METHOD AND SYSTEM FOR ASSISTING A CUSTOMER IN REAL-TIME

A method and sale assistance system for assisting a customer in real-time when the customer is interested in a product is disclosed. The sale assistance system receives behavioural information associated with a customer present in a store based on a real-time data feed of the customer captured by monitoring devices located in the store. The data feed is captured while the customer is viewing and checking one or more products in the store and the behavioural information is predicated by a trained neural network by using the data feed. Based on the behavioural information, interest of the customer is identified for a product from the one or more product. Thereafter, an alert notification about the interest of the customer is provided to a store representative for the one or more products based on the identified interest of the customer while the customer is in the store.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority from Indian Patent Application No. 202141010515, filed Mar. 12, 2021, the entire contents of which are incorporated herein by reference.

TECHNICAL FIELD

The present subject matter is related in general to sale systems and analysis systems and more particularly, but not exclusively, to methods and systems for assisting a customer in real-time.

BACKGROUND

In today's modern and omnichannel environments, consumers are flooded with information about goods and services. Retailers that can connect with their customers by providing targeted information and offering value stand apart and have potential to create deep customer engagement. Although with emergence and advancements in technology, retailers target appropriate consumers and enable consumers to make better informed decisions about which products or services to consume. Yet not all consumer decisions rely on extensive information searches and detailed decision processes. Some decisions are spontaneous which may be quickly decided while shopping online or in stores.

Typically, any purchase provides the retailer a multitude of disparate information, including transactional data, consumer data, and the like. Retailers that can draw effective insights from such large data can make better predictions about consumer behavior, design more appealing offers, better target their customers, and develop tools that encourage consumers to make purchase decisions that favour their products.

Currently there exists many methodologies used by retailers for assisting customers while purchasing one or more products. However, experience of the customers is still affected due to non-availability of information in real-time. For instance, sometimes customers shopping in stores show enough interest in a product kept on aisles, but in the end do not buy it. In such cases, customers tend to hold the product in hand, turn it around, try to read the product details and spend a considerable amount of time analysing it. There may be multiple reasons for not buying it, such as not being happy with pricing, promotion, requiring some clarifications regarding branding, quality concerns, warranty issues, after sales service, and the like. Many a times, customers do not consult store associates to clarify their queries, but just decide not to buy the product. This may be a big problem for products which are newly launched, and customers are not much aware of brand and product itself. From the store perspective, they lose an opportunity to sell a product. If in case customer was a probable buyer and his/her concerns or questions were addressed, then such an opportunity could have got converted to sale. From a manufacturer's perspective, it is a lost opportunity to collect customer feedback, especially for newly launched products. Thus, assisting the customers in real-time for any liable product becomes very crucial from a sale perspective.

SUMMARY

In an embodiment, the present disclosure may relate to a method for assisting a customer in real-time when the customer is interested in a product. The method comprises receiving behavioural information associated with a customer present in a store based on a real-time data feed of the customer captured by monitoring devices located in the store. The data feed is captured while the customer is viewing and checking one or more products in the store, and the behavioural information is predicated by a trained neural network by using the data feed. The method includes identifying interest of the customer for a product from the one or more product based on the behavioural information and providing alert notification about the interest of the customer to a store representative for the one or more products based on the identified interest of the customer while the customer is in the store.

In an embodiment, the present disclosure may relate to a sale assistance system for assisting a customer in real-time when the customer is interested in a product. The sale assistance system may comprise a processor and a memory communicatively coupled to the processor, where the memory stores processor executable instructions, which, on execution, may cause the sale assistance system to receive behavioural information associated with a customer present in a store based on a real-time data feed of the customer captured by monitoring devices located in the store. The data feed is captured while the customer is viewing and checking one or more products in the store, and the behavioural information is predicated by a trained neural network by using the data feed. The sale assistance system identifies an interest of the customer for a product from the one or more product based on the behavioural information and provides an alert notification about the interest of the customer to a store representative for the one or more products based on the identified interest of the customer while the customer is in the store.

