the customers for better utilization and engagement of the customers with the transportation service. Further, the capacities, the LTV, or the engagement level of the customers can be utilized to identify current states of the customers, and the targeted content can be recommended to the customers based on their current states, such as engaged, at risk, active, or inactive, to optimize the customer engagement with the transportation service. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings illustrate the various embodiments of systems, methods, and other aspects of the invention. It will be apparent to a person skilled in the art that the illustrated element boundaries (e.g., boxes, groups of boxes, or other shapes) in the figures represent one example of the boundaries. In some examples, one element may be designed as multiple elements, or multiple elements may be designed as one element. In some examples, an element shown as an internal component of one element may be implemented as an external component in another, and vice-versa. 
         FIG. 1  is a block diagram that illustrates an environment for optimizing customer engagement in a transportation service, in accordance with an embodiment of the present invention; 
         FIG. 2  is a block diagram that illustrates an application server of  FIG. 1 , in accordance with an embodiment of the present invention; and 
         FIGS. 3A and 3B , collectively, is a flow chart that illustrates a method for optimizing customer engagement in a transportation service, in accordance with an embodiment of the present invention. 
     
    
    
     Further areas of applicability of the present invention will become apparent from the detailed description provided hereinafter. It should be understood that the detailed description of exemplary embodiments is intended for illustration purposes only and is, therefore, not intended to necessarily limit the scope of the invention. 
     DETAILED DESCRIPTION 
     As used in the specification and claims, the singular forms “a”, “an” and “the” include plural references unless the context clearly dictates otherwise. For example, the term “an article” may include a plurality of articles unless the context clearly dictates otherwise. Those with ordinary skill in the art will appreciate that the elements in the figures are illustrated for simplicity and clarity and are not necessarily drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated, relative to other elements, in order to improve the understanding of the present invention. There may be additional components described in the foregoing application that are not depicted on one of the described drawings. In the event such a component is described, but not depicted in a drawing, the absence of such a drawing should not be considered as an omission of such design from the specification. 
     Before describing the present invention in detail, it should be observed that the present invention utilizes a combination of system components, which constitutes systems and methods for optimizing customer engagement in a transportation service. Accordingly, the components and the method steps have been represented, showing only specific details that are pertinent for an understanding of the present invention so as not to obscure the disclosure with details that will be readily apparent to those with ordinary skill in the art having the benefit of the description herein. As required, detailed embodiments of the present invention are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the invention, which can be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present invention in virtually any appropriately detailed structure. Further, the terms and phrases used herein are not intended to be limiting but rather to provide an understandable description of the invention. 
     References to “one embodiment”, “an embodiment”, “another embodiment”, “yet another embodiment”, “one example”, “an example”, “another example”, “yet another example”, and so on, indicate that the embodiment(s) or example(s) so described may include a particular feature, structure, characteristic, property, element, or limitation, but that not every embodiment or example necessarily includes that particular feature, structure, characteristic, property, element or limitation. Furthermore, repeated use of the phrase “in an embodiment” does not necessarily refer to the same embodiment. 
     A transportation service is a service in which a vehicle is provided to a customer to transit between a plurality of locations including source and destination locations specified by the customer. The vehicle is a means of transport that is deployed by a transport provider to provide the transportation service, such as an on-demand cab service, to the customer. For example, the vehicle may be an automobile, a bus, a car, a bike, and the like. Hereinafter, various methods of optimizing the customer engagement in the transportation service have been described that will become apparent to a person having ordinary skill in the relevant art. 
