A system for managing funnel-based marketing

Abstract

The present invention relates to a funnel-based marketing management system comprising: a collection module that collects consumer information, which includes information on a consumer’s ad click frequency, time spent on a landing page, whether a product is selected, and whether a product is purchased; a classification module that classifies funnel stages according to a customer journey based on the consumer information; a cycle generation module that generates cycle information regarding stage-by-stage marketing performance and purchase conversion flow based on the classified funnel stages; a recommendation module that selects additional marketing tasks corresponding to funnel stages with poor stage-by-stage marketing performance based on the cycle information; an execution module that executes the selected additional marketing tasks and stores the execution history; and an analysis module that generates comprehensive performance information based on the cycle information and the execution history. The system includes an output module that visualizes the funnel stage, cycle information, additional marketing tasks, execution history, and comprehensive performance information on a dashboard. The cycle generation module aggregates multiple marketing tasks performed in the funnel stage into a single cycle unit and classifies the operational form of the funnel stage into one of a slow cycle, a fast cycle, or an automated cycle. The comprehensive performance information includes sales growth rate, Return on Ad Spend (ROAS), Lifetime Value (LTV), purchase rate relative to ad clicks, churn reduction rate, and purchase conversion rate. The analysis module calculates the deviation value of the marketing performance for each funnel stage by comparing the comprehensive performance information with pre-set standard performance information, and assigns an improvement priority for funnel stages where the deviation value is above a pre-set threshold. The output module sets key indicators by industry based on the comprehensive performance information, wherein the key indicators include ROAS and purchase conversion rate for the e-commerce industry, and LTV and churn reduction rate for the SaaS industry.The present invention is characterized by visualizing the set key indicators by highlighting them at the top of the dashboard, the execution module generating multiple drafts for the additional marketing task, performing an A / B / N test by randomly exposing the drafts to users, collecting consumer response data in real time and including it in the execution history, and the analysis module evaluating the performance of the drafts through a multi-slot bandit algorithm based on the execution history, and transmitting a control signal to the execution module to increase the traffic allocation ratio of the draft with the highest performance evaluated through the multi-slot bandit algorithm. According to the present invention, since marketing data generated throughout the customer journey can be integratedly analyzed and the entire process from strategy formulation to execution, performance analysis, and visualization can be automated within a single system, complex marketing flows can be managed concisely and systematically, and real-time performance monitoring and flexible strategy adjustment are possible. Furthermore, since marketing operations can be performed efficiently without separate personnel, small and medium-sized enterprises or startups with limited manpower and budgets can expect substantial improvements in marketing performance, and an efficient operational structure can be implemented to reduce waste of marketing resources and maximize Return on Advertising Spend (ROAS). In addition, by industry Based on key metrics automatically configured according to characteristics and performance data from each funnel stage, underperforming areas can be identified and improvement directions prioritized; this enables quantitative and consistent marketing decision-making without relying on repetitive and subjective judgments. Through this, continuous improvement of key performance indicators, such as purchase conversion rates and customer re-engagement rates, and long-term growth can be achieved simultaneously.

Description

Technology Field

[0001] The present invention relates to a funnel-based marketing management system. Background Technology

[0002] Online marketing has established itself as a core growth tool in the digital age, serving as an essential strategy for companies to expand consumer touchpoints and drive purchases. Various metrics exist to quantify consumer responses, such as ad click-through rates, time spent on landing pages, product browsing behavior, and purchase conversion rates; data-driven marketing strategies based on these indicators are becoming a crucial means of boosting corporate revenue and brand growth. Recently, the marketing paradigm has been shifting beyond simply driving traffic to a direction that demands precise analysis and strategic responses across the entire customer journey.

[0003] However, conventional online marketing operations face structural limitations that make it difficult to proactively respond to the increasingly complex customer journey and the diversity of vast amounts of data. Since functions such as marketing strategy formulation, content creation, advertising execution, CRM operations, and performance analysis are conducted in isolation, they are often not supported by a system capable of organically integrating and managing them. Consequently, it is difficult to immediately detect missed conversions or concentrated dropouts at specific stages within the flow from customer acquisition to purchase, and to make appropriate improvements.

[0004] Moreover, systematically managing a fragmented marketing structure requires a diverse range of specialized personnel, such as performance marketers, CRM experts, data analysts, and web optimization specialists. However, assembling such a workforce demands significant cost and time, which realistically poses a heavy burden, particularly for small and medium-sized enterprises or early-stage startups.

[0005] Even when utilizing external personnel, operational inefficiencies follow, such as a tendency for strategy and execution to become disconnected, unclear accountability for performance, and increased communication costs. Consequently, there are limitations in consistently performing precise marketing analysis and iterative optimization.

[0006] Therefore, there is a need for an integrated marketing management system that can sophisticatedly structure funnel stages around the customer journey, systematically analyze performance at each stage, and provide comprehensive diagnoses based on execution history, while automatically selecting and prioritizing key indicators tailored to the industry. The problem to be solved

[0007] The objective of the present invention is to provide a funnel-based marketing management system that solves the aforementioned conventional problems by integratively analyzing marketing data generated throughout the customer journey and automatically processing the entire process of strategy formulation, execution, performance analysis, and visualization within a single system, thereby breaking away from conventional fragmented and inefficient marketing operations, efficiently allocating resources through precise decision-making based on real-time performance, and maximizing purchase conversion performance. means of solving the problem

