Lead Generation Database Construction

A customer database is an organized collection of interrelated data designed for easy access, management, and updating. In real estate, it’s a central repository of information about potential❓ buyers, sellers, or renters.

An effective database should have the following characteristics: comprehensiveness, accuracy, organization, scalability, and security.

The importance of the customer database is based on several scientific theories and principles, including Social Network Theory, Customer Lifetime Value❓❓ (CLTV), and the Pareto Principle (80/20 rule).

CLTV = (Average Transaction Value) × (Number of Transactions per Year) × (Average Customer Lifespan) - (Customer Acquisition Cost)

Sources of potential customers are both online (real estate websites, social media, search engines, paid advertising, and webinars) and traditional (referrals, personal networks, real estate exhibitions, direct mail, and flyers).

Data collection and organization include using data collection forms, obtaining consent, using CRM software (e.g., Salesforce, HubSpot, Zoho CRM), and segmenting leads based on criteria like location, budget, and needs.

Lead scoring helps to identify leads most likely to convert, using criteria like demographics (age, gender, income, occupation), behavior (website interaction, email opens, webinar attendance), and interests (property types, preferred locations, budget).

Lead Score = (w1 * x1) + (w2 * x2) + … + (wn * xn)
where:
wi = weight of criterion i
xi = value of criterion i for the lead

Data quality is continuously improved through regular auditing, removing duplicates, and integrating with external data sources like LinkedIn.

Effective management strategies involve regular and personalized communication (e.g., personalized emails, marketing automation, valuable content), relationship building (personal follow-ups, special events, excellent customer service), and data analysis to measure performance and improve (tracking key metrics like conversion rate and customer acquisition cost, analyzing data to identify trends, and adjusting strategies based on analysis results).

Conversion Rate = (Number of Actual Customers / Number of Potential Customers) × 100