     Referring now to  FIG. 1 , a block diagram that illustrates an environment  100  for optimizing customer engagement in a transportation service, in accordance with an embodiment of the present invention is shown. The environment  100  includes a database server  102  and an application server  104  that are connected to each other by way of a first communication network  106 . Examples of the first communication network  106  include, but are not limited to, a wireless fidelity (Wi-Fi) network, a light fidelity (Li-Fi) network, a satellite network, an Internet, a mobile network such as cellular data, high speed packet access (HSPA), or any combination thereof. The environment  100  further includes a customer-computing device  108  and a driver-computing device  110  that are connected to the application server  104  by way of a second communication network  112 . Examples of the second communication network  112  include, but are not limited to, a Wi-Fi network, a Li-Fi network, a satellite network, an Internet, a mobile network such as cellular data, HSPA, or any combination thereof. In an exemplary embodiment, the environment  100  may include a single communication network, such as the first or the second communication network  106  or  112 . In such a scenario, the database server  102 , the application server  104 , the customer-computing device  108 , and the driver-computing device  110  may communicate with each other by way of the first or second communication network  106  or  112 . 
     The database server  102  is a data management and storage server that includes a processor (not shown) and a memory (not shown) for managing and storing historical travel data of one or more customers. The historical travel data of the one or more customers includes travel data of one or more rides that had been taken by the one or more customers in the past. In an exemplary embodiment, the historical travel data of a customer may include at least one of a pick-up location, a drop-off location, a pick-up time, a drop-off time, a ride fare, or a travel distance of the rides availed by the customer. The processor of the database server  102  receives and stores at least one of the pick-up location, the drop-off location, the pick-up time, the drop-off time, the ride fare, or the travel distance of the availed rides from the memory of the database server  102 . In an embodiment, the historical travel data of the customer is stored in a tabular data structure in the database server  102 . The tabular data structure may include one or more rows and one or more columns. The one or more columns may indicate time-wise pattern of travel by the customer, for example, day-wise, week-wise, month-wise, or the like. The one or more rows may indicate the historical travel related information of the customer associated with each time-wise pattern. The database server  102  further manages and stores customer information of the one or more customers (who had requested for the one or more rides) and driver information of one or more drivers (who had been selected for providing services to the one or more customers corresponding to the one or more rides). In an exemplary embodiment, the customer information of the customer may include at least a name, a contact number, or a customer account of the customer registered with the transportation service. Similarly, the driver information of a driver may include at least a name, a registered vehicle, or a driver account of the driver registered with the transportation service. In an embodiment, the database server  102  may receive a query from the application server  104  over the first communication network  106  to provide the historical travel data of the customer. In response to the received query, the database server  102  retrieves and provides the historical travel data of the customer to the application server  104  over the first communication network  106 . Examples of the database server  102  include, but are not limited to, a personal computer, a laptop, or a network of computer systems. 
     The application server  104  is a computing device, a software framework, or a combination thereof, that may provide a generalized approach to create the application server implementation. In an embodiment, the operation of the application server  104  may be dedicated to execution of procedures, such as, but are not limited to, programs, routines, or scripts stored in one or more memories for supporting its applied applications. In an embodiment, the application server  104  processes the historical travel data extracted from the database server  102 . Based on the processing of the extracted historical travel data, the application server  104  identifies one or more regular time intervals of travel by the customer. In an exemplary embodiment, the application server  104  may be configured to execute a density-based spatial clustering of applications with noise (DBSCAN) technique to identify the one or more regular time intervals. Further, the application server  104  detects regular and random travel data of the customer from the extracted historical travel data based on the one or more regular time intervals. Further, in an embodiment, the application server  104  is configured to determine regular and random capacities of the customer based on the regular and random travel data, respectively. The application server  104  further determines an engagement level of the customer based on at least the regular and random capacities. Based on the engagement level of the customer, the application server  104  determines a life-time value (LTV) of the customer. Further, in an embodiment, the application server  104  determines a current state (e.g., engaged, lost, active, or inactive) of the customer based on at least one of the regular and random capacities, engagement level, or the LTV of the customer. Based on the current state of the customer, the application server  104  recommends targeted content to the customer that optimizes the customer engagement with the transportation service. The targeted content may include one or more offers or discounts related to ride fares for the future rides or entertainment-related content, such as movies, music, and the like. The application server  104  may be realized through various web-based technologies such as, but not limited to, a Java web-framework, a .NET framework, a PHP framework, or any other web-application framework. Examples of the application server  104  include, but are not limited to, a personal computer, a laptop, or a network of computer systems. Various operations of the application server  104  have been described in detail in conjunction with  FIGS. 2, 3A, and 3B . 