[0008] The above objective is achieved, according to the present invention, by a collection module that collects consumer information, which is information regarding a consumer's ad click frequency, time spent on a landing page, whether a product is selected, and whether a product is purchased; a classification module that classifies funnel stages according to a customer journey based on the consumer information; a cycle generation module that generates cycle information regarding stage-by-stage marketing performance and purchase conversion flow based on the classified funnel stages; a recommendation module that selects additional marketing tasks corresponding to funnel stages with poor stage-by-stage marketing performance based on the cycle information; an execution module that executes the selected additional marketing tasks and stores the execution history; and an analysis module that generates comprehensive performance information based on the cycle information and the execution history.and includes an output module that visualizes the funnel stage, cycle information, additional marketing tasks, execution history, and comprehensive performance information on a dashboard; the cycle generation module aggregates multiple marketing tasks performed in the funnel stage into a single cycle unit and classifies the operational form of the funnel stage into one of a slow cycle, a fast cycle, or an automated cycle; the comprehensive performance information includes sales growth rate, Return on Ad Spend (ROAS), Lifetime Value (LTV), purchase rate relative to ad clicks, churn reduction rate, and purchase conversion rate; the analysis module calculates the deviation value of marketing performance for the funnel stage by comparing the comprehensive performance information with pre-set standard performance information and assigns improvement priorities for funnel stages where the deviation value is above a pre-set threshold; the output module sets key indicators by industry based on the comprehensive performance information, wherein the key indicators include ROAS and purchase conversion rate for the e-commerce industry and LTV and churn reduction rate for the SaaS industry; the set key indicators are highlighted and visualized at the top of the dashboard; and the execution This is achieved by a funnel-based marketing management system characterized by the module generating multiple drafts for the additional marketing task, performing an A / B / N test that randomly exposes the drafts to users, collecting consumer response data in real time and including it in the execution history, and the analysis module evaluating the performance of the drafts through a multi-slot bandit algorithm based on the execution history, and transmitting a control signal to the execution module to increase the traffic allocation ratio of the draft with the highest performance evaluated through the multi-slot bandit algorithm.

[0009] In addition, the aforementioned funnel stages include an inflow stage where consumers are attracted through advertising, an interest stage where consumers search for information and consume content on a landing page, a desire stage where consumers form a purchase intention for the product, an action stage where consumers select the product and proceed with the purchase, a revenue stage where the consumer's product purchase is completed and revenue is generated, and a re-engagement stage where consumers revisit the landing page or recommend the product to other consumers after the purchase.

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[0012] delete Effects of the invention

[0013] According to the present invention, since marketing data generated throughout the customer journey can be analyzed integrally and the entire process from strategy formulation to execution, performance analysis, and visualization can be automated within a single system, complex marketing flows can be managed concisely and systematically, and real-time performance monitoring and flexible strategy adjustment are possible.

[0014] Furthermore, since marketing operations can be performed efficiently without the need for separate personnel, small and medium-sized enterprises or startups with limited manpower and budgets can expect tangible improvements in marketing performance. It also enables the implementation of an efficient operational structure that reduces wasted marketing resources and maximizes Return on Advertising Spend (ROAS).

[0015] Furthermore, based on key performance indicators automatically configured according to industry characteristics and performance data from each funnel stage, underperforming areas can be identified and improvement directions prioritized. This enables quantitative and consistent marketing decision-making without relying on repetitive and subjective judgments. Through this, continuous improvement in key performance indicators, such as purchase conversion rates and customer re-engagement rates, and long-term growth can be achieved simultaneously.

[0016] Meanwhile, the effects of the present invention are not limited to those mentioned above, and various effects may be included within the scope obvious to a person skilled in the art from the contents described below. Brief explanation of the drawing

[0017] FIG. 1 illustrates the overall connection between the components of a funnel-based marketing management system according to an embodiment of the present invention, and Figures 2 and 3 illustrate an example of visualization as a dashboard through an output module of a funnel-based marketing management system according to an embodiment of the present invention. Specific details for implementing the invention

[0018] Hereinafter, some embodiments of the present invention will be described in detail with reference to the exemplary drawings. It should be noted that in assigning reference numerals to the components of each drawing, the same components are given the same reference numeral whenever possible, even if they are shown in different drawings.

[0019] In addition, when describing embodiments of the present invention, if it is determined that a detailed description of related known configurations or functions would hinder understanding of the embodiments of the present invention, such detailed description is omitted.

[0020] In addition, terms such as first, second, A, B, (a), (b), etc., may be used when describing the components of the embodiments of the present invention. These terms are used merely to distinguish the components from other components, and the essence, order, or sequence of the components is not limited by the terms used.

[0021] Additionally, in the specification, the singular form includes the plural form unless specifically stated otherwise in the text. The terms “comprising” and / or “comprising” as used in the specification do not exclude the presence or addition of one or more other components in addition to the mentioned components.

[0022] Additionally, in describing embodiments of the present invention, each "part," "module," or "step" may be implemented through a processor and memory. The term processor should be broadly interpreted to include general-purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), controllers, microcontrollers, state machines, and the like. In some environments, the term processor may refer to an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), and the like. The term processor may also refer to a combination of processing devices, such as, for example, a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors combined with a DSP core, or any other combination of such configurations.

[0023] Furthermore, memory should be broadly interpreted to include any electronic component capable of storing electronic information. Memory may also refer to various types of processor-readable media such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), eraseable-programmable read-only memory (EPROM), electrically eraseable PROM (EEPROM), flash memory, magnetic or optical information storage devices, registers, etc. If a processor can read information from memory or write information to memory, memory is said to be in an electronic communication state with the processor, and each "part," "module," or "stage" may be implemented through a program or application based on the processor and memory in an electronic communication state.

[0025] From now on, a funnel-based marketing management system (100) according to an embodiment of the present invention will be described in detail with reference to the attached drawings.

[0026] FIG. 1 illustrates the overall connection between the components of a funnel-based marketing management system according to an embodiment of the present invention, and FIG. 2 and 3 illustrate an example of visualization as a dashboard through an output module of a funnel-based marketing management system according to an embodiment of the present invention.

