Offline marketing activity site selection method and system based on social media data

Abstract

The invention discloses an offline marketing activity site selection method and system based on social media data, and relates to the technical field of offline marketing activity site selection. An address data acquisition module obtains geographic information based on online map service and acquires business district distribution and people flow data; the brand data acquisition module is used for capturing brand-related social interaction data in batches and analyzing comment emotion and topic trend; the data analysis module fuses and collects long video data, short video data and image-text data of the plurality of platform data sources, identifies social media data related to brands, and constructs brand portrait data based on the social media data; through real-time analysis of multi-source data, accurate coverage of a target customer group is realized. Automatic data processing and intelligent recommendation reduce manual operation and improve efficiency. The method has high adaptability and learning ability, and can dynamically adjust marketing suggestions to adapt to market changes. And the positioning accuracy of the target customer is improved, so that the influence and the rate of return on investment of marketing activities are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of offline marketing activity site selection, and in particular to a method and system for offline marketing activity site selection based on social media data. Background Art

[0002] The Chinese invention patent with publication number CN116757737A discloses a marketing method and device based on address information, which includes: obtaining the address information of customers and merchants; constructing an inverted index table based on the address information; after obtaining the address information of a new customer, searching the inverted index table based on the keywords in the address information of the new customer to obtain the candidate address data of the new customer; matching the address information of the new customer with the candidate address data of the new customer to obtain a matching address; dividing the new customer into an address grid corresponding to the matching address, wherein the address grid is obtained by dividing the geographic space; and conducting marketing analysis on the new customer based on the portrait data in the address grid. The present invention can accurately match the customer address and the marketing address, so that the relationship between the customer address data and the marketing address can be more accurately analyzed, and precision marketing can be achieved.

[0003] The problems with the above technical solution are: the marketing address mining method is not clear; and based on the relationship between customer address data and marketing addresses, the specific implementation content is not clear.

[0004] Therefore, it is urgent to design a method and system for offline marketing activity site selection based on social media data to solve the current problem of accurate site selection for enterprises in planning offline marketing activities. Summary of the invention

[0005] In order to solve the above technical problem of accurate site selection in offline marketing activities of current enterprises, the present invention provides a method and system for offline marketing activity site selection based on social media data. The following technical solutions are adopted:

[0006] A method for selecting a site for an offline marketing activity based on social media data, comprising the following steps:

[0007] Step 1: The address data collection module obtains geographic information based on online map services and collects business district distribution and passenger flow data;

[0008] Step 2: The brand data collection module batch-captures brand-related social interaction data and analyzes comment sentiment and topic trends;

[0009] Step 3: The data analysis module processes the geographic location information and integrates multiple data to form point portrait data;

[0010] Step 4: The data analysis module integrates the long video data, short video data, and graphic data collected from multiple platform data sources, identifies social media data related to the brand, and constructs brand portrait data based on social media data;

[0011] Step 5: The offline site recommendation module uses collaborative filtering to find the best matching brand data based on the site portrait;

[0012] Based on brand portraits, collaborative filtering is used to find the best matching points from the scene group to form a list of offline site recommendations;

[0013] Step 6: The content generation module generates marketing content based on the big model.

[0014] Optionally, in step 1, the address data collection module uses Internet crawler technology and API interface to obtain geographic information from online map services and collect business district distribution and passenger flow data;

[0015] The sources of business district distribution and passenger flow data also include on-site sampling and reporting. Business personnel use GPS recording and real-time image uploading to conduct on-site sampling and reporting.

[0016] By adopting the above technical solutions, accurate coverage of target customer groups can be achieved through real-time analysis of multi-source data. Automated data processing and intelligent recommendations reduce manual operations and improve efficiency. With high adaptability and learning capabilities, marketing suggestions can be dynamically adjusted to adapt to market changes. The positioning accuracy of target customers is improved, thereby enhancing the influence and return on investment of marketing activities.

[0017] Optionally, in step 2, natural language processing technology is used to analyze comment sentiment and topic trends.

[0018] By adopting the above technical solutions, we can batch capture brand-related social interaction data and use natural language processing, such as NLP technology, to analyze comment sentiment and topic trends;

[0019] The specific steps are as follows: Collect comment data from social media platforms. Remove noise such as emoticons, links, non-text characters, etc. through data cleaning. Perform sentiment classification on comments. Identify the main topics in the comments; Analyze the changing trends of topics and sentiments over time; LP technology can understand the public's views on a topic or product, thus providing a basis for subsequent brand portraits.