     The customer-computing device  108  is a computing device that is utilized by the customer to perform one or more activities. For example, the customer utilizes the customer-computing device  108  to schedule a ride between a plurality of locations including source and destination locations. To schedule the ride, the customer uses the customer-computing device  108  to transmit a ride request for the ride by means of a service application installed on the customer-computing device  108 . The ride request includes ride-related information, for example, a pick-up location, a drop-off location, a waiting time, a vehicle type, or other service-related details and preferences. In a scenario when the pick-up location of the customer is the same as the source location, the passenger may not provide the pick-up location. In such scenario, the pick-up location is automatically captured by the application server  104  based on Global Positioning System (GPS) information transmitted by the customer-computing device  108  over the second communication network  112 . However, if the pick-up location is different from the captured source location, the customer may provide the pick-up location. The various modes of the input used by the customer may include, but are not limited to, a touch-based input, a text-based input, a voice-based input, a gesture-based input, or a combination thereof. Based on a confirmation of the ride request provided by the customer, the customer-computing device  108  transmits the ride request to the application server  104  over the second communication network  112 . Examples of the customer-computing device  108  include, but are not limited to, a personal computer, a laptop, a smartphone, a tablet computer, a personal digital assistant (PDA), and the like. 
     The driver-computing device  110  is a computing device that is used by the driver to perform one or more activities. For example, the driver uses the driver-computing device  110  to view an upcoming ride request from the customer. The driver further uses the driver-computing device  110  to accept or reject the upcoming ride request. Based on the acceptance of the ride request by the driver, the application server  104  allocates the ride request to the driver. The driver further uses the driver-computing device  110  to view a route between the pick-up and drop-off locations provided by the application server  104  over the second communication network  112 . The various modes of the input used by the driver may include, but are not limited to, a touch-based input, a text-based input, a voice-based input, a gesture-based input, or a combination thereof. In an exemplary embodiment, the driver-computing device  110  may be a vehicle head unit. In another exemplary embodiment, the driver-computing device  110  may be an external communication device, such as a smartphone, a PDA, a tablet computer, a laptop, or any other portable communication device, that is placed inside the vehicle of the driver. 
     Referring now to  FIG. 2 , a block diagram that illustrates the application server  104  of  FIG. 1 , in accordance with an embodiment of the present invention is shown. An embodiment of present invention, or portions thereof, may be implemented as computer readable code on the application server  104 . In one example, the application server  104  may be implemented using hardware, software, firmware, non-transitory computer readable media having instructions stored thereon, or a combination thereof and may be implemented in one or more computer systems or other processing systems. Hardware, software, or any combination thereof may embody modules and components used to implement the methods of  FIGS. 3A and 3B . The application server  104  includes a processor  202 , a communication interface  204 , communication infrastructure  206 , a main memory  208 , a secondary memory  210 , and an input/output (I/O) port  212 . 
     The processor  202  includes suitable logic, circuitry, and/or interfaces that are operable to execute one or more instructions stored in at least one of the main and secondary memories  208  and  210  to perform one or more operations. The processor  202  may be a special purpose or a general purpose processing device. The processor  202  may be a single processor, multiple processors, or combinations thereof. The processor  202  may have one or more processor “cores”. Further, the processor  202  may be connected to the communication infrastructure  206 , such as a bus, a bridge, a message queue, the first and second communication networks  1 - 6  and  112 , multi-core message-passing scheme, and the like. Examples of the processor  202  include an application-specific integrated circuit (ASIC) processor, a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a field-programmable gate array (FPGA), and the like. The processor  202  may be connected to the communication interface  204  for receiving and transmitting information (e.g., the historical travel data, the ride request, the targeted content, and the like) from the database server  102 , the customer-computing device  108 , and the driver-computing device  110  over at least one of the first or second communication network  106  or  112 . 