In an embodiment, the present disclosure relates to a non-transitory computer readable medium including instructions stored thereon that when processed by at least one processor may cause a sale assistance system to receive behavioural information associated with a customer present in a store based on a real-time data feed of the customer captured by monitoring devices located in the store. The data feed is captured while the customer is viewing and checking one or more products in the store, and the behavioural information is predicated by a trained neural network by using the data feed. The instruction causes the processor to identify interest of the customer for a product from the one or more product based on the behavioural information and provides an alert notification about the interest of the customer to a store representative for the one or more products based on the identified interest of the customer while the customer is in the store

DETAILED DESCRIPTION

Embodiments of the present disclosure may relate to a method and sale assistance system for assisting a customer in real-time when the customer is interested in a product. Currently there exists many methodologies used by retailers for assisting customers while purchasing one or more products. However, experience of the customers is still affected due to non-availability of information in real-time. For instance, sometimes customers shopping in stores show enough interest in a product, but in the end may end up not buying the product. In such cases, customers tend to hold the product in hand, turn it around, try to read the product details, and spend a considerable amount of time analysing it. There may be multiple reasons for not buying it, such as not being happy with pricing, promotion, requiring some clarifications regarding branding, quality concerns, warranty issues, after sales service, and the like. Many a times, customers do not consult store associates to clarify their queries, but just decide not to buy the product. This may be a big problem for products which are newly launched, and customers are not much aware of brand and product itself.

The present disclosure resolves this problem by identifying interest of the customer for a product based on behavioural information. The behavioural information is associated with the customer present in a store based on a real-time data feed of the customer captured by monitoring devices located in the store. The data feed is captured while the customer is viewing and checking one or more products in the store. Thereafter, an alert notification about the interest of the customer is provided to a store representative for one or more products based on the identified interest of the customer while the customer is in the store. Therefore, the present disclosure assists the customer in real-time while the customer is in the store. Thus, the present disclosure provides an opportunity to understand concerns of the customers for any product in real-time.

FIG. 1Aillustrate an exemplary environment for assisting a customer in real-time when the customer is interested in a product in accordance with some embodiments of the present disclosure.

As shown inFIG. 1A, an environment100includes a sale assistance system101connected to a store103through a communication network105. Particularly, the sale assistance system101may be connected to a server of the store103(not shown inFIG. 1Aexplicitly). In an embodiment, the sale assistance system101may be present within the server of the store103, as shown inFIG. 1B. In an embodiment, the communication network105may include, but is not limited to, a direct interconnection, a Peer-to-Peer (P2P) network, Local Area Network (LAN), Wide Area Network (WAN), wireless network (for example, using Wireless Application Protocol), Internet, Wi-Fi, and the like.

In an embodiment, the store103may be defined as a business place for retail sale of products and services. The store103includes monitoring devices107located at one or more locations near one or more products and at one or more positions of the store103. The monitoring devices107may include video surveillance cameras. Further, the store103may include a neural network model109for predicting behavioural information associated with a customer.

The sale assistance system101may be used for assisting a customer in real-time. Particularly, when the customer is interested in a product. The sale assistance system101may include, but is not limited to, a laptop, a desktop computer, a notebook, a smartphone, a tablet, a server, and any other computing devices. A person skilled in the art would understand that any other devices, not mentioned explicitly, may also be used as the sale assistance system101in the present disclosure.

Further, the sale assistance system101may include an input/output (I/O) interface111, a memory113and a processor115. The I/O interface111may be configured to receive behavioural information associated with a customer present in the store103based on a real-time data feed of the customer captured by the monitoring devices107. The behavioural information from the I/O interface111may be stored in the memory113. The memory113may be communicatively coupled to the processor115of the sale assistance system101. The memory113may also store processor instructions which may cause the processor115to execute the instructions for assisting a customer in real-time when the customer is interested in a product.

