Method for predicting activity cost and target

Abstract

The invention relates to an activity cost and target prediction method, and relates to the technical field of marketing and data analysis, and the method comprises the following steps: collecting and sorting historical activity data, generating a user portrait, obtaining the current sales condition in real time, and comprehensively analyzing key factors influencing the activity effect. Specific sales targets and key performance indicators are set, multiple activity schemes are designed, and real-time monitoring and feedback are carried out in the implementation process. According to the method, the historical activity data, the user portrait, the current sales condition and the sales target are integrated, and the corresponding market activity scheme is formulated by using a big data technology, so that the accuracy and effect of market activities can be improved, resource allocation is optimized, the cost is reduced, and the method has a wide application prospect.

Description

Technical Field

[0001] This application relates to the technical fields of marketing and data analysis, and in particular to the technology that combines computer science and business management related to event cost and target prediction. Background Art

[0002] Currently, in the modern business environment, the success of marketing activities directly affects a company's sales performance and brand image. Traditional marketing activity planning often relies on experience and intuition and lacks systematic data support. The limitation of this method is that it is difficult to quantify the activity effect, and the resource allocation is not reasonable enough, resulting in cost waste and the difficulty in achieving sales targets.

[0003] In addition, with the diversification of consumer demands, companies need to consider more and more the characteristics and behavior patterns of the target audience in activity planning. Although some companies have started to apply data analysis technology, there are still difficulties in integrating historical data, user portraits, and real-time sales situations. Therefore, there is an urgent need for a systematic and scientific method for predicting event costs and targets to improve the accuracy and effectiveness of marketing activities. Summary of the Invention

[0004] The purpose of this application is to provide a method for predicting event costs and targets to solve the problems raised in the above background art.

[0005] The method for predicting event costs and targets provided by this application adopts the following technical solutions: aiming to formulate corresponding event plans by comprehensively analyzing historical event data, personnel portraits, current sales situations and targets, and using big data technology. The method includes the following steps: First, collect historical event data, including budget, actual expenditure, sales volume, number of participants, event type, and implementation time, and organize the collected data into a structured format to establish an event database; Second, generate personnel portraits, analyze the basic information, behavior patterns, and preferences of participants to identify the target audience; Third, obtain current sales situation data in real time, regularly collect sales volume, order quantity, and customer feedback, and analyze market dynamics, competitor activity information, and changes in consumer demands; Fourth, based on big data technology, deeply analyze the collected data to identify the key factors affecting the event effect; Fifth, determine the specific sales targets of the event and formulate relevant key performance indicators; Sixth, design multiple event plans, including budget, resource allocation, and promotion strategies, and evaluate the effects of each plan; Seventh, conduct real-time monitoring and feedback during the event implementation process to adjust the plan in a timely manner to ensure the achievement of the target; Eighth, effect evaluation and optimization. After the implementation of the activity, collect relevant data and conduct effect evaluation, analyze the achievement of each key performance indicator, and optimize the future prediction model and activity plan according to the evaluation results to continuously improve the activity effect.

[0006] By adopting the above technical solutions, multi-dimensional data can be systematically integrated to enhance the scientific nature of activity planning; through accurate user portraits, personalized activity plans can be formulated to enhance customer participation; real-time monitoring and feedback mechanisms can ensure the flexible adjustment of activities, thereby maximizing the sales effect.

[0007] Preferably, the basic information of the participants includes age, gender, geographical location, purchase history, and activity participation frequency. The behavior pattern uses a behavior analysis tool to analyze the purchase decision-making process and preferences of the participants to construct a detailed user portrait.

[0008] Preferably, clustering algorithms are applied to divide the participants into different groups to facilitate the formulation of personalized activity plans for each group.

[0009] Preferably, using big data mining technology, comprehensively analyze the historical activity data, personnel portraits, and current sales situation, and adopt machine learning algorithms to identify the key factors affecting the activity effect and generate a prediction model.