     The communication interface  204  includes suitable logic, circuitry, and/or interfaces that are operable to execute one or more instructions stored in at least one of the main and secondary memories  208  and  210  to perform one or more operations. The communication interface  204  may be configured to communicate with at least one of the first or second communication network  106  or  112  to receive and transmit the information from the database server  102 , the customer-computing device  108 , and the driver-computing device  110  that are communicatively coupled to the application server  104 . Examples of the communication interface  204  may include a modem, a network interface, i.e., an Ethernet card, a communications port, and the like. The information transferred via the communication interface  204  may be signals, such as electronic, electromagnetic, optical, or other signals as will be apparent to a person skilled in the art. The signals may travel via a communications channel, such as the first or second communication network  106  or  112 . Examples of the communication channel may include, but are not limited to, cable, fiber optics, a phone line, a cellular phone link, a radio frequency link, a wireless link, and the like. 
     The communication infrastructure  206  includes suitable logic, circuitry, and/or interfaces that are operable to execute one or more instructions stored in at least one of the main and secondary memories  208  and  210  to perform one or more operations. The communication infrastructure  206  may be a bus, a bridge, a message queue, multi-core message-passing scheme, and the like. Further, the processor  202  may be connected to the communication infrastructure  206 . The processor  202 , the main memory  208 , the secondary memory  210 , and the I/O port  212  may communicate with each other by way of the communication infrastructure  206 . 
     The main memory  208  includes suitable logic, circuitry, and/or interfaces to store the one or more instructions that are executed by the processor  202  to perform the one or more operations. The main memory  208  may further store one or more algorithms, procedures, codes, and the like that are executed by the processor  202  to perform the one or more operations. For example, the processor  202  may execute the one or more instructions, algorithms, procedures, codes, and the like stored in the main memory  208  to process the extracted historical travel data, identify the one or more regular time intervals, detect the regular and random travel data from the historical travel data, and determine the regular and random capacities, engagement level, and the LTV of the customer. Examples of the main memory  208  include a random access memory (RAM), a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), a Hard Disk Drive (HDD), and a Secure Digital (SD) card, and the like. In an embodiment, the main memory  208  is connected the secondary memory  210 . The secondary memory  210  may be configured to temporarily store the extracted historical travel data, the ride request, the targeted content, and the like. The secondary memory  210  may include a hard disk drive or a removable storage drive (not shown), such as a floppy disk drive, a magnetic tape drive, a compact disc, an optical disk drive, a flash memory, and the like. Further, the removable storage drive may read from and/or write to a removable storage device in a manner known in the art. In an embodiment, the removable storage unit may be a non-transitory computer readable recording media. 
     The I/O port  212  includes suitable logic, circuitry, and/or interfaces that are operable to execute one or more instructions stored in at least one of the main and secondary memories  208  and  210  to perform one or more operations. The I/O port  212  may include various input and output devices that are configured to operate under the control of the processor  202  by way of the communication infrastructure  206 . Examples of the input devices may include a keyboard, a mouse, a joystick, a touchscreen, a microphone, and the like. Examples of the output devices may include a display screen, a speaker, headphones, and the like. Examples of the I/O port  212  include a universal serial bus (USB) port, an Ethernet port, a real or virtual keypad, a mouse, a stylus, and the like. The various operations of the application server  104  have been described in detail in conjunction with  FIGS. 3A and 3B . 
     Referring now to  FIGS. 3A and 3B , a flow chart  300  that illustrates a method for optimizing the customer engagement in the transportation service, in accordance with an embodiment of the present invention is shown. At step  302 , the historical travel data of the customer is extracted. The processor  202  extracts the historical travel data of the customer from the database server  102  over the first communication network  106 . The historical travel data includes the travel data of the one or more rides availed by the customer in the past. The historical travel data may include at least one of the pick-up location, the drop-off location, the pick-up time, the drop-off time, the ride fare, or the travel distance of the one or more rides by the customer. The processor  202  stores the extracted historical travel data of the customer in at least one of the main or secondary memory  208  or  210 . 