[0027] As illustrated in FIG. 1, a funnel-based marketing management system (100) according to one embodiment of the present invention includes a collection module (110), a classification module (120), a cycle generation module (130), a recommendation module (140), an execution module (150), an analysis module (160), and an output module (170).

[0028] First, the collection module (110) collects consumer information, such as the frequency of consumer ad clicks, time spent on the landing page, whether a product is selected, and whether a product is purchased, and is electrically connected to the classification module (120).

[0029] Specifically, the collection module (110) can perform the function of collecting structured consumer information necessary for marketing analysis from multiple channels in real time, based on data regarding various behaviors performed by consumers in an online environment. The collected consumer information is subsequently used as basic data for classification, analysis, strategy formulation, and visualization, and can quantitatively evaluate customer conversion flow and marketing performance.

[0030] The collection module (110) is linked with websites, mobile applications, advertising platforms, e-commerce backends, CRM systems, etc., to automatically collect data generated from various sources, refine it into a defined data format, and transmit it into the system.

[0031] The consumer information collected by the collection module (110) may include ad click frequency, time spent on the landing page, whether a product was selected and whether a product was purchased, and in addition, various information such as content scroll depth, product detail page access history, whether a product was added to the cart, whether a purchase process was initiated, payment completion history, whether a return and refund were processed, sign-up and login history, whether a referral code was entered, or whether a review was written after purchase may be included.

[0032] This consumer information can be tracked in real time based on events such as clicks, touches, inputs, navigations, and conversions that occur according to consumer behavior, and each event can be recorded along with timestamps, consumer identifiers, screen identifiers, and device information and stored in a structured form.

[0033] Here, ad click frequency refers to the number of times a consumer clicks on a digital advertisement posted online, and this can be used as an indicator to quantitatively determine the level of consumer inflow. The collection module (110) can collect ad click frequency in real time through a tracking script or UTM tag linked to an advertising platform and can be used to analyze the initial inflow behavior of consumers.

[0034] Landing page dwell time refers to the length of time a consumer stays on a specific webpage after reaching it following an ad click. Landing page dwell time can be calculated by determining the time difference between the webpage loading point and the point of exit or page transition, and it can be used as a key metric to evaluate a consumer's level of content interest or exploration. For example, the quality of content or the appropriateness of page layout can be evaluated by distinguishing between consumers who stayed on a specific page for more than 100 seconds and those who left within 3 seconds.

[0035] Product selection status is data used to determine whether a consumer has shown initial purchase intent behavior, such as clicking on a specific product detail page or adding it to a shopping cart, and can be used as a key indicator to determine entry into the behavior stage among the funnel stages described later. The collection module (110) tracks events occurring within an e-commerce page or app, such as a consumer's entry into product details, adding to wishlist, or adding to a shopping cart, and each event can be stored along with consumer identifiers such as a product identifier (SKU), UUID, and hashed email, the time of the event occurrence, and information about the previous visited page.

[0036] Product purchase status refers to the history of a consumer actually completing a payment and creating an order. This can be used as a key indicator for the revenue stage among the funnel stages described later, and the collection module (110) can collect information regarding product purchase status through a server API response called upon payment completion.

[0037] Information regarding product purchase status may include the purchased product ID, payment amount, payment method, time of purchase, customer ID, whether the customer is new or existing, and order status (valid, cancelled, refund, etc.). In this case, collection may be performed through integration with an e-commerce platform or an in-house order processing system.

[0038] Consumer information collected through the collection module (110) can be collected in parallel through multiple channels, such as an advertising platform, website, mobile application, email marketing system, or CRM system.

[0039] In addition, the collection module (110) may use methods such as RESTful API communication, an event-based messaging system, and a log file collection method to collect data from multiple channels in an integrated manner, and may standardize the data collection path through a connector or an integration adapter for each channel.

[0040] Meanwhile, the collection module (110) can perform a preprocessing process to organize the collected consumer information and process it into a form suitable for analysis. This preprocessing process may include event sorting, removal of duplicate data, filtering of abnormal values, time zone unification, and session merging, and can filter out noise data such as repeated clicks by the same consumer, abnormally short dwell times, or traffic caused by ad bots, and can also perform normalization and resampling operations to increase the consistency and accuracy of the data.

[0041] Afterwards, consumer information collected through the collection module (110) is transmitted to the classification module (120) and classified into each funnel stage according to the flow of the customer journey, and can be used as basic data for cycle analysis and marketing performance evaluation.

[0042] Accordingly, the collection module (110) precisely collects actual consumer behavior data from multiple channels in alignment with key items of the funnel flow and preprocesses it, thereby providing basic information that can quantitatively analyze the flow of the entire marketing funnel, which can play a key role in increasing the accuracy of subsequent analysis and strategic decision-making.

[0043] Next, the classification module (120) classifies the funnel stages according to the customer journey based on consumer information received from the collection module (110), and is electrically connected to the collection module (110), the cycle generation module (130), and the output module (170).

[0044] Specifically, the classification module (120) analyzes consumer information collected through the collection module (110) to determine which marketing stage the consumer is currently in and classifies it into a funnel stage according to the Customer Journey. Subsequently, the classified funnel stage can be used as a reference value for analyzing marketing performance flow and evaluating conversion structure in the cycle generation module (130) to be described later.

[0045] The Customer Journey is a concept that structures the flow of a consumer's relationship with a brand or product over time. It can serve as a key standard for understanding consumers' psychological changes and behavioral patterns, and for establishing optimal marketing strategies at each stage. The stages of the customer journey are classified according to consumer behavior types, levels of interest, and the degree of purchase intent formation, and can be divided into the awareness, consideration, decision, retention, and recommendation stages.