[0010] Preferably, the machine learning algorithm is a regression analysis algorithm. The regression analysis algorithm formula used mainly revolves around the multiple linear regression model. The specific formula can be expressed as: Multiple linear regression formula Y = β 0 + β 1 X 1 + β 2 X 2 + … + β n X n + ∈ Formula description: Y: Dependent variable, usually the goal of the marketing activity, such as sales volume; X 1 , X 2 , …, X n : Independent variables, which may include influencing factors such as activity type, discount strength, market trend, and user portrait characteristics; β 0 : Intercept, indicating the expected value of the dependent variable when all independent variables are zero; β 1 , β 2 , …, β n : Regression coefficients of each independent variable, indicating the degree of influence of the independent variable on the dependent variable; ∈: The error term, representing the difference between the dependent variable and the predicted value, reflecting the part not explained by the model; Least squares method S: The sum of squared errors.

[0011] Y i : The actual observed value.

[0012] The predicted value calculated according to the regression model.

[0013] Coefficient of determination (R 2 ) SS res : The sum of squared residuals, the variation not explained by the model. SS tot : The total sum of squares, the total variation of the dependent variable.

[0014] Hypothesis testing Use t-test and F-test to test the significance of model parameters. The calculation formula for the t-value is: Through the above formula, the effect of market activities can be systematically analyzed to help formulate more accurate market strategies.

[0015] Preferably, according to the structure of historical activity data, set the specific sales target, which is the percentage increase in sales or the number of customers acquired, and formulate relevant key performance indicators to ensure that the target is quantifiable and traceable.

[0016] Preferably, the activity plan is designed based on the data analysis results, including budget allocation, resource allocation, and activity form. Conduct a SWOT analysis on each plan to evaluate its potential effect, risk, and market adaptability.

[0017] Preferably, conduct a simulation test on each activity plan to evaluate its performance in the actual environment, and adjust the plan details according to the evaluation results to ensure the selection of the optimal plan to maximize the activity effect.

[0018] Preferably, evaluate the marketing effects of different channels to select the optimal channel for activity promotion.

[0019] Preferably, establish a data security and privacy protection mechanism to ensure the effective protection of participants' personal information during the activity. The description of the data security and privacy protection mechanism is as follows: Legitimacy and Transparency of Data Collection User Consent: Obtain explicit consent from users before collecting their personal information. Inform users through the privacy policy about how their data will be used, including the purpose, scope of collection, and potential data sharing recipients. Privacy Policy: Develop a clear and easy-to-understand privacy policy that details how data is collected, stored, processed, and used. Provide users with the means to access and modify their personal information at any time. Data Encryption during Transmission Encryption: Use the SSL / TLS protocol to encrypt data during transmission to prevent data from being stolen or tampered with during transit. Storage Encryption: Encrypt sensitive data stored in the database (such as personal identity information, payment information, etc.) to ensure that even if the data is leaked, attackers cannot easily obtain usable information. Access Control and Permission Management: Implement strict permission management for users accessing data to ensure that only authorized personnel can access specific data. Use role-based access control (RBAC) to restrict data access. Authentication: Implement strong authentication mechanisms such as two-factor authentication (2FA) to enhance the security of data access. Data Minimization Principle Necessary Collection: Only collect data necessary to achieve specific purposes. Avoid collecting unnecessary personal information to reduce potential privacy risks. Regular Audits: Regularly audit and update the collected data to ensure that data that is no longer used or needed can be deleted in a timely manner. Data Storage and Processing Secure Storage: Store data on secure servers and adopt measures such as firewalls and intrusion detection systems to prevent unauthorized access. Data Partitioning: Store sensitive data separately from non-sensitive data to reduce the risk of sensitive information leakage. Data Access Records and Monitoring Log Recording: Record all access and modification operations on data, including the identity of the visitor, time, and type of operation, for subsequent auditing and tracking. Real-time Monitoring: Implement a real-time monitoring system to detect abnormal access behaviors and take timely measures. Data Breach Emergency Response Plan Emergency Response Team: Form a dedicated data breach emergency response team and develop a detailed emergency response plan to take prompt action in the event of a data breach. Notification Mechanism: Once a data breach is confirmed, promptly notify affected users and report to the regulatory authorities in accordance with relevant laws and regulations. The data sharing and third-party cooperation contract stipulates that a contract is signed with a third-party service provider to clarify the responsibilities for data use and protection and requires it to follow the same data protection standards; Regular audits: Regularly audit partners to ensure they follow the data protection agreement; User rights Right of access and right of deletion: Users have the right to request access to their personal data and to request the deletion of data that is no longer needed or has been processed improperly; Data portability: Users have the right to request that their data be exported in a common format for use on other platforms.

