Spanish area codes and user profiles: geographic attribute matching and number filtering optimization techniques

I. The Regional Attributes of Spanish Area Codes: A Core Anchor Point for User Profiling

(I) The coding patterns and regional associations of Spanish area codes

1. The length of the code is linked to the economic level: Spanish area codes follow the core rule that "the more developed the economy, the shorter the code". Two-digit area codes mainly cover core economic cities such as Madrid and Barcelona. These areas have active economies and strong consumption power. Three-digit area codes correspond to small and medium-sized cities and remote areas such as Seville and Valencia. The economic development level is relatively moderate and consumption is more pragmatic.
2. The coding range corresponds to the administrative region: Each region and province in Spain has its own exclusive area code range. For example, the core area code of the Madrid Autonomous Community is 91, the core area code of the Catalonia Region is 93, and the area code of the Andalusia Region starts with 95. By using the Spanish area code, you can directly locate the administrative region to which the user belongs, providing a direct basis for regional attribute positioning.
3. Number type and demographic characteristics: Spanish mobile numbers start with 6 or 7 and do not require a domestic area code. These numbers are mainly used by young people and mobile populations. Fixed numbers require a corresponding area code and are mostly used by local residents and business owners. This provides a convenient way to initially distinguish demographic characteristics.

(II) Core Regional Attributes Implied by Spanish Area Codes

1. Economic attributes: Areas with 2-digit area codes (91, 93, etc.) are economically developed with high per capita income, corresponding to high consumption capacity; small and medium-sized cities with 3-digit area codes (954, 963, etc.) have moderate economic levels, corresponding to moderate consumption capacity and a preference for cost-effectiveness; remote areas with 3-digit area codes have weak economies, corresponding to basic consumption and a demand-driven nature.
2. Cultural attributes: The cultural customs of different areas vary significantly. For example, Catalonia (area code 93, etc.) emphasizes the inheritance of local culture and has a high acceptance of cultural and creative products and folk products; Madrid (area code 91), as an international city, has a diverse cultural integration and a strong demand for high-end fashion and business services; and the southern Andalusia region (area code 95, etc.) has a strong folk culture and prefers traditional consumption scenarios.
3. Consumption scenario attributes: Core cities (area codes 91 and 93) have rich business consumption, fashion consumption, and cultural tourism consumption scenarios; small and medium-sized cities (area codes 954 and 963, etc.) focus on local life consumption and family consumption; remote areas have simple consumption scenarios, mainly daily necessities consumption. The differences in scenario attributes provide a clear direction for profile construction.
   
   

II. The Logic and Methods of Precise Matching Spanish Area Codes with User Profiles

(a) Basic Anchoring: Using the Spanish area code as the core to lock in the basic features of the profile

1. Establish a map of area codes and their corresponding geographical attributes: Organize all area codes in Spain and create a standardized mapping table based on "Spanish area code - region - economic attribute - cultural attribute - consumption scenario". For example, 91 corresponds to Madrid/high spending power/multiculturalism/business and fashion consumption; 93 corresponds to Barcelona/high spending power/local cultural and creative industries/cultural tourism and fashion consumption; and 954 corresponds to Seville/medium spending power/folk culture/local lifestyle consumption.
2. Extracting area codes to complete basic user profile anchoring: Spanish area codes are extracted from user-registered phone numbers and precisely matched with a mapping table to quickly assign basic geographic attribute tags to users. For example, if a user's number contains the area code 91, the basic profile characteristics of "high spending power, business/fashion-oriented" can be initially anchored; if the number contains the area code 954, the characteristics of "medium spending power, folk/local lifestyle-oriented" can be anchored.
3. Refine basic characteristics by combining number type: differentiate between the profiles of landline and mobile phones, add "local resident, enterprise/family user" characteristics to landline users, and add "young people, mobile attributes, online activity" characteristics to mobile phone users, further refining the basic profile.

(II) Advanced Matching: Multi-dimensional Data Supplementation to Improve Profile Accuracy

1. Behavioral data supplements user profile features: By combining user browsing history, purchase history, and inquiry content, more detailed user profiles can be created. For example, if mobile users with area code 91 frequently browse high-end business packages, the feature "Madrid - High-end business people - Corporate white-collar workers - High average order value demand" can be added; if regular users with area code 93 frequently inquire about cultural and tourism routes, the feature "Barcelona - Cultural and tourism enthusiasts - Mid-range consumption - Local experience demand" can be added.
2. Enhancing User Profile Dimensions with Basic Attribute Data: Obtain basic information such as user age, gender, and occupation through compliant channels to enrich user profile dimensions. For example, for female users aged 30-45 with area code 954, add the characteristics "Seville - housewife - middle income - local life service needs"; for male users aged 25-35 with area code 963, add the characteristics "Valencia - young professional - essential consumption - online shopping preference".
3. Verifying the effectiveness of user profiles using regional activity data: The accuracy of user profiles is verified by combining user activity data from different area codes in Spain. For example, users with core area codes such as 91 and 93 have high online activity; if the matched profile feature is "online activity - high-frequency consumption," the profile is highly effective. Users with remote area codes who match the "high-frequency online consumption" feature require further verification and correction.
   
   

III. Number Optimization Techniques Based on Spanish Area Codes

(I) Basic Optimization: Precise Removal of Invalid Numbers Guided by Area Code

1. Area code format verification and removal of abnormal numbers: Based on the standard format of Spanish area codes, a format verification rule is established. Landline numbers must conform to the format "+34-2/3-digit area code-7/8-digit local number", and mobile phones must conform to the format "+34-10-digit number starting with 6/7". Abnormal numbers with missing area codes or incorrect formats are removed using regular expressions or data cleaning tools.
2. Filtering low-quality numbers based on regional activity: By combining regional activity data for different area codes in Spain, focus on filtering numbers from highly active area codes and eliminate low-quality numbers from inactive area codes. For example, some remote 3-digit area codes have a high probability of being disconnected or out of service. A filtering threshold can be set based on historical data to prioritize the elimination or verification of numbers from these areas.
3. Deduplication based on the "area code + number" combination: Deduplication is performed based on the unique combination of "Spanish area code + local number". Tools such as Excel and databases are used to remove duplicate number records, avoiding repeated contact that may cause user annoyance and reducing the waste of marketing resources.

(II) Advanced Optimization: Precise Number Filtering Guided by Profile Matching

1. Target Profile - Precise Area Code Matching: Based on a preset target user profile, filter numbers with corresponding Spanish area codes. For example, if the target profile is "high-end business people," then focus on filtering numbers with core area codes such as 91 (Madrid) and 93 (Barcelona); if the target profile is "people with local life service needs," then filter numbers with area codes of smaller cities such as 954 (Seville) and 963 (Valencia).
2. Linked filtering based on number type and user profile features: Optimize filtering by combining the correlation logic between number type and user profile features. For example, if the target profile is "young online consumers," then the focus is on filtering mobile numbers starting with 6 or 7; if the target profile is "local businesses," then the focus is on filtering landline numbers with the corresponding area code.
3. Standardize the format of numbers after screening: Standardize the format of the screened high-quality numbers to "+34-area code-local number", remove spaces, parentheses and other special characters, so as to facilitate CRM system entry, batch outreach and subsequent profile matching and verification, and improve the continuity of the operation process.