[0020] Optionally, in step 3, the data analysis module processes the geographical location information and integrates multiple data for the collected point data to form point portrait data;

[0021] Clustering algorithms are used to cluster the feature items of the point data, and the points are formed into several category groups based on their geographical locations and feature portraits, and the points that have been successfully promoted in the past are expanded into type groups.

[0022] By adopting the above technical solutions, the collected point data is processed and the geographical location information is integrated to form point portrait data, such as point type, user rating, customer unit price, surrounding housing prices, province, city, district, business district, etc. The clustering algorithm (k-means) is further used for clustering, and the points are formed into several categories according to the geographical location and feature portraits, and the points that have been successfully promoted in the past are expanded to type groups.

[0023] Optionally, in step 3, K-means clustering algorithm and PCA dimensionality reduction are used to update and optimize the population model every hour.

[0024] Optionally, in step 4, use NLP and OCR technology to identify social media data related to the brand, build various feature items of the brand portrait based on the recognition results, train the neural network model through PyTorch, and update the consumer portrait every hour.

[0025] By adopting the above-mentioned technical solution, the collected long video data, short video data, graphic data and other data sources from multiple platforms are integrated, and NLP and OCR technologies are used to identify the various feature items related to building a brand portrait in the brand-related social media data. Each feature item refers to the consumer's age, focus, topics of interest, etc., thereby forming brand portrait data.

[0026] Optionally, in step 6, a marketing campaign plan is designed based on the point portrait data formed in step 3 and the brand portrait data generated in step 4, and a marketing plan generation model is used to automatically generate suitable visual materials and marketing copy.

[0027] By adopting the above technical solution, the marketing plan generation model can be a self-developed model or a commercial AI model. It can automatically design marketing activities based on the location portrait data formed in step 3, such as location type, user rating, average customer price, surrounding housing prices, province, city, district, business district, and brand portrait data in step 4, such as consumer age, focus, and topic of concern, and automatically generate suitable visual materials;

[0028] Based on the location portrait data formed in step 3, such as location type, user rating, average order value, surrounding housing prices, province, city, district, and business district, and the brand portrait data in step 4, such as consumer age, focus, and topics of interest, a marketing campaign plan is designed to automatically generate appropriate marketing copy.

[0029] An offline marketing activity site selection system based on social media data is used to implement a method for offline marketing activity site selection based on social media data. The offline marketing activity site selection system includes an address data collection module, a brand data collection module, a data analysis module, an offline site selection recommendation module and a content generation module. The address data collection module and the brand data collection module are respectively communicated with the data analysis module, the data analysis module is communicated with the offline site selection recommendation module, the data analysis module and the offline site selection recommendation module are respectively communicated with the content generation module, and the content generation module automatically generates adaptive visual materials and marketing copy based on a large model generated by a marketing plan.

[0030] By adopting the above technical solution, the address data collection module includes two parts, one is the collection of online geographic information, and the other is the on-site sampling and reporting of offline geographic information. The on-site sampling and reporting of offline geographic information can be based on the application of mobile devices, so that business personnel can use GPS recording and real-time image uploading functions;

[0031] The brand data collection module can be implemented based on online data capture tools or software;

[0032] The data analysis module can be implemented based on a computer that deploys relevant data analysis models.

[0033] In summary, the present invention includes at least one of the following beneficial technical effects:

[0034] The present invention can provide a method and system for offline marketing activity site selection based on social media data, which can achieve accurate coverage of target customer groups through real-time analysis of multi-source data. Automated data processing and intelligent recommendation reduce manual operations and improve efficiency. It has high adaptability and learning ability, and can dynamically adjust marketing suggestions to adapt to market changes. It improves the accuracy of target customer positioning, thereby enhancing the influence and return on investment of marketing activities. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of a method for selecting a site for an offline marketing activity based on social media data according to the present invention. DETAILED DESCRIPTION

[0036] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0037] The embodiments of the present invention disclose a method and system for selecting a site for an offline marketing activity based on social media data.