[0020] In summary, this application includes at least one of the following beneficial technical effects: 1. The method for predicting activity costs and targets provided by this application formulates corresponding activity plans by comprehensively analyzing historical activity data, personnel portraits, current sales situations, and targets, and using big data technology. This method can improve the success rate of activities, optimize resource allocation, and reduce costs; 2. The method for predicting activity costs and targets provided by this application combines big data technology and systematic analysis, can effectively improve the success rate of marketing activities and resource utilization efficiency, and has broad application prospects and market value; 3. The method for predicting activity costs and targets of this application can systematically integrate multi-dimensional data, improve the scientific nature of activity planning; formulate personalized activity plans through accurate user portraits to enhance customer participation; real-time monitoring and feedback mechanisms ensure flexible adjustment of activities, thereby maximizing sales effects. Brief description of the drawings

[0021] Figure 1 is the method flow block diagram of this application; Figure 2 is the page diagram of the coupon management system of this application; Figure 3 is the page diagram of the prize management system of this application. Detailed implementation manners

[0022] The following is a further detailed description of this application in combination with the attached Figure 1 ,.

[0023] A method for predicting activity costs and targets of this application, the core steps include: 1) Data collection By establishing an activity database, systematically collect various data of historical activities. The data sources include: Budget: Activity budget and actual expenditure records; Sales volume: Sales volume data during the activity period; Number of participants: The number of customers participating in the activity; Activity type: Classification information for different types of activities; Implementation time: The start and end times of the activity.

[0024] 2) Generation of personnel portraits Using data analysis tools, generate personnel portraits. The analysis content includes: Basic information: Age, gender, geographical location, purchase history, activity participation frequency, etc.; Behavior pattern: Through behavior analysis tools, analyze the purchase decision-making process and preferences of participants to construct a detailed user portrait.

[0025] 3) Acquisition of real-time sales data Through system automation means, obtain real-time data on the current sales situation and collect regularly: Sales amount: Changes in the sales amount during different time periods; Number of orders: Statistics on the number of orders of activity participants; Customer feedback: Collect customer feedback information through questionnaires or online reviews.

[0026] 4) In-depth data analysis Adopt big data technology to conduct in-depth analysis of the above data and identify key factors affecting the activity effect, including: Market dynamics: Analyze market trends and changes in consumer behavior; Competitor activity information: Collect the activity situations of competitors and conduct comparative analysis; Changes in consumer needs: Through data mining technology, identify the changing trends of consumer needs.

[0027] 5) Setting sales targets Based on historical data and market analysis results, set specific sales targets to ensure the quantifiability and traceability of the targets. The specific targets may include: Percentage increase in sales amount: The growth rate of the target sales amount compared to the previous activity; Number of customers acquired: The target number of new customers.

[0028] 6) Design of multiple solutions For different market demands and target customers, design multiple activity solutions. Each solution includes: Budget allocation: Specific allocation of various expenses; Resource allocation: Resource allocation strategies for personnel, materials, time, etc.; Activity form: Selection of offline activities, online activities or hybrid activities; Conduct a SWOT analysis on each solution to evaluate its potential effect, risks and market adaptability.

[0029] 7) Real-time monitoring and feedback During the implementation of the activity, establish a real-time monitoring system to ensure that the activity progress data can be obtained in a timely manner, and adjust the activity plan according to the feedback to ensure the achievement of the goal.

[0030] 8) Effect evaluation and optimization After the activity ends, collect relevant data for effect evaluation, and analyze the achievement of each key performance indicator. According to the evaluation results, optimize the future prediction model and activity plan to continuously improve the activity effect.

[0031] Example 1: A method for predicting activity cost and target. For a certain e-commerce company planning to launch a series of promotional activities during the upcoming "Double Eleven" shopping festival, in order to ensure the success of the activity, the company decides to use the method of this application to predict the activity cost and target.