[0046] First, the Awareness stage is the initial touchpoint where consumers first become aware of the existence of a brand or product. This stage marks the point in time when the first interaction between the brand and the consumer occurs, taking place through various channels such as advertisements, content, social media, news articles, or offline flyers. For example, this stage applies when a consumer watches a product advertisement video on YouTube, encounters sponsored content on Instagram, or sees a product name for the first time during a Naver search. Although consumers do not have a purchase intention during this stage, they begin to recognize the existence and value of the brand; therefore, brand exposure frequency, ad views, and first-visit page logs can be utilized as key metrics.

[0047] The consideration stage is the phase where consumers move beyond mere awareness of a brand or product to develop a certain level of interest. It is accompanied by active behaviors, such as searching for specific information about products and services and comparing them with other brands. For instance, this stage may involve consumers visiting a brand's official website to view product details, comparing features with other products in the same category on price comparison sites, or evaluating quality by watching blog or YouTube reviews. As such, the consideration stage is a point where consumers' search intentions become clearly evident, and key indicators such as page dwell time, clicks on product detail pages, usage of comparison features, or review viewing history can be utilized.

[0048] The decision stage is the phase immediately preceding or at the point of execution where a consumer makes a clear decision to purchase a product and proceeds to the actual purchase. During this stage, consumers may perform actions such as adding products to their shopping cart, considering the application of coupons or reward points, selecting a payment method, or proceeding to the payment page. For example, this could be the stage where a consumer logs in, adds products to their cart, enters a shipping address, or reviews payment terms. Since the decision stage is directly linked to marketing performance, key metrics such as purchase conversion rate, cart abandonment rate, and checkout completion rate can be utilized.

[0049] The retention stage is the phase where consumers maintain a relationship with a brand by continuously interacting with it even after purchasing a product. This stage can include various activities such as revisiting, repurchasing, viewing personalized content, using loyalty points, and utilizing the customer service center. For example, this could be the stage where a consumer reads an email newsletter received from the seller after receiving the product, checks discount information via push notifications, or proceeds with a repurchase. Since the retention stage is directly linked to the increase in Customer Lifetime Value (LTV), visit frequency, repurchase rate, and long-term customer retention rate can be utilized as key indicators.

[0050] The advocacy stage is a phase where consumers, highly satisfied with a purchased brand or product, voluntarily share or recommend it to others; it is a stage based on loyalty and trust. Consumers in this stage can leverage their purchasing experience to leave positive reviews on blogs or social media, write photo reviews, or send referral codes to friends to encourage purchases. For example, this stage might involve consumers posting product reviews on Naver Cafe or sharing posts on Instagram with the brand's hashtag. Since actions during the advocacy stage are highly likely to lead to an influx of new customers, they can act as a catalyst for brand expansion.

[0051] The classification module (120) can classify the information corresponding to the funnel stage by analyzing which stage of the customer journey the consumer is currently in based on the collected consumer information. Through this, it is possible to identify in real time which marketing flow the consumer belongs to and convert it into structured data that can be used for measuring marketing performance at each stage and analyzing purchase conversion flow.

[0052] Here, a funnel stage refers to a unit of analysis structured from a marketing perspective by classifying the flow of behavior that occurs during the series of processes leading to a purchase, within the entire journey of a consumer interacting with a brand or product.

[0053] The term "funnel" originally refers to a funnel; in marketing, it is a concept that visually represents a structure where a large number of potential consumers are drawn in during the initial stages, but the conversion rate to purchase gradually decreases. Initially, a large number of consumers become aware of a brand through advertisements or content, but as they go through stages such as information search, comparison, and purchase decision, the number of consumers who actually make a purchase gradually diminishes. Funnel stages organize this flow into steps resembling the shape of a funnel narrowing from the top to the bottom, and each stage can reflect changes in consumer behavior and psychological state.

[0054] These funnel stages may specifically include the inflow stage, interest stage, needs stage, action stage, profit stage, and re-engagement stage.

[0055] The inflow stage is the stage where consumers are attracted through advertising; the interest stage is the stage where consumers search for information and consume content on the landing page; the desire stage is the stage where consumers form a purchase intention for the product; the action stage is the stage where consumers select a product and proceed with the purchase; the revenue stage is the stage where the consumer's product purchase is completed and revenue is generated; and the re-engagement stage refers to the stage where consumers revisit the landing page or recommend the product to other consumers after purchasing it.

[0056] More specifically, the inflow stage is the phase where a consumer establishes their first contact with a brand or product. Actions such as clicking on ads via external channels, clicking on organic search results, or visiting via external links can fall under this stage. This stage directly corresponds to the awareness stage of the customer journey and can be classified by capturing data immediately after a consumer first becomes aware of the brand. For example, if a consumer clicks on an Instagram ad and reaches a brand's landing page for the first time, that action can be classified as the inflow stage.

[0057] The interest stage is a stage where consumers start to take an interest in a brand or product and search for related information or consume content, corresponding to the consideration stage of the customer journey, and activities such as increased time spent on the page, scrolling through content, viewing product detail pages, and browsing blog reviews can be used as key indicators. For example, if a consumer views a specific product detail page for more than 30 seconds and clicks on two or more related review posts, the classification module (120) can classify this as the interest stage.

[0058] The needs stage is the phase where a consumer's purchase intent begins to form concretely; actions such as adding items to a shopping cart, comparing prices, checking discount information, and reviewing purchase conditions may fall under this stage. The needs stage corresponds to the beginning of the decision phase of the customer journey and can be interpreted as the point where the consumer's purchase intent becomes clearly evident. For example, if a consumer views the same product two or more times, adds it to a shopping cart, and checks for coupon availability, this can be classified as the needs stage.

[0059] The action stage is the stage where actual purchasing behavior takes place, and actions such as entering a payment page, selecting a payment method, and completing payment may fall under the action stage. The action stage corresponds to the latter part of the decision stage of the customer journey and is the point where actual results of a marketing campaign occur. For example, at the point where a consumer pays for a product and completes an order, the classification module (120) can classify this time as the action stage.