[0038] Reference Figure 1 , a method for selecting a site for an offline marketing activity based on social media data, comprising the following steps:

[0039] Step 1: The address data collection module obtains geographic information based on online map services and collects business district distribution and passenger flow data;

[0040] Step 2: The brand data collection module batch-captures brand-related social interaction data and analyzes comment sentiment and topic trends;

[0041] Step 3: The data analysis module processes the geographic location information and integrates multiple data to form point portrait data;

[0042] Step 4: The data analysis module integrates the long video data, short video data, and graphic data collected from multiple platform data sources, identifies social media data related to the brand, and constructs brand portrait data based on social media data;

[0043] Step 5: The offline site recommendation module uses collaborative filtering to find the best matching brand data based on the site portrait;

[0044] Based on brand portraits, collaborative filtering is used to find the best matching points from the scene group to form a list of offline site recommendations;

[0045] Step 6: The content generation module generates marketing content based on the big model.

[0046] In step 1, the address data collection module uses Internet crawler technology and API interface to obtain geographic information from online map services and collect business district distribution and passenger flow data;

[0047] The sources of business district distribution and passenger flow data also include on-site sampling and reporting. Business personnel use GPS recording and real-time image uploading to conduct on-site sampling and reporting.

[0048] Through real-time analysis of multi-source data, accurate coverage of target customer groups is achieved. Automated data processing and intelligent recommendations reduce manual operations and improve efficiency. With high adaptability and learning capabilities, marketing suggestions can be dynamically adjusted to adapt to market changes. The accuracy of target customer positioning is improved, thereby increasing the impact and return on investment of marketing activities.

[0049] In step 2, natural language processing technology is used to analyze comment sentiment and topic trends.

[0050] Capture brand-related social interaction data in batches and use natural language processing, such as NLP technology, to analyze comment sentiment and topic trends;

[0051] The specific steps are as follows: Collect comment data from social media platforms. Remove noise such as emoticons, links, non-text characters, etc. through data cleaning. Perform sentiment classification on comments. Identify the main topics in the comments; Analyze the changing trends of topics and sentiments over time; LP technology can understand the public's views on a topic or product, thus providing a basis for subsequent brand portraits.

[0052] In step 3, the data analysis module processes the geographical location information and integrates multiple data from the collected point data to form point portrait data;

[0053] Clustering algorithms are used to cluster the feature items of the point data, and the points are formed into several category groups based on their geographical locations and feature portraits, and the points that have been successfully promoted in the past are expanded into type groups.

[0054] For the collected point data, the geographical location information is processed and multi-party data is integrated to form point portrait data, such as point type, user rating, customer unit price, surrounding housing prices, province, city, district, business district, etc. The clustering algorithm (k-means) is further used for clustering, and the points are formed into several types of groups according to their geographical location and feature portraits, and the points that have been successfully promoted in the past are expanded to type groups.

[0055] In step 3, K-means clustering algorithm and PCA dimensionality reduction are used to update and optimize the population model every hour.

[0056] In step 4, NLP and OCR technologies are used to identify social media data related to the brand. The various feature items of the brand portrait are constructed based on the identification results. The neural network model is trained through PyTorch, and the consumer portrait is updated every hour.

[0057] By integrating the collected long video data, short video data, graphic data and other data sources from multiple platforms, NLP and OCR technology are used to identify the various feature items related to building brand portraits in brand-related social media data. Each feature item refers to the consumer's age, focus, topics of interest, etc., thereby forming brand portrait data.

[0058] In step 6, a marketing campaign plan is designed based on the point portrait data formed in step 3 and the brand portrait data generated in step 4, and a marketing plan generation model is used to automatically generate suitable visual materials and marketing copy.

[0059] The marketing plan generation model can be a self-developed model or a commercial AI model. It can automatically design marketing plans based on the location portrait data formed in step 3, such as location type, user rating, average customer price, surrounding housing prices, province, city, district, and business district, and the brand portrait data in step 4, such as consumer age, focus, and topic of concern, and automatically generate suitable visual materials.

[0060] Based on the location portrait data formed in step 3, such as location type, user rating, average order value, surrounding housing prices, province, city, district, and business district, and the brand portrait data in step 4, such as consumer age, focus, and topics of interest, a marketing campaign plan is designed to automatically generate appropriate marketing copy.

[0061] For example, the intelligent marketing generation model (SMG) can be used to generate the above-mentioned marketing plan. The SMG large model analyzes location portrait data and brand portrait data to provide marketers with personalized visual materials and marketing copy, thereby improving the effectiveness and conversion rate of marketing activities.