     At step  304 , weekday and weekend travel data are determined from the extracted historical travel data. The processor  202  determines the weekday and weekend travel data from the extracted historical travel data based on a timestamp associated with each travel data (i.e., each of the one or more rides) in the extracted historical travel data. The timestamp may be at least one of the pick-up or drop-off time associated with each of the one or more rides. The weekday travel data is the travel data associated with one or more weekdays (for example, “Monday”, “Tuesday”, “Wednesday”, “Thursday” or “Friday”) of one or more weeks. The weekend travel data is the travel data associated with one or more weekend days (for example, “Saturday” or “Sunday”) of the one or more weeks. 
     At step  306 , the weekday and weekend travel data are grouped on to a first defined timeline in a week-wise manner. The processor  202  groups the weekday and weekend travel data on to the first defined timeline. For example, the weekday and weekend travel data are separately grouped in the week-wise manner on to a weekly grid. 
     At step  308 , dense time intervals are identified by way of the DBSCAN technique based on at least one of the grouped weekday and weekend travel data. For example, the processor  202  executes the one or more instructions, algorithms, codes, or routines corresponding to the DBSCAN technique stored in the main memory  208 , and identifies one or more dense time intervals from the grouped weekday travel data. Similarly, the processor  202  identifies the one or more dense time intervals from the grouped weekend travel data. The one or more dense time intervals are one or more time windows in which the one or more rides were taken by the customer. The one or more time windows may be associated with one or more days (e.g., weekdays or weekend days). Further, in an embodiment, the processor  202  identifies the one or more regular time intervals from each of the one or more time windows corresponding to the weekday (and/or weekend) travel data. The identification of the one or more regular time intervals has been described in steps  310 - 316 . 
     At step  310 , at least one of an age (A), a count of bookings (B), a conversion rate (C), a count of demands (D) in each time window of the one or more time windows is determined. The processor  202  determines at least one of the age (A), the count of bookings (B), the conversion rate (C), or the count of demands (D) in each of the one or more time windows. The age (A) of a time window (i.e., an identified dense time interval) is a current time length from a current time instance to a historical time instance of the time window. The count of bookings (B) in the time window is a number of rides taken by the customer during the time window. The conversion rate (C) of the time window is a ratio of the count of bookings and the count of demands in the time window. The count of demands (D) in the time window is a number of requests for the one or more rides received from the customer-computing device  108  in the time window. In an embodiment, the processor  202  determines at least one of the age (A), the count of bookings (B), the conversion rate (C), or the count of demands (D) based on the travel data associated with the time window. The processor  202  further determines the one or more days in the time window. Further, the processor  202  determines a maximum number of demands (Dmax) corresponding to a day from the one or more days of the time window. The processor  202  further determines the count of days (D) for each of the remaining days of the one or more days in the time window. 
     At step  312 , a check is performed for a first time window from the one or more time windows to determine whether the maximum number of demands (Dmax) in the first time window is greater than a first threshold value and the count of demands (D) for each of the remaining days is greater than a second threshold value. In an embodiment, the first threshold value is determined based on the age (A) of the first time window and a first factor (a). For example, the first threshold value may be determined based on a product of the first factor (a) and the age (A) of the first time window. In an exemplary embodiment, a value of the first factor (a) is 0.2. In an embodiment, the second threshold value is determined based on the age (A) of the first time window and a second factor (β). For example, the second threshold value may be determined based on a product of the second factor (β) and the age (A) of the first time window. In an exemplary embodiment, a value of the second factor (β) is 0.15. In an embodiment, the processor  202  utilizes the conversion rate (C) to remove the one or more customers having low bookings. For example, if the conversion rate (C) of the customer is less than a cut-off conversion value, then the processor  202  may not consider the customer for the customer engagement. If at step  312 , for the first time window, it is determined that the maximum number of demands (Dmax) is greater than the first threshold value and the count of demands (D) for each of the remaining days is greater than the second threshold value, then step  316  is executed. However, if at step  312 , it is determined that the maximum number of demands (Dmax) is less than the first threshold value and the count of demands (D) for each of the remaining days is greater (or less) than the second threshold value, or vice-versa, then the first time window is not identified as a regular time interval, and step  314  is executed. 