[0032] Detailed steps 1. Collection of historical activity data: The company reviewed the "Double Eleven" promotional activities in the past three years and collected detailed data including activity cost, sales volume, number of participants, limited-time discount activities, and coupon activities. Through the coupon management system page (Appendix Figure 2 ), set, released, taken off the shelf, used details and statistics of the coupons; after data collation, a structured database was established, containing the key performance indicators of each activity.

[0033] 2. Generation of user portraits: By analyzing the customer database, extract the user information of the participants in the activity, including age, gender, geographical location, and purchase history. Using cluster analysis, identify three main customer groups: young women, housewives, and male consumers; generate detailed user portraits for each group, including their shopping preferences and frequency of participating in activities.

[0034] 3. Obtaining the current sales situation: Real-time monitor the sales data of the company's website, regularly analyze the sales volume, order quantity, and customer feedback, and evaluate the mood and expectations of consumers for the upcoming "Double Eleven" activity through social media monitoring tools.

[0035] 4. Big data analysis: Using data mining technology, comprehensively analyze the historical activity data, user portraits, and current sales situation; adopt regression analysis to identify the key factors affecting sales, such as activity type, discount strength, etc., and generate a prediction model; The algorithm formula of regression analysis is as follows: Y = β 0 +β1 X 1 +β 2 X 2 +…+β n X n +∈ Formula Explanation: Y: Dependent variable, usually the goal of the marketing activity, such as sales volume; X 1 ,X 2 ,…,X n : Independent variables, including activity type, discount intensity; β 0 : Intercept, representing the expected value of the dependent variable when all independent variables are zero; β 1 ,β 2 ,…,β n : Regression coefficients of respective independent variables, representing the influence degree of independent variables on the dependent variable; ∈: Error term, representing the difference between the dependent variable and the predicted value, reflecting the part not explained by the model; Least Squares Method S: Sum of squared errors.

[0036] Y i : Actual observed value.

[0037] Predicted value calculated according to the regression model.

[0038] Coefficient of determination (R 2 ) SS res : Sum of squared residuals, the variation not explained by the model. SS tot : Total sum of squares, the total variation of the dependent variable.

[0039] Hypothesis Testing Use t-test and F-test to test the significance of model parameters. The calculation formula of t value is: Through the above formula, systematically analyze the effect of the marketing activity and identify the key factors affecting sales.

[0040] 5. Goal Setting: According to the data analysis results, the company sets the sales goal: a 20% increase in sales volume compared to the "Double Eleven" sales volume last year; formulates relevant KPIs: number of customers acquired, average order amount.

[0041] 6. Activity Plan Design: Three activity plans were designed: Plan A, time-limited discount; Plan B, full reduction activity; Plan C, buy one get one free. A SWOT analysis was conducted on each plan to evaluate its potential effects, risks, and market adaptability. Finally, Plan A and Plan B were selected for implementation.

[0042] 7. Plan Effect Evaluation: Before the activity started, a small-scale simulation test was carried out to evaluate the performance of different plans. According to the feedback results, the activity details were adjusted to determine the final activity plan.

[0043] 8. Implementation and Feedback: A detailed implementation plan was formulated, including task allocation, time nodes, and resource requirements. During the "Double Eleven" period, the sales data and customer feedback were monitored in real time during the implementation process, and the plan was adjusted in a timely manner to respond to market changes.

[0044] 9. Effect Evaluation and Optimization: After the activity ended, the sales data was collected, and the achievement of each KPI was analyzed. According to the evaluation results, the future prediction model was optimized to ensure better effects of subsequent activities.

[0045] Example 2: A method for predicting activity costs and targets, a precision marketing activity based on user portraits. A certain chain retail supermarket plans to launch a new round of promotional activities in summer and uses the method of this application for targeted market promotion.

[0046] Detailed Steps 1. Collection of Historical Activity Data: Data on past summer promotional activities was collected, including the number of customer participants, sales volume, and activity costs. The data was sorted and stored in the activity database.

[0047] 2. Generation of Personnel Portraits: User portraits were generated through membership registration information and purchase history, with special attention paid to household consumers. The shopping habits of consumers were identified. For example, household consumers prefer to buy cold drinks and outdoor supplies in summer.