[0060] The revenue stage is a stage based on the point in time when revenue is actually generated after payment is completed, and in terms of commerce, it may correspond to cases where conditions such as order confirmation, delivery completion, or no refunds are met. This stage partially corresponds to the maintenance stage of the customer journey and can serve as an important criterion for measuring business performance. For example, if 7 days have passed since the purchase was completed and delivery was completed without a return request, the classification module (120) may classify it as the revenue stage.

[0061] Finally, the re-engagement stage is a stage in which consumers engage in activities such as interacting again or introducing the product to others after purchasing it, and corresponds to the maintenance and recommendation stages of the customer journey. Activities in this stage may include revisiting the website, consuming subsequent content, writing reviews, and entering a referral code. For example, if a consumer who purchased the product views the brand's new promotion page two weeks later and shares a review on social media, the classification module (120) may classify this as the re-engagement stage.

[0062] The classification module (120) can classify the funnel stages by quantitatively analyzing consumer information received from the collection module (110). For example, if an ad click event occurs but the time spent on the landing page is 3 seconds or less and there is no product search, it can be classified as the inflow stage; if a product is added to the shopping cart but payment is not made, it can be classified as the desire stage. Additionally, if a payment completion API call is successfully responded to, it can be classified as the revenue stage, and if a revisit log or review registration log is detected after the revenue stage, it can be classified as the re-engagement stage.

[0063] Additionally, the classification module (120) can classify the consumer's funnel stage by prioritizing the action corresponding to the stage with the highest conversion priority for each action when multiple actions included in the consumer information correspond to different funnel stages.

[0064] Here, conversion priority refers to assigning a higher weight to actions within the marketing funnel structure that are more directly linked to a consumer's purchase conversion.

[0065] For example, if a consumer enters the payment page after adding products to the shopping cart within a single session, the classification module (120) can classify the consumer into a behavioral stage by prioritizing the entry into the payment page, as the entry into the payment page is a conversion metric that is more directly linked to the execution of a purchase.

[0066] Accordingly, the classification module (120) can generate clearer and more consistent funnel stage classification results by prioritizing the conversion priority of the action corresponding to the top in the conversion flow in the funnel structure, regardless of the timing of occurrence of each action.

[0067] Next, the cycle generation module (130) generates cycle information regarding step-by-step marketing performance and purchase conversion flow based on the funnel stage received from the classification module (120), and is electrically connected to the classification module (120), recommendation module (140), and output module (170).

[0068] Specifically, by comprehensively analyzing the execution status of marketing tasks performed at each funnel stage and the consumer conversion flow, cycle information can be generated, which is information regarding stage-by-stage marketing performance and purchase conversion flow at the cycle level.

[0069] Cycle information refers to structured analytical unit information that describes the actual flow and performance of marketing tasks executed at each funnel stage. It can be a quantitative indicator of marketing performance and purchase conversion flow at each stage, including conversion rate, response rate, drop-off rate, average time to move, missed conversion rate, and whether performance thresholds have been reached.

[0070] The cycle generation module (130) can convert workflows and consumer movement patterns into quantified indicators so that, through cycle information, it can identify which funnel stage is experiencing a bottleneck and which stage is transitioning quickly within a specific marketing cycle.

[0071] Therefore, through cycle information, users can intuitively identify which stages within the overall funnel flow are stagnating, which stages are transitioning rapidly, and how significantly marketing activities at each stage are generating actual results.

[0072] In this context, stage-by-stage marketing performance refers to an indicator representing the actual results produced by marketing activities performed at each stage of the funnel, and may include conversion rates, response rates, the degree of reduction in churn rates, average response times, and whether performance thresholds have been reached.

[0073] In addition, the purchase conversion flow is an indicator of flow representing how smoothly consumers move along the overall funnel structure from the entry stage to the re-engagement stage, and may include the inter-stage conversion rate, drop-off rate, average travel time, and missed conversion rate.

[0074] For example, if a 'performance marketing campaign' conducted during the inflow stage is being performed at a slow speed, has poor results, and is being operated only through manual registration, the cycle generation module (130) can generate cycle information by assigning attributes such as 'Speed: Slow', 'Status: Ready', and 'Work Type: Manual' to the corresponding stage. On the other hand, if an 'email onboarding sequence' task executed during the interest stage is automated and executed at a fast speed, and has good results through SaaS integration, the cycle generation module (130) can generate quantified cycle information in the form of 'Speed: Automated', 'Analysis Optimized', and 'Linked System: SaaS'.

[0075] The cycle generation module (130) aggregates multiple marketing tasks performed at each funnel stage into a single cycle unit using cycle information, and can classify the cycle according to categorized classification criteria such as 'slow cycle', 'normal cycle', 'fast cycle', and 'automated cycle'. These cycle types can be defined based on statistical values ​​such as average task processing speed, automation rate, frequency of manual intervention, task success rate, and consumer response indicators, and performance analysis specialized for each cycle type is possible.

[0076] For example, if the review writing rate is significantly low despite the 'post-purchase review request notification' task performed in the revenue stage being automatically sent, the cycle generation module (130) may classify the task as a 'fast cycle' and record the 'poor performance' attribute together. Conversely, if the 'cart reminder notification' performed in the action stage is manually set and the execution speed is slow but leads to a high conversion rate, it may be evaluated as a 'slow cycle' but 'good performance'.

[0077] The cycle generation module (130) can store the generated cycle information by connecting it to each funnel stage, and can be used as base information for automatic marketing strategy recommendation in the recommendation module (140) described later and visualization output in the output module (170).