At any instant, when a customer is present in the store103and viewing and checking one or more products, the monitoring devices107may continuously capture a data feed associated with the customer. Simultaneously, the data feed from the monitoring devices107may be provided to the neural network model109for predicting the behavioural information associated with the customer. The neural network model109may be trained previously using training data associated with customer in the store103. In an embodiment, the neural network model109may be present in the sale assistance system101. In an embodiment, any existing behavioural analysis neural network models may be used for predicting the behavioural information of the customer. The neural network model109may be, for instance, a Convolution Neural Network (CNN) model.

The behavioural information may indicate behavioural of the customer while viewing one or more products in the store103. For instance, a customer in the store103holding a product for more than ten minutes and continuously reading details about the product and keeping the product back in stack in the store103.

Further, based on the behavioural information, the sale assistance system101identifies an interest of the customer for the product. For instance, considering the above example, the sale assistance system101may indicate that the customer is interested in buying the product but may have some confusion and queries on the product. The sale assistance system101may store the interest of the customer for the one or more products. Consequently, the sale assistance system101may provide an alert notification about the interest of the customer to a store representative for the one or more products based on the identified interest of the customer while the customer is in the store103. In an embodiment, the store representative is one of: a support representative associated with one or more sections of the store103for assisting the customer, or a representative at a checkout counter. The alert notification may be provided upon identifying the customer at a specific location of the store103using the monitoring devices107. For instance, the specific location may be one or more sections in the store103used for displaying the one or more products and the checkout counter of the store103. In one example, the customer is identified at the checkout counter. In this example, the sale assistance system101may include checking an order list of the customer using the monitoring devices107located near the checkout counter.

Further, the sale assistance system101may check a presence of one of the products in the order list for which the interest of the customer is identified. Finally, an alert notification is transmitted to a representative at a checkout counter with relevant information about the customer and the interest associated with the product when the product is not in the order list. Thus, the store representative with the relevant information on the product communicates with the customer about the product. The communication may include for instance, providing one or more discounts in case of pricing concern of the customer and providing response for queries on the product and seeking feedback of the customer on the product.

FIG. 2shows a detailed block diagram of a sale assistance system in accordance with some embodiments of the present disclosure.

The sale assistance system101may include data200and one or more modules209which are described herein in detail. In an embodiment, data200may be stored within the memory113. The data200may include, for example, behavioural data201, interest level data203, feedback data205, and other data207.

The behavioural data201may include behavioural details associated with the customer present in the store103. The behavioural data201may be received from the store103. The behavioural information is predicated by the neural network model109by using the data feed captured by the monitoring devices107located in the store103.

The interest level data203may include information about the interest of the customer while the customer is viewing and checking one or more products in the store103. For instance, the information may include whether the customer is interested in the product or not.

The feedback data205may include feedback of the customer on the product received while communicating with the customer about the product.

The other data207may store data, including temporary data and temporary files, generated by modules209for performing the various functions of the sale assistance system101.

In an embodiment, the data200in the memory113are processed by the one or more modules209present within the memory113of the sale assistance system101. In an embodiment, the one or more modules209may be implemented as dedicated units. As used herein, the term module refers to an application specific integrated circuit (ASIC), an electronic circuit, a field-programmable gate arrays (FPGA), Programmable System-on-Chip (PSoC), a combinational logic circuit, and/or other suitable components that provide the described functionality. In some implementations, the one or more modules209may be communicatively coupled to the processor115for performing one or more functions of the sale assistance system101. The said modules209when configured with the functionality defined in the present disclosure will result in a novel hardware.

In one implementation, the one or more modules209may include, but are not limited to, a receiving module213, an interest identification module215, and an alert providing module217. The one or more modules209may also include other modules219to perform various miscellaneous functionalities of the sale assistance system101. In an embodiment, the other modules219may include a training module for training the neural network model109using the training data associated with customers in the store103.

The receiving module213may receive behavioural information associated with the customer present in the store103based on the real-time data feed of the customer captured by the monitoring devices107located in the store103. The data feed is captured while the customer is viewing and checking one or more products in the store103. The behavioural information is received through the server of the store103. Further, the receiving module213may receive the feedback of the customer on the one or more products.