     At step  314 , a next time window i.e., a second time window is selected from the one or more time windows, and the steps  310  and  312  are executed for the second time window. At step  316 , the first time window, for which the maximum number of demands (Dmax) is greater than the first threshold value and the count of demands (D) for each of the remaining days is greater than the second threshold value, is determined as the regular time interval. Similarly, the processor  202  checks the remaining time windows, if any, of the one or more time windows to determine remaining regular time intervals. Thus, before proceeding to step  318 , the processor  202  determines the one or more regular time intervals. In an embodiment, based on the execution of the steps  310 ,  312 , and  314 , as described above with respect to the first time window, the processor  202  determines the one or more regular time intervals from the one or more time windows (i.e., the one or more dense time intervals). 
     At step  318 , the regular travel data is detected from the historical travel data for the one or more regular time intervals. For each of the one or more regular time intervals, the processor  202  detects the regular travel data from the historical travel data based on each of the one or more regular time intervals, and stores the regular travel data in at least one of the main or secondary memory  208  or  210 . 
     At step  320 , the regular capacity for each of the one or more regular time intervals is determined. The processor  202  determines the regular capacity for each of the one or more regular time intervals based on the corresponding regular travel data, and stores in at least one of the main or secondary memory  208  or  210 . In an exemplary embodiment, the regular capacity for each regular time interval is determined based on estimated values of at least one of the pick-up location, the drop-off location, the ride fare, or the travel distance associated with the one or more rides in the regular travel data of the corresponding regular time interval. In another exemplary embodiment, the regular capacity for each regular time interval is determined based on at least one of an average distance of the one or more rides, the count of bookings (B), or an average ride fare of the one or more rides in the regular travel data of the corresponding regular time interval. 
     At step  322 , the random travel data is detected based on the historical travel data and the regular travel data of the one or more regular time intervals. The processor  202  detects the random travel data by filtering out the regular travel data of the one or more regular time intervals from the historical travel data. 
     At step  324 , an estimated mean (EM) and a standard deviation (SD) of the random travel data are determined. The random travel data are grouped based on a week number (W(n)) to obtain a number of rides in each week. The processor  202  groups the random travel data based on the week number (W(n)) in which the one or more rides were taken by the customer. The estimated mean (EM) is an estimate mean of the random travel data using Gaussian kernel weights. 
     At step  326 , the random capacity with and without recency is determined. In an embodiment, the processor  202  determines the random capacity without recency based on the determined estimated mean (EM) and the determined standard deviation (SD) of the random travel data. The processor  202  further determines the random capacity with recency based on recent travel data associated with a second defined timeline. For example, the random capacity with recency is determined based on a previous week random capacity and the age (A) of a previous week. The random capacity for the previous week is multiplied by a coefficient, based on the age of the previous week. The value of the coefficient reduces with increase in the value of the week number (W(n)), thereby, weights of the previous random capacities is reduced for the grouped random travel data with increasing week number. In an exemplary embodiment, the determination of the random capacity is given by the equation (1): 
       RC( n )= a *(EM+SD)+ b *RC( n− 1)+ c*w ( n− 1)  (1)
 
     In the equation (1), a, b, and c are coefficients that are obtained from regression on specifically chosen data set, w(n−1) is a number of random travels in a week (n−1). Further, “a*(EM+SD)” in the equation (1) indicates the estimation based on total random history and “b*RC(n−1)+c*w(n−1)” in the equation (1) indicates the random capacity considering the recency. Further, “RC(n)” is the random capacity in the nth week with and without recency. 
     At step  328 , a total capacity of the customer is determined. The processor  202  determines the total capacity based on the random and regular capacities of the customer. The total capacity of the customer is determined by executing an algebraic summation of the random and regular capacities of the customer. The regular capacity of the customer is determined by executing one or more algebraic operations (e.g., a summation, a mean, an average, a median, or a standard deviation) on one or more regular capacities corresponding to the one or more regular time intervals. 