[0078] The cycle generation module (130) can perform a key role in diagnosing the execution flow of the entire marketing strategy and providing quantitative judgment criteria for establishing a performance-based strategy by integratively structuring the step-by-step marketing performance and purchase conversion flow.

[0079] Next, the recommendation module (140) is electrically connected to the cycle generation module (130), execution module (150), analysis module (160), and output module (170) to select additional marketing tasks corresponding to funnel stages with poor marketing performance at each stage, based on cycle information received from the cycle generation module (130).

[0080] Specifically, the recommendation module (140) analyzes the funnel stage cycle information generated by the cycle generation module (130) to identify specific funnel stages where marketing performance is poor or conversion flow is delayed, and can select additional marketing tasks to improve conversion in those stages.

[0081] Here, additional marketing tasks refer to strategic marketing execution items automatically selected to complement or improve low conversion flows, in addition to existing marketing tasks. Additional marketing tasks can be selected based on the causes of problems and execution characteristics at each funnel stage, or by analyzing the execution conditions, linked systems, and target characteristics of existing marketing tasks.

[0082] For example, if the funnel entry rate is low and classified as a 'slow cycle' in the inflow stage, the recommendation module (140) may select additional marketing tasks such as changing target keywords, improving ad copy, and A / B testing of landing pages; if the content consumption metric is low in the interest stage, tasks such as changing the information configuration of the product detail page, emphasizing user reviews, and improving website UX may be selected as response tasks. Additionally, if the shopping cart abandonment rate is high in the desire stage, conversion-inducing tasks such as issuing discount coupons, displaying urgent banners such as "stock running low," and providing delivery benefits may be selected; if the repurchase rate is low in the profit stage after the action stage, tasks such as sending an email sequence after purchase, providing point accumulation information, and setting up follow-up promotion notifications may be automatically recommended; and if recommendation conversion is low in the re-engagement stage, customer expansion-inducing tasks such as rewarding for writing reviews, encouraging social sharing, and providing friend referral bonuses may be selected as additional marketing tasks.

[0083] The recommendation module (140) can prioritize selected additional marketing tasks and organize the task type, expected response rate, execution target, and application conditions into a structured form and transmit them to the execution module (150). At this time, the priority for additional marketing tasks can be calculated based on cycle information generated by the cycle generation module (130).

[0084] Accordingly, the recommendation module (140) can perform the core function of diagnosing bottleneck points within the marketing funnel flow based on cycle information and automatically selecting additional marketing tasks most suitable for the corresponding stage, thereby deriving the potential for improvement of the overall marketing strategy in real time.

[0085] Next, the execution module (150) executes additional marketing tasks selected through the recommendation module (140) and stores the execution history, and is electrically connected to the recommendation module (140), the analysis module (150), and the output module (170).

[0086] Specifically, the execution module (150) can execute a task according to the execution conditions, target consumer group, delivery channel, and execution method of the task after receiving additional marketing tasks selected from the recommendation module (140).

[0087] The execution module (150) can perform additional marketing tasks automatically according to pre-set automation rules or through a manual execution procedure approved by an operator.

[0088] For example, if it is determined that a supplementary strategy is needed to increase the search retention rate of target consumers because the dropout rate in the interest stage is analyzed to be high, the execution module (150) can execute the 'onboarding content improvement campaign' task received from the recommendation module (140). At this time, the execution module (150) determines the conditions, target range, distribution timing, and content type for executing the task according to internal guidelines or external settings, and then performs the actual task execution in an automated manner or in a manual manner involving operator intervention.

[0089] Additionally, the execution module (150) stores an execution history, which is information about additional marketing tasks executed.

[0090] Execution history refers to historical information that systematically records under what conditions and when additional marketing tasks were executed, to which target audiences they were applied through which channels, and what the results were. For example, if the selected additional marketing task is the execution of an email campaign, the execution history may include the sending start time, recipient group, message template used, open rate, click-through rate, and whether errors occurred.

[0091] In addition, the execution module (150) stores the history of exceptional situations, such as execution failure, failure to meet trigger conditions, or external API integration errors, as an execution history, thereby comprehensively tracking not only successful marketing operations but also strategies that were not realized, and can provide practical grounds for precise diagnosis of system performance and strategy improvement from a long-term perspective.

[0092] Next, the analysis module (160) generates comprehensive performance information based on the received cycle information and execution history, and is electrically connected to the cycle generation module (130), execution module (150), and output module (170).

[0093] Specifically, the analysis module (160) can generate comprehensive performance information that evaluates the efficiency and effectiveness of the overall marketing campaign by comprehensively analyzing the funnel stage cycle information received from the cycle generation module (130) and the execution history of additional marketing tasks stored in the execution module (150).

[0094] Comprehensive performance information refers to information that comprehensively summarizes key indicators reflecting the stage-by-stage marketing performance generated at each stage of the marketing funnel and the results of additional marketing activities executed accordingly; this can serve as a basis for comprehensively judging the success of a marketing strategy.

[0095] Comprehensive performance information may include multiple indicators encompassing the financial and efficiency aspects of marketing, such as sales growth rate, Return on Ad Spend (ROAS), Lifetime Value (LTV), purchase rate relative to ad clicks, churn reduction rate, and purchase conversion rate.

[0096] The analysis module (160) can quantitatively calculate the impact of marketing activities on final financial performance by correlating cycle information, such as conversion rates and dropout rates at each funnel stage, with the execution history of additional marketing activities.

[0097] For example, the correlation can be analyzed and quantified such that the success of a remarketing campaign corresponding to the execution history at a specific funnel stage leads to an increase in the purchase conversion rate corresponding to cycle information, and consequently, an improvement in ROAS corresponding to overall performance information.

[0098] Additionally, the analysis module (160) can calculate the deviation value of the step-by-step marketing performance by comparing the generated comprehensive performance information with the preset standard performance information, and can assign an improvement priority for the funnel stage where the deviation value is greater than or equal to the preset threshold.