[0048] 3. Obtaining Current Sales Situation: The sales data of the supermarket in the past few months was monitored to analyze seasonal trends. Through customer feedback and social media, the interest of consumers in summer activities was evaluated.

[0049] 4. Big Data Analysis: Using big data analysis tools, regression analysis was adopted to analyze historical data and current market trends, and factors affecting sales were identified, such as promotion time, product mix, etc.; The algorithm formula for regression analysis is as follows: Y = β 0 + β 1 X 1 + β 2 X 2 + … + β n X n + ∈ Formula Explanation: Y: Dependent variable, usually the goal of marketing activities, such as sales volume; X 1 , X 2 , …, X n : Independent variables, including activity type, discount rate; β 0 : Intercept, representing the expected value of the dependent variable when all independent variables are zero; β 1 , β 2 , …, β n : Regression coefficients of respective independent variables, representing the influence degree of independent variables on the dependent variable; ∈: Error term, representing the difference between the dependent variable and the predicted value, reflecting the part not explained by the model; Least Squares Method S: Sum of Squares of Errors.

[0050] Y i : Actual observed value.

[0051] Predicted value calculated according to the regression model.

[0052] Coefficient of Determination (R 2 ) SS res : Sum of Squares of Residuals, variation not explained by the model. SS tot : Total Sum of Squares, total variation of the dependent variable.

[0053] Hypothesis Testing Use t-test and F-test to test the significance of model parameters. The calculation formula of t value is: Through the above formula, systematically analyze historical data and current market trends to identify factors affecting sales.

[0054] 5. Goal Setting: Set the sales goal as a 15% increase in last year's summer sales volume and formulate corresponding KPIs.

[0055] 6. Activity plan design: Promotion activities targeting household consumers were designed, such as "family package" discounts and "buy one get one free" offers. Through the prize management system page (appendix Figure 3 ), a full-track prize tracing was established in the background, and a market adaptability analysis was conducted for each plan to ensure the feasibility of the plan.

[0056] 7. Plan effect evaluation: Before the official launch of the activity, the activity effect was tested through small-scale pilot tests, feedback data was collected, and the activity content and promotion methods were adjusted according to the feedback.

[0057] 8. Implementation and feedback: An implementation plan was formulated, clarifying the responsible persons and time nodes. During the activity, the sales situation was monitored in real time, customer feedback was collected, and the activity strategy was adjusted in a timely manner.

[0058] 9. Effect evaluation and optimization: After the activity ended, a comprehensive analysis of the sales data was conducted, each KPI was evaluated, and according to the evaluation results, the user portrait and market strategy were adjusted to provide a reference for future activities.

[0059] Example 3: Prediction and implementation of large-scale promotion activities A retail company plans to conduct a large-scale promotion activity during holidays to increase sales and customer participation. To ensure the success of the activity, the prediction method of activity cost and target of the present invention is adopted for comprehensive analysis and prediction.

[0060] Detailed steps 1. Collection of historical activity data: Collect activity data during the same holiday in the past three years, including budget (50,000 yuan), actual expenditure (45,000 yuan), sales volume (200,000 yuan), number of participants (800 people), activity type (combination of online and offline), and implementation time (one week).

[0061] 2. Generation of personnel portrait: By analyzing the customer database, a portrait of the participants was generated, including age (25 - 45 years old), gender (male-female ratio 1:1), geographical location (city and surrounding areas), purchase history (purchase records in the past three months), and activity participation frequency (participate once every quarter on average).

[0062] 3. Obtaining the current sales situation: Two weeks before the activity, the current sales data was obtained in real time, including sales volume (30,000 yuan), number of orders (150), and customer feedback (collected through social media and online surveys).

[0063] 4. Big data analysis: Based on historical data, use big data technology to analyze the key factors affecting the activity effect, such as customer preferences, the choice of promotion time periods, and competitors' strategies; 5. Goal setting: Set the sales goal for this activity to a 20% increase (compared to the previous year), that is, the target sales amount is 240,000 yuan. The key performance indicators include sales amount, the number of customer participants, and customer satisfaction (collected through survey feedback).