The interest identification module215may identify the interest of the customer for the product from the one or more product based on the behavioural information. For instance, if the customer seems interested, confused, or not interested to buy the product. For instance, consider, if the behavioural information indicates that a customer is viewing the product for five to ten minutes and gestures of the customer identify the customer as confused, the interest identification module215may identify that the customer is interested in buying the product. However, the customer may have a few queries or concerns on the product.

The alert providing module217may provide the alert notification about the interest of the customer to the store representative for the one or more products based on the identified interest of the customer while the customer is in the store103. In an embodiment, the store representative is one of: the support representative associated with the one or more sections of the store103for assisting the customer, or the representative at a checkout counter. The alert providing module217may provide the alert notification on identifying the customer at the specific location of the store103. For instance, the specific location may be the one or more sections in the store103used for displaying the one or more products and the checkout counter of the store103.

FIG. 3A-3Bshow exemplary scenarios of a retail store for assisting a customer in real-time when the customer is interested in a product in accordance with some embodiments of the present disclosure.

FIG. 3Ashows an exemplary scenario of a retail store300. In current context, two sections of the retail store300is illustrated. The retail store300includes monitoring devices such as, surveillance camera3011and a surveillance camera3012in a first section302. Further, the retail store300includes a surveillance camera3013and surveillance camera3014in a second section304. The first section302includes a customer303viewing and checking a product305.

Thus, in this scenario, the surveillance camera3011and the surveillance camera3012may capture real-time data feed while the customer303is viewing the product305. The data feed is provided to the neural network model109for predicting the behavioural information of the customer303. The behavioural information may be used further to identify the interest of the customer303for the product. For instance, in the current context, assume that the behavioural information for the customer303indicates that the customer303is confused about the product305. The interest of the customer303for the product305is stored for further analysis. Further, the customer303is monitored in the retail store300at a specific location. For instance, the customer303is monitored and identified at the second section304of the retail store300, which is a checkout counter. In such condition, the surveillance camera3013and the surveillance camera3014may check a presence of the product305in an order list of the customer303. Consider, the product305is not identified in the order list of the customer303. In such case, the sale assistance system101may provide an alert notification by transmitting to a checkout representative306present at the checkout counter with relevant information about the customer303and the interest associated with the product305. Thus, the checkout representative306with the relevant information on the product305may communicate with the customer303about the product. The communication may include, for instance, providing one or more discounts in case of pricing concern of the customer303and providing response for queries on the product305and seeking feedback of the customer303on the product305.

In another scenario, as shown inFIG. 3B, consider another part of the retail store300, where a customer307is being monitored using a surveillance camera3015. Similar to above scenario inFIG. 3A, the behavioural information associated with the customer307is received by the sale assistance system101. The behavioural information of the customer307is used for identifying the interest of the customer307. For instance, considering the customer307is very much interested in a product, but may have queries on the product. In such condition, the sale assistance system101may provide the alert notification to a support representative309located nearby the customer307for assisting the customer307. The alert notification may be received on a mobile device311of the support representative.

FIG. 4illustrates a flowchart showing a method for assisting a customer in real-time when the customer is interested in a product in accordance with some embodiments of present disclosure.

As illustrated inFIG. 4, the method400includes one or more blocks for assisting a customer in real-time when the customer is interested. The method400may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types.

At block401, the behavioural information associated with the customer present in the store103is received by the receiving module213based on a real-time data feed of the customer captured by the monitoring devices107located in the store103. The data feed is captured while the customer is viewing and checking one or more products in the store103. The behavioural information is predicated by the trained neural network model109by using the data feed.

At block403, the interest of the customer is identified by the interest identification module215for the product from the one or more product based on the behavioural information.