     At step  330 , the engagement level of the customer is determined. The processor  202  determines the engagement level of the customer based on the total capacity of the customer and a realized demand. The engagement level is defined as a ratio of the realized demand and the total capacity of the customer. For example, the engagement level of the customer in the week (W(n)) is the ratio of demands in the week (W(n)) and the total capacity of the customer in the previous week (W(n−1)). The engagement level may be utilized to determine whether the customer is deviated from a mean engagement. For example, based on the engagement level of the customer in the one or more weeks, the processor  202  obtains an engagement pattern of the customer, and determines an engagement state of the customer (e.g., engaged, disengaged, new, or lost) based on the deviation of the engagement pattern from the mean engagement. 
     At step  332 , the LTV of the customer is determined. The processor  202  determines the LTV of the customer based on at least the engagement level of the customer. In an exemplary embodiment, the LTV of the customer may be determined based on at least an expected value of the engagement level (E1), an expected value of the conversion rate (E2), and the regular capacity (RegCap) and random capacity (RanCap). The determination of the LTV of the customer is given by the equation (2): 
       LTV=(ΣATS of Regular interval+RanCap* E [ATS])*( E [1])*( E [2])  (2)
 
     At step  334 , the targeted content is recommended to the customer. Based on the determined LTC of the customer, the processor  202  recommends the targeted content to the customer. The targeted content may also be recommended based on the total capacity or the engagement level of the customer. The processor  202  transmits the targeted content to the customer-computing device  108  of the customer over the second communication network  112 . The targeted content includes one or more offers or discounts that matches with the interests of the customer. The one or more offers or discounts may be related to one or more ride fares for one or more future rides. The one or more offers or discounts may be further related to entertainment-related content, such as movies, music, and the like. Such recommendation of the targeted content encourages the customer to use the transportation service (e.g., cab service), which in turn optimizes the customer engagement with the transportation service. Furthermore, the processor  202  ranks the customers based on at least one of the regular capacity, random capacity, LTV, or engagement level of each of the customers. The ranking of the customers can be further utilized to identify high, medium, or low risk customers. The recommendation of the targeted content can be decided by the processor  202  based on the risk associated with the customers. 
     Specific advantages of the method and the system include determining the LTV of the customers in the transportation service, such as the cab service. The method further includes determining regular and random travel patterns and engagement patterns of the customers over one or more previous weeks, months, or years. Based on at least one of the regular and random travel patterns, the engagement patterns, and the LTV of the customers, the current state (e.g., engaged, disengaged, lost, or new) and the risk (e.g., high, medium, or low) of each of the customers is identified. Further, based on at least one of the current state and the risk associated with each customer, the processor  202  recommends the targeted content to the customers. The recommendation of the targeted content to the customers improves the engagement of the customers with the transportation service and thus, helps in retaining the customers. Thus, the method and the system provide an efficient way of optimizing the customer engagement. 
     A person having ordinary skill in the art will appreciate that embodiments of the disclosed subject matter can be practiced with various computer system configurations, including multi-core multiprocessor systems, minicomputers, mainframe computers, computers linked or clustered with distributed functions, as well as pervasive or miniature computers that may be embedded into virtually any device. For instance, at least one processor, such as the processor  202 , and a memory, such as the main memory  208  and the secondary memory  210 , implement the above described embodiments. Further, the operations may be described as a sequential process, however some of the operations may in fact be performed in parallel, concurrently, and/or in a distributed environment, and with program code stored locally or remotely for access by single or multiprocessor machines. In addition, in some embodiments, the order of operations may be rearranged without departing from the spirit of the disclosed subject matter. 
     Techniques consistent with the present invention provide, among other features, systems and methods for optimizing customer engagement in a transportation service. Unless stated otherwise, terms such as “first” and “second” are used to arbitrarily distinguish between the elements such terms describe. Thus, these terms are not necessarily intended to indicate temporal or other prioritization of such elements. While various exemplary embodiments of the disclosed system and method have been described above it should be understood that they have been presented for purposes of example only, not limitations. It is not exhaustive and does not limit the invention to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practicing of the invention, without departing from the breadth or scope.