[0099] Here, standard performance information can be pre-configured based on industry-specific average performance data, past success story data, or target indicators set by the user.

[0100] Through this, the analysis module (160) can go beyond simple performance measurement and actively diagnose how much current marketing activities have reached compared to the target and which parts need urgent improvement.

[0101] For example, if the shopping cart abandonment rate of a needs stage is 20% or higher than the standard performance information, the analysis module (160) designates the needs stage as "first priority for improvement" and feeds this information to the recommendation module (140) to induce the selection of additional marketing tasks, such as shopping cart reminder notifications, as a priority. Through this process, a cyclical structure of self-diagnosis and automatic improvement is completed, thereby continuously optimizing marketing efficiency.

[0102] The comprehensive performance information and improvement priorities generated in the analysis module (160) are subsequently transmitted to the output module (170) and can be used as key data visualized on the marketing management dashboard. Through this, the user can grasp the overall performance and problems of marketing at a glance and obtain the quantitative evidence necessary to establish an optimized strategy.

[0103] Next, the output module (170) visualizes the received funnel stage, cycle information, additional marketing tasks, execution history, and overall performance information on a dashboard, and is electrically connected to the classification module (120), cycle generation module (130), recommendation module (140), execution module (150), and analysis module (160).

[0104] Specifically, the output module (170) can perform the role of integrating and providing all key data generated and collected within the funnel-based marketing management system (100) according to one embodiment of the present invention in the form of a dashboard, which is a user-friendly graphic interface.

[0105] The dashboard is designed to provide a comprehensive overview of the entire process, from the execution of marketing strategies to performance analysis, enabling users to intuitively understand complex data and metrics and make rapid decisions.

[0106] The output module (170) can arrange and visualize information required for visualization hierarchically according to a funnel structure.

[0107] For example, as illustrated in Fig. 2, the funnel stage can be visualized in the form of a funnel to visualize the number of consumers and conversion rate at each stage, and cycle information can be expressed as a bar graph or icon to represent the speed, performance, status, etc. of marketing work executed at each stage.

[0108] For example, steps classified as 'slow cycles' are indicated by red bars, and 'automated cycles' by green bars, allowing users to intuitively identify problem points.

[0109] Additionally, the output module (170) can display additional marketing tasks and execution history as specific widgets within the dashboard, allowing for visualization to track the process of which complementary strategies were proposed and how they were actually executed. Through these widgets, users can view the list of proposed tasks and the execution results of each task.

[0110] In addition, as illustrated in FIG. 3, the output module (1700) can provide visualized marketing performance and status through an indicator dashboard.

[0111] The metrics dashboard displays a summary of key metrics such as revenue, ROAS (Return on Advertising Spend), customer LTV, and conversion rate at the top, and allows visualization of each metric's growth rate or change compared to the previous period.

[0112] The metrics dashboard can provide detailed result indicators in the form of monthly reports, such as the number of purchasing customers, average order value, CAC (Customer Acquisition Cost), or the proportion of the marketing budget. Additionally, specific figures for each stage of the funnel are displayed in a time-series format, allowing the flow of marketing performance to be grasped at a glance.

[0113] Meanwhile, the output module (170) can automatically set key indicators of high importance by industry based on comprehensive performance information and prioritize visualizing these indicators on the dashboard.

[0114] For example, in the e-commerce industry, Return on Advertising Spend (ROAS) and purchase conversion rate can be set as key metrics, while in the SaaS industry, Lifetime Value (LTV) and churn reduction rate can be set as key metrics and highlighted at the top of the dashboard for visualization.

[0115] The automatic setting function of this output module (170) can reduce the hassle of the user manually setting indicators and provide an optimal performance management environment suitable for the industry characteristics.

[0116] Accordingly, the output module (170) can go beyond simply displaying data and support real-time data updates, filtering, and drill-down functions to help the user explore detailed information on a specific period, a specific channel, or a specific funnel stage. These functions can serve as essential elements for the user to perform in-depth analysis based on data and obtain specific information necessary to improve marketing strategies.

[0117] Meanwhile, a funnel-based marketing management system (100) according to one embodiment of the present invention can perform a machine learning-based real-time optimization process using a Multi-Armed Bandit algorithm.

[0118] A multi-slot bandit algorithm refers to a reinforcement learning-based algorithm that explores the performance of each option through a small amount of data in a situation where it is not known which of several options yields the best result, and dynamically controls the system to concentrate more resources on the option with the best performance.

[0119] The execution module (150) can automatically perform A / B / N testing on various variables of marketing content, such as ad text, images, or button colors on a landing page, by utilizing a multi-slot bandit algorithm. Here, A / B / N testing refers to an experimental method in which various drafts of a website or marketing content are randomly exposed to users, and the performance of each draft is compared to find the optimal version.

[0120] The execution module (150) may randomly expose multiple drafts at the start of a marketing operation and collect consumer response data in real time to include additionally in the execution history. The execution history collected in this way is transmitted to the analysis module (160), and the analysis module (160) may evaluate the performance of each draft through a multi-slot bandit algorithm based on this data and send a control signal to the execution module (150) to allocate more traffic to the draft showing the highest performance.

[0121] For example, when the recommendation module (140) selects a shopping cart reminder notification as an additional marketing task, the execution module (150) can send the notification simultaneously by configuring different drafts such as "Draft A: The item in your shopping cart will soon be sold out.", "Draft B: There is a discount coupon applicable to the item in your shopping cart.", and "Draft C: There is a special item in your shopping cart that you will regret missing."