At block405, the alert notification is provided by the alert providing module217about the interest of the customer to the store representative for the one or more products based on the identified interest of the customer while the customer is in the store103. The alert notification may be provided upon identifying the customer at the specific location of the store103using the monitoring devices107. For instance, the specific location may be the one or more sections in the store103used for displaying the one or more products and the checkout counter of the store103. In one example, the customer is identified at the checkout counter. In this example, the sale assistance system101may include checking an order list of the customer using the monitoring devices107located near the checkout counter.

Computing System

FIG. 5illustrates a block diagram of an exemplary computer system500for implementing embodiments consistent with the present disclosure. In an embodiment, the computer system500may be used to implement the sale assistance system101. The computer system500may include a central processing unit (“CPU” or “processor”)502. The processor502may include at least one data processor for assisting a customer in real-time when the customer is interested in a product. The processor502may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.

Using the I/O interface501, the computer system500may communicate with one or more I/O devices such as input devices512and output devices513. For example, the input devices512may be an antenna, a keyboard, a mouse, a joystick, a (infrared) remote control, a camera, a card reader, a fax machine, a dongle, a biometric reader, a microphone, a touch screen, a touchpad, a trackball, a stylus, a scanner, a storage device, a transceiver, a video device/source, etc. The output devices613may be a printer, a fax machine, a video display (e.g., Cathode Ray Tube (CRT), Liquid Crystal Display (LCD), Light-Emitting Diode (LED), plasma, Plasma Display Panel (PDP), Organic Light-Emitting Diode display (OLED) or the like), audio speaker, etc.

In some embodiments, the computer system500consists of the sale assistance system101. The processor502may be disposed in communication with the communication network509via a network interface503. The network interface503may communicate with the communication network509. The network interface503may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), transmission control protocol/internet protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. The communication network509may include, without limitation, a direct interconnection, LAN, WAN, wireless network (e.g., using Wireless Application Protocol), the Internet, etc. Using the network interface503and the communication network509, the computer system500may communicate with store514. The network interface503may employ connection protocols include, but not limited to, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), TCP/IP, token ring, IEEE 802.11a/b/g/n/x, etc.

The communication network509includes, but is not limited to, a direct interconnection, an e-commerce network, a P2P network, LAN, WAN, wireless network (e.g., using Wireless Application Protocol), the Internet, Wi-Fi, and such. The first network and the second network may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), TCP/IP, Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the first network and the second network may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc.

The memory505may store a collection of program or database components, including, without limitation, user interface506, an operating system507etc. In some embodiments, computer system500may store user/application data, such as the data, variables, records, etc., as described in this disclosure. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase.

In some embodiments, the computer system500may implement a web browser508stored program component. The web browser508may be a hypertext viewing application, for example MICROSOFT® INTERNET EXPLORER™, GOOGLE® CHROME™, MOZILLA® FIREFOX™, APPLE® SAFARI™, etc. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS), Secure Sockets Layer (SSL), Transport Layer Security (TLS), etc. Web browsers508may utilize facilities such as AJAX™, DHTML™, ADOBE® FLASH™, JAVASCRIPT™, JAVA™, Application Programming Interfaces (APIs), etc. In some embodiments, the computer system500may implement a mail server stored program component. The mail server may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as ASP™, ACTIVEX™, ANSI™ C++/C #, MICROSOFT®, NET™, CGI SCRIPTS™, JAVA™, JAVASCRIPT™, PERL™, PHP™, PYTHON™, WEBOBJECTS™, etc. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), MICROSOFT® exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like. In some embodiments, the computer system500may implement a mail client stored program component. The mail client may be a mail viewing application, such as APPLE® MAIL™, MICROSOFT® ENTOURAGE™, MICROSOFT® OUTLOOK™, MOZILLA® THUNDERBIRD™, etc.

An embodiment of the present disclosure provides a sales opportunity for high value items is not lost.

An embodiment of the present disclosure provides an opportunity to understand customer concerns.

An embodiment of the present disclosure provides an opportunity for the store for an upsell.

An embodiment of the present disclosure enables collection of valuable information for the product manufactures regarding the product.

