The present invention relates methodology for generating predictive model for predicting consumer purchase behaviour.

Background:

A century ago, most people were living in residential areas with constrained potential outcomes to leave their locale, and few different ways to fulfil their requirements. Presently, because of the quickened development of innovation and the extreme difference in way of life, customers start to have progressively various necessities. At the same time the instruments used to study their behaviour have evolved, and today databases are included in consumer behaviour research. All through time numerous models were created, first to dissect, and later to foresee the buyer conduct. Therefore, the idea of predictive model is created, and by applying it currently, organizations are attempting to comprehend and foresee the conduct of their customers.

Retail and E-commerce are one of the first industries that recognized the benefits of using predictive analytics and started to employ it. In fact, understanding of the customer is a first-priority goal for any retailer. In today’s competitive business environment understanding of your customer requirement and offering the right products at right time is the key to any successful business. Due to high growth of internet, online shopping is becoming most interesting and popular activities for the consumers.

Predicting the ever-evolving consumer behaviour is one of the biggest challenges faced by marketers around the world. Well, it has always been a challenging task, but today, it is even harder as consumers are constantly being exposed to new technologies, products, and even new wants.

Another challenge in the existing solutions is that predictive analytics uses historical transaction data and univariant forecasting methodologies. Further, existing solutions mainly rely on internal data sources such as sales and operations and hence uses limited source of data.

Considering the challengesthere is a need for solution that captures internet search data on a real-time basis, process such data, and build several predictive modelling algorithms along with other data sources to predict future sales, explain sales surge and decline, and the like. There is a need for holistic approach, to capture all relevant information from the internet about consumer’s behaviour and perception without limiting only to consumer online search behaviour, and to understand how online competitive marketing campaigns, product positioning, promotions, price inflation execute on digital platforms would affect such the consumer’s behaviour.

Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context.

The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.

The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art.

Objective of the invention

The primary object of thepresent invention ismethodology for generating predictive model for predicting consumer purchase behaviour.

Summary of the invention:

Accordingly following invention ismethodology for generating predictive model for predicting consumer purchase behaviour.

Thesystem captures or receives the input raw data from various sources and stores them into its database. The system further processes the input data to generate online data for further processing. The online data indicates the structured form of the input raw data. The system further processes the online data along with pre-stored research data to generate a learning mode, and thereafter, a predictive model. The predictive model generated helps the users to determine various kinds of predictive information, for example forecasting of events, reasoning of now casting, demand prediction, trends picking and the like.

Brief description of drawings

Further clarify various aspects of some example embodiments of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It is appreciated that these drawings depict only illustrated embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail through the use of the accompanying drawings.

In order that the advantages of the present invention will be easily understood, a detail description of the invention is discussed below in conjunction with the appended drawings, which, however, should not be considered to limit the scope of the invention to the accompanying drawings, in which:

Figure 1 shows a block diagram representation of system ofmethodology for generating predictive model for predicting consumer purchase behaviouraccording to the present invention.

Detailed description of invention:

The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.

In any embodiment described herein, the open-ended terms "comprising," "comprises,” and the like (which are synonymous with "including," "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like.

The present invention relatesmethodology for generating predictive model for predicting consumer purchase behaviour.

The environment comprises the system, communication network, user devices, and input data. The system is connected to the user devices via the communication network. The communication network may include, a direct interconnection, an e-commerce network, a Peer to Peer (P2P) network, Local Area Network (LAN), Wide Area Network (WAN), wireless network (e.g., using Wireless Application Protocol), Internet, Wi-Fi, and the like. The system may include server, computer, and portable devices. In one implementation, the system may include cloud-based infrastructure to enable real time processing. Further, the system may be accessed by the user devices associated with the user. The user device may include various types of portable communication devices capable of communicating with the system. In one implementation, the system may be implemented in the user devices.

Thesystem captures or receives the input raw data from various sources and stores them into its database. The system further processes the input data to generate online data for further processing. The online data indicates the structured form of the input raw data. The system further processes the online data along with pre-stored research data to generate a learning mode, and thereafter, a predictive model. The predictive model generated helps the users to determine various kinds of predictive information, for example forecasting of events, reasoning of now casting, demand prediction, trends picking and the like.

Thesystem includes a processor, I/O interface, and a memory. The I/O interface is configured to receive the input data from various sources. The received data may be stored in the memory of the system.

The memory is communicatively coupled with the processor of the system. The memory may also store processor instructions which may cause the processor to execute the instructions for generating the predictive model. The memory includes modules and data.

The modules include a generating module, ingesting module, processing module, and capturing module. The data includes online data, pre-stored research data, learning model, and the predictive model.

The capturing module of the system captures input raw data from a plurality of online sources. The input raw data may comprise social media data, click stream associated with online users, search data, forum data, blogs data, and email discussions and the like. The input raw data is raw data collected from various online sources which help the system to understand online user’s action and behaviour while searching any product/service or searching for any topic.

In next step, the generating module processes the captured input raw data and generates the online data associated with the topic related searches performed by online users. This helps the system understand about the user behaviour and preferences while searching for any topic/product/service on internet.

In next step, the ingesting module ingests the online data with the prestored research data. According to an embodiment, the prestored research dataindicates history data about the topic. For example, the prestored research data may include syndicated competitor's data suite which have been built and maintained from many years, which helps the system understand about the past history about that topic/product/service and the like.

In nest step, the processing module processes the online data with the pre-stored research data to determine search pattern of the online users and user-behaviour information of the online users. Further, the system performs this processing step for predefined time period, for example one months, one quarter, or one year. Once the sufficient processing is done, in next system, the generating module generates a learning model based on the captured search pattern of the online users and the user-behaviour information of the online users for the predefined period. The learning model generated further matures over the time understand in more depth about the user behaviour and search pattern associated with the online users regarding any topic/product/service or any other subject for which the user may search online.

Once the learning model is generated and matured, in next step, the generating module further generates the predictive model based on the learning model. Now, the predictive model generated is used to determine at least one of forecasting of events in real-time, reasoning of now casting, demand prediction, and trends picking.

Fig.1 illustrates methodology for generating predictive model for predicting consumer purchase behaviour.

The method may be described in the general context of computer executable instructions. Generally, the computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform functions or implement abstract data types.

First of all thesystem generates, by a processor, online data associated with topic related searches performed by online users.

In the next step thesystem ingests, by the processor, the online data with prestored research data. The prestored research data indicates history data about the topic.

Further,the system processes, by the processor, the online data with the prestored research data to determine search pattern of the online users and user-behaviour information of the online users.

Finally, thesystem generates, by the processor, the predictive model by analyzing the search pattern of the online users and user-behaviour information of the online users.

Additional advantages and modification will readily occur to those skilled in art. Therefore, the invention in its broader aspect is not limited to specific details and representative embodiments shown and described herein. Accordingly various modifications may be made without departing from the spirit or scope of the general invention concept as defined by the appended claims and their equivalents.

We Claims:

1. The present invention relates methodology for generating predictive
model for predicting consumer purchase behaviour.
2. Methodology for generating predictive model for predicting consumer
purchase behaviour claimed in claim 1,this helps the system understand
about the user behaviour and preferences while searching for any
topic/product/service on internet.