[0062] The SMG big model can connect location portrait data and brand portrait data to conduct in-depth analysis of target consumers, competitors, market environment, etc.

[0063] Based on the data analysis results, the SMG big model automatically generates visual materials that are in line with the brand tone and target consumer preferences.

[0064] The SMG big model uses natural language processing technology, combined with brand characteristics and target consumer needs, to automatically generate creative copy.

[0065] The SMG big model continuously learns from user feedback and marketing effectiveness data, optimizes visual materials and copywriting, and improves the adaptability of marketing plans.

[0066] Specific application:

[0067] Connect point portrait data and brand portrait data to the SMG big model.

[0068] The SMG big model processes and analyzes data to mine key information such as target consumers, competitors and market environment.

[0069] SMG big model selects appropriate visual materials and copywriting templates according to brand characteristics and marketing goals.

[0070] The SMG big model automatically generates visual materials and marketing copy based on templates and data analysis results.

[0071] Marketing personnel review and modify the generated visual materials and copy to ensure that they meet actual needs.

[0072] Publish the generated marketing plan to major platforms, collect feedback data, and continuously optimize the SMG big model.

[0073] An offline marketing activity site selection system based on social media data is used to implement a method for offline marketing activity site selection based on social media data. The offline marketing activity site selection system includes an address data collection module, a brand data collection module, a data analysis module, an offline site selection recommendation module and a content generation module. The address data collection module and the brand data collection module are respectively communicated with the data analysis module, the data analysis module is communicated with the offline site selection recommendation module, the data analysis module and the offline site selection recommendation module are respectively communicated with the content generation module, and the content generation module automatically generates adaptive visual materials and marketing copy based on a large model generated by a marketing plan.

[0074] The address data collection module consists of two parts: one is the collection of online geographic information, and the other is the on-site sampling and reporting of offline geographic information. The on-site sampling and reporting of offline geographic information can be based on the application of mobile devices, allowing business personnel to use GPS recording and real-time image uploading functions;

[0075] The brand data collection module can be implemented based on online data capture tools or software;

[0076] The data analysis module can be implemented based on a computer that deploys relevant data analysis models.

[0077] The following is a specific case study to illustrate a method and system implementation principle for offline marketing site selection based on social media data:

[0078] Client: A pharmaceutical company

[0079] Product: A certain brand of oral liquid

[0080] Core question: How to carry out offline activities for a certain brand of oral liquid?

[0081] Address data collection:

[0082] Public data collection: Based on public data (such as Meituan, travel websites, etc.), a large amount of point data of scenic spots / business districts is collected, including location, user ratings, average order value, etc.

[0083] Salesman inspection and reporting: The company's salesmen will regularly go offline:

[0084] 1) Negotiate with the location and obtain authorization to place advertisements; 2) Report data based on the provided dimensions and complete or proofread the location data

[0085] Brand data collection:

[0086] Social media data collection, based on the product keywords (a certain brand of oral liquid) and competitor keywords (Huoxiang Zhengqi Water, Shidi Shui), the whole network data collection is carried out, including Weibo, Toutiao, Xiaohongshu, Douyin, and Kuaishou. During the collection process, OCR technology is used to obtain the keyword content that appears in the pictures and videos to ensure the comprehensiveness of the data.

[0087] Enterprise data upload supports enterprises uploading internal marketing materials. Enterprises usually have a large amount of approved marketing materials, so they can be used as material input for subsequent large model content generation.

[0088] Point portrait: The collected point data (scenic spots, business districts, etc.) are put into the data warehouse for cleaning and dimensional modeling. At the same time, algorithms such as K-means clustering algorithm and PCA dimensionality reduction are used to construct point similarity features, which are combined into point portrait data.

[0089] Brand portrait: NLP analysis is performed based on the acquired data, including word segmentation, stop word removal, intent identification, and topic LDA extraction. Through social media data and NLP analysis, we can clearly get the focus of this product and competitors in social media. Such as heatstroke prevention, travel essentials, travel partners, etc. We can also see the difference: a certain brand of oral liquid will focus on non-alcoholic and can be drunk while driving, while a certain oral liquid contains alcohol. These brand mindsets form brand portrait data.