[0122] Subsequently, the execution module (150) exposes each design at an equal rate and collects consumer reactions such as notification click rates. These consumer reactions are transmitted to the analysis module (160) so that performance can be evaluated in real time. At this time, if the analysis module (160) determines that the click rate of a specific design is significantly higher than that of other designs, it may send a control signal to the execution module (150) to "increase the exposure ratio of Design B to 80%" according to the multi-slot bandit algorithm. Accordingly, the execution module (150) exposes Design B to more consumers, and reduces the exposure ratio of Designs A and C, which have low performance.

[0123] This real-time optimization process can be continuously repeated without separate user intervention, and control can be exercised to automatically select the most optimized marketing content or to frequently display dynamically optimized content. Through these intelligent optimization features, marketing efficiency and performance can be further enhanced by optimizing the details of executed tasks in real time.

[0124] As described above, according to the funnel-based marketing management system (100) of one embodiment of the present invention, marketing data generated throughout the customer journey can be analyzed in an integrated manner, and the entire process from strategy formulation to execution, performance analysis, and visualization can be automated within a single system. Therefore, complex marketing flows can be managed concisely and systematically, and real-time performance monitoring and flexible strategy adjustment are possible.

[0125] Furthermore, since marketing operations can be performed efficiently without the need for separate personnel, small and medium-sized enterprises or startups with limited manpower and budgets can expect tangible improvements in marketing performance. It also enables the implementation of an efficient operational structure that reduces wasted marketing resources and maximizes Return on Advertising Spend (ROAS).

[0126] Furthermore, based on key performance indicators automatically configured according to industry characteristics and performance data from each funnel stage, underperforming areas can be identified and improvement directions prioritized. This enables quantitative and consistent marketing decision-making without relying on repetitive and subjective judgments. Through this, continuous improvement in key performance indicators, such as purchase conversion rates and customer re-engagement rates, and long-term growth can be achieved simultaneously.

[0128] Although all components constituting the embodiments of the present invention have been described above as being combined or operating in combination, the present invention is not necessarily limited to such embodiments. That is, within the scope of the purpose of the present invention, all components may be selectively combined in one or more ways to operate.

[0129] Furthermore, terms such as "include," "compose," or "have" as described above, unless specifically stated otherwise, mean that the relevant component may be inherent; therefore, they should be interpreted as allowing for the inclusion of additional components rather than excluding them. All terms, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the present invention pertains, unless otherwise defined. Commonly used terms, such as those defined in advance, should be interpreted in accordance with their meaning in the context of the relevant technology and should not be interpreted in an ideal or overly formal sense unless explicitly defined in the present invention.

[0130] Furthermore, the above description is merely an illustrative explanation of the technical concept of the present invention, and those skilled in the art to which the present invention pertains will be able to make various modifications and variations within the scope of the essential characteristics of the present invention.

[0131] Accordingly, the embodiments disclosed in this invention are intended to illustrate, not limit, the technical concept of the invention, and the scope of the technical concept of the invention is not limited by these embodiments. The scope of protection of this invention shall be interpreted by the claims below, and all technical concepts within an equivalent scope shall be interpreted as being included within the scope of rights of this invention. Explanation of the symbols

[0132] 100: Funnel-based marketing management system according to an embodiment of the present invention 110: Collection Module 120: Classification Module 130: Cycle Generation Module 140: Recommendation Module 150: Execution module 160: Analysis Module 170: Output module

Claims

Claim 1 A collection module that collects consumer information, which includes information on the frequency of consumer ad clicks, time spent on landing pages, whether a product is selected, and whether a product is purchased; a classification module that classifies funnel stages according to the customer journey based on the consumer information; a cycle generation module that generates cycle information regarding stage-by-stage marketing performance and purchase conversion flow based on the classified funnel stages; a recommendation module that selects additional marketing tasks corresponding to funnel stages with poor stage-by-stage marketing performance based on the cycle information; an execution module that executes the selected additional marketing tasks and saves the execution history; and an analysis module that generates comprehensive performance information based on the cycle information and the execution history.The system includes an output module that visualizes the funnel stage, cycle information, additional marketing tasks, execution history, and comprehensive performance information on a dashboard. The cycle generation module aggregates multiple marketing tasks performed in the funnel stage into a single cycle unit and classifies the operational form of the funnel stage into one of a slow cycle, a fast cycle, or an automated cycle. The comprehensive performance information includes sales growth rate, Return on Ad Spend (ROAS), Lifetime Value (LTV), purchase rate relative to ad clicks, churn reduction rate, and purchase conversion rate. The analysis module calculates the deviation value of marketing performance for each funnel stage by comparing the comprehensive performance information with pre-set standard performance information, and assigns improvement priorities for funnel stages where the deviation value is above a pre-set threshold. The output module sets key indicators by industry based on the comprehensive performance information, wherein the key indicators include ROAS and purchase conversion rate for e-commerce industries, and LTV and churn reduction rate for SaaS industries. The system highlights and visualizes the set key indicators at the top of the dashboard. A funnel-based marketing management system characterized by the module generating multiple drafts for the additional marketing task, performing an A / B / N test that randomly exposes the drafts to users, collecting consumer response data in real time and including it in the execution history, and the analysis module evaluating the performance of the drafts through a multi-slot bandit algorithm based on the execution history, and transmitting a control signal to the execution module to increase the traffic allocation ratio of the draft with the highest performance evaluated through the multi-slot bandit algorithm. Claim 2 A funnel-based marketing management system according to claim 1, wherein the funnel stage comprises: an inflow stage in which a consumer is attracted through an advertisement; an interest stage in which the consumer performs information search and content consumption on a landing page; a desire stage in which the consumer's purchase intention for a product is formed; a behavior stage in which the consumer selects a product and proceeds with a purchase; a profit stage in which the consumer's product purchase is completed and profit is generated; and a re-engagement stage in which the consumer revisits the landing page or recommends the product to other consumers after the product purchase. Claim 3 delete Claim 4 delete Claim 5 delete

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