[0064] 6. Activity plan design: Design multiple plans, including different promotion strategies: full reduction, discount, and giveaways, and evaluate the budget allocation for each plan (20,000 yuan, 15,000 yuan, and 10,000 yuan respectively).

[0065] 7. Plan effect evaluation: Conduct a SWOT analysis for each plan to identify its potential effects, risks, and market adaptability.

[0066] 8. Implementation and feedback: During the implementation of the activity, use data monitoring tools to track sales data and customer participation in real time, and adjust the promotion strategy in a timely manner.

[0067] 9. Effect evaluation and optimization: Collect data after the activity ends, analyze the actual sales amount (250,000 yuan), the number of participants (1,000 people), and customer satisfaction (90% satisfied), and optimize the future activity prediction model based on the evaluation results.

[0068] The embodiments of this specific implementation manner are all preferred embodiments of this application, and do not limit the protection scope of this application accordingly. The same components are represented by the same reference numerals. Therefore, all equivalent changes made according to the structure, shape, and principle of this application shall be covered within the protection scope of this application.

Claims

1. The method for forecasting activity costs and targets is characterized by: The method comprises the following steps: S1. Collect historical activity data, including budget, actual expenditure, sales, number of participants, activity type and implementation time, and organize the collected data into a structured format to establish an activity database; S2. Generate personnel portraits and analyze participants’ basic information, behavior patterns, and preferences to identify target audiences; S3. Obtain current sales data in real time, regularly collect sales, order quantity and customer feedback, and analyze market dynamics, competitor activity information and changes in consumer demand; S4. Based on big data technology, conduct in-depth analysis of the collected data to identify the key factors that affect the effectiveness of the activity; S5. Determine the specific sales target of the activity and develop relevant key performance indicators; S6. Design various activity plans, including budget, resource allocation and promotion strategies, and evaluate the effectiveness of each plan; S7. Conduct real-time monitoring and feedback during the implementation of the activity so as to adjust the plan in time to ensure the achievement of the goal; S8. Effect evaluation and optimization. After the implementation of the activity, collect relevant data and conduct effect evaluation, analyze the achievement of each key performance indicator, and optimize future forecasting models and activity plans based on the evaluation results to continuously improve the activity results.

2. The method for predicting activity costs and targets according to claim 1, characterized in that: The basic information of the participants includes age, gender, geographic location, purchase history and frequency of activity participation, and the behavioral patterns use behavioral analysis tools to analyze the participants' purchase decision-making process and preferences to build a detailed user portrait.

3. The method for predicting activity costs and targets according to claim 2, characterized in that: A clustering algorithm is applied to divide the participants into different groups so as to formulate personalized activity plans for each group.

4. The method for predicting activity costs and targets according to claim 1, characterized in that: Using big data mining technology, we conduct a comprehensive analysis of the historical activity data, personnel portraits and current sales conditions, and use machine learning algorithms to identify key factors that affect activity results and generate a prediction model.

5. The method for predicting activity costs and targets according to claim 4, characterized in that: The machine learning algorithm is a regression analysis algorithm.

6. The method for predicting activity costs and targets according to claim 1, characterized in that: Based on the structure of historical activity data, set specific sales targets, which are sales growth percentage or customer acquisition quantity, and formulate relevant key performance indicators to ensure that the targets are quantifiable and traceable.

7. The method for predicting activity costs and targets according to claim 1, characterized in that: The activity plan is designed based on the results of data analysis, including budget allocation, resource allocation and activity form. A SWOT analysis is conducted on each plan to evaluate its potential effects, risks and market adaptability.

8. The method for predicting activity costs and targets according to claim 7, characterized in that: Conduct simulation tests on each of the activity plans to evaluate their performance in the actual environment, adjust the plan details based on the evaluation results, and ensure that the best plan is selected to maximize the activity effect.

9. The method for predicting activity costs and targets according to claim 1, characterized in that: Evaluate the marketing effectiveness of different channels in order to select the best channel for event promotion.

10. The method for predicting activity costs and targets according to claim 1, characterized in that: Establish a data security and privacy protection mechanism to ensure that participants' personal information is effectively protected during the event.

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