[0090] Offline site selection recommendation:

[0091] Based on the location recommendation, based on the characteristics of a certain location and the current time characteristics, the trained recommendation search framework is used to find suitable brands in the brand library. For example, a dinosaur park in a certain place has a lot of children, so it is suitable to associate it with children's beverage brands; for example, an ancient town has a lot of open-air places, so it is suitable to place a certain brand of oral liquid and other anti-heat products.

[0092] Recommendation based on brand portrait: Based on a certain brand of oral liquid, find a suitable location. For example, a certain brand of oral liquid is suitable for summer heatstroke prevention and alcohol-free use. Therefore, offline activities can be held in the cooling area and parking lot of tourist attractions (a famous mountain attraction, an ancient town, a cave tourist attraction, etc.).

[0093] Content generation module:

[0094] Image marketing content generation, based on the characteristics of the brand and the location, uses the big model to generate some image marketing materials. For example, combining the IP of a certain brand of oral liquid and the characteristics of a certain scenic spot, generate some image marketing materials (posters, roll-up banners, stickers):

[0095] Text marketing content generation: Based on the characteristics of the brand and the location, some text marketing materials are generated using the big model. For example, combining the IP of a certain brand of oral liquid and the characteristics of a certain scenic spot, some text content suitable for forwarding to Weibo, Xiaohongshu, and Moments is generated.

[0096] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for selecting a site for offline marketing activities based on social media data, characterized in that: The following steps are involved: Step 1: The address data collection module obtains geographic information based on online map services and collects business district distribution and passenger flow data; Step 2: The brand data collection module batch-captures brand-related social interaction data and analyzes comment sentiment and topic trends; Step 3: The data analysis module processes the geographic location information and integrates multiple data to form point portrait data; Step 4: The data analysis module integrates the long video data, short video data, and graphic data collected from multiple platform data sources, identifies social media data related to the brand, and constructs brand portrait data based on social media data; Step 5: The offline site recommendation module uses collaborative filtering to find the best matching brand data based on the site portrait; Based on brand portraits, collaborative filtering is used to find the best matching locations from the scene group to form a list of offline location recommendations; Step 6: The content generation module generates marketing content based on the big model.

2. The method for selecting a site for offline marketing activities based on social media data according to claim 1, characterized in that: In step 1, the address data collection module uses Internet crawler technology and API interface to obtain geographic information from online map services and collect business district distribution and passenger flow data; The sources of business district distribution and passenger flow data also include on-site sampling and reporting. Business personnel use GPS recording and real-time image uploading to conduct on-site sampling and reporting.

3. The method for selecting a site for offline marketing activities based on social media data according to claim 2, characterized in that: In step 2, natural language processing technology is used to analyze comment sentiment and topic trends.

4. The method for selecting a site for offline marketing activities based on social media data according to claim 3, characterized in that: In step 3, the data analysis module processes the geographical location information and integrates multiple data from the collected point data to form point portrait data; Clustering algorithms are used to cluster the feature items of the point data, and the points are formed into several category groups based on their geographical locations and feature portraits, and the points that have been successfully promoted in the past are expanded into type groups.

5. The method for selecting a site for offline marketing activities based on social media data according to claim 4, characterized in that: In step 3, K-means clustering algorithm and PCA dimensionality reduction are used to update and optimize the population model every hour.

6. The method for selecting a site for offline marketing activities based on social media data according to claim 5, characterized in that: In step 4, NLP and OCR technologies are used to identify social media data related to the brand. The various feature items of the brand portrait are constructed based on the identification results. The neural network model is trained through PyTorch, and the consumer portrait is updated every hour.

7. The method for selecting a site for offline marketing activities based on social media data according to claim 6, characterized in that: In step 6, a marketing campaign plan is designed based on the point portrait data formed in step 3 and the brand portrait data generated in step 4, and a marketing plan generation model is used to automatically generate suitable visual materials and marketing copy.

8. An offline marketing activity site selection system based on social media data, characterized by: A method for offline marketing activity site selection based on social media data is used to implement claim 7, the offline marketing activity site selection system includes an address data collection module, a brand data collection module, a data analysis module, an offline site selection recommendation module and a content generation module, the address data collection module and the brand data collection module are respectively communicated with the data analysis module, the data analysis module is communicated with the offline site selection recommendation module, the data analysis module and the offline site selection recommendation module are respectively communicated with the content generation module, and the content generation module automatically generates adaptive visual materials and marketing copy based on a large model generated by a marketing plan.

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