The disclosed method and system overcome technical problem of assisting the customers in real-time by identifying interest of the customer for a product based on behavioural information, which is associated with the customer present in a store based on a real-time data feed of the customer captured by monitoring devices located in the store. The data feed is captured while the customer is viewing and checking one or more products in the store. Thereafter, an alert notification about the interest of the customer is provided to a store representative for one or more products based on the identified interest of the customer while the customer is in the store. Therefore, the present disclosure assists the customer in real-time while the customer is in the store. Thus, providing an opportunity to understand concerns of the customers for any product in real-time.

Currently, there exists many methodologies used by retailers for assisting customers while purchasing one or more products. However, experience of the customers is still affected due to non-availability of information in real-time. For instance, sometimes customers shopping in stores show enough interest in a product, but in the end may end up not buying the product. In such cases, customers tend to hold the product in hand, turn it around, try to read the product details and spend a considerable amount of time analysing it. There may be multiple reasons for not buying it, such as not being happy with pricing, promotion, requiring some clarifications regarding branding, quality concerns, warranty issues, after sales service, and the like. Many a times, customers do not consult store associates to clarify their queries, but just decide not to buy the product. This may be a big problem for products which are newly launched, and customers are not much aware of brand and product itself.

In light of the above-mentioned advantages and the technical advancements provided by the disclosed method and system, the steps as discussed above are not routine, conventional, or well understood in the art, as the steps enable the following solutions to the existing problems in conventional technologies. Further, the steps clearly bring an improvement in the functioning of the system itself as the steps provide a technical solution to a technical problem.

The described operations may be implemented as a method, system, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof. The described operations may be implemented as code maintained in a “non-transitory computer readable medium,” where a processor may read and execute the code from the computer readable medium. The processor is at least one of a microprocessor or a processor capable of processing and executing the queries. A non-transitory computer readable medium may include media such as magnetic storage medium (e.g., hard disk drives, floppy disks, tape, etc.), optical storage (CD-ROMs, DVDs, optical disks, etc.), volatile and non-volatile memory devices (e.g., EEPROMs, ROMs, PROMs, RAMs, DRAMs, SRAMs, Flash Memory, firmware, programmable logic, etc.), etc. Further, non-transitory computer-readable media include all computer-readable media except for a transitory. The code implementing the described operations may further be implemented in hardware logic (e.g., an integrated circuit chip, Programmable Gate Array (PGA), ASIC, etc.).

Still further, the code implementing the described operations may be implemented in “transmission signals,” where transmission signals may propagate through space or through a transmission media, such as, an optical fiber, copper wire, etc. The transmission signals in which the code or logic is encoded may further include a wireless signal, satellite transmission, radio waves, infrared signals, Bluetooth, etc. The transmission signals in which the code or logic is encoded is capable of being transmitted by a transmitting station and received by a receiving station, where the code or logic encoded in the transmission signal may be decoded and stored in hardware or a non-transitory computer readable medium at the receiving and transmitting stations or devices. An “article of manufacture” includes non-transitory computer readable medium, hardware logic, and/or transmission signals in which code may be implemented. A device in which the code implementing the described embodiments of operations is encoded may include a computer readable medium or hardware logic. Of course, those skilled in the art will recognize that many modifications may be made to this configuration without departing from the scope of the present disclosure, and that the article of manufacture may include suitable information bearing medium known in the art.

The illustrated operations ofFIG. 5show certain events occurring in a certain order. In alternative embodiments, certain operations may be performed in a different order, modified, or removed. Moreover, steps may be added to the above-described logic and still conform to the described embodiments. Further, operations described herein may occur sequentially or certain operations may be processed in parallel. Additionally, operations may be performed by a single processing unit or by distributed processing units.

Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the subject matter of the present disclosure. It is therefore intended that the scope of the present disclosure be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the description of the embodiments of the present disclosure is intended to be illustrative, but not limiting, of the scope of the present disclosure, which is set forth in the following claims.