Skip to main content

Potential Addons

Website​

Maps​

Integrated maps to provide users with the property's exact location and the surrounding neighborhood.

User Profile Expansion​

  1. Saved Searches: Users can save their customized search criteria for future reference, allowing them to quickly revisit their preferred property preferences without having to re-enter the criteria each time.

  2. Search History: The website will maintain a search history, enabling users to easily track their previous searches and revisit properties they have viewed in the past.

  3. Search Alerts: Users can opt-in to receive email alerts when new properties matching their saved search criteria become available.

CRM​

Custom MLS Browsing Site Dashboard​

Overview​

The Custom MLS Browsing Site Dashboard provides a comprehensive overview of user interactions, lead generation, and website performance metrics. This dashboard is designed to empower relators and website administrators to gain valuable insights into user behavior, identify trends in property searches, and optimize the website's effectiveness.

Data Visualization​

The dashboard utilizes a variety of data visualization techniques to present information in a clear and actionable manner. These visualizations include:

  • Line Charts: Tracking trends in page views, user interactions, and lead generation over time.
  • Bar Charts: Comparing performance across different property types, neighborhoods, and search criteria.
  • Pie Charts: Illustrating the distribution of user demographics, property preferences, and lead conversion rates.
  • Heatmaps: Visualizing user engagement patterns across the website's pages and features.

Key Metrics​

The dashboard focuses on several key metrics that are crucial for understanding user behavior and website performance:

  • Page Views: The total number of times each page on the website is visited.
  • Click-Through Rates: The percentage of users who click on specific links or buttons.
  • Scroll Depth: How far down a page users scroll before exiting.
  • Time on Page: The average amount of time users spend on each page.
  • User Actions: Specific actions performed on pages, such as submitting forms, filtering listings, or clicking on suggested properties.
  • Lead Generation: The number of prospective buyers identified through website interactions.
  • Lead Conversion Rates: The percentage of leads that convert into actual clients.

Filters and Segmentation​

The dashboard provides filters and segmentation options to allow users to analyze data from specific perspectives. These filters may include:

  • Time Range: Selecting a specific date range to view data trends over time.
  • Property Type: Segmenting data by property type (single-family homes, condominiums, apartments, etc.).
  • Neighborhood: Filtering data to specific neighborhoods or areas of interest.
  • User Demographics: Analyzing data based on user demographics (age, location, family size, etc.).
  • Lead Source: Identifying the source of leads (website interactions, email campaigns, referrals, etc.).

Actionable Insights​

The dashboard's data visualizations and filters empower users to gain actionable insights that can be used to improve the website's effectiveness. These insights may include:

  • Identifying popular pages and features to optimize user experience.
  • Understanding user preferences and search patterns to refine property suggestions.
  • Evaluating the effectiveness of follow-up campaigns and lead nurturing strategies.
  • Tracking lead conversion rates to identify areas for improvement in the sales process.

Automation​

Tailored Properties Campaign​

Proactively suggesting relevant properties can help relators connect potential buyers with their ideal homes. By analyzing MLS data and user preferences, the website can deliver personalized property recommendations.

1. Regular MLS Data Analysis

The website will periodically extract and analyze MLS data to identify new and relevant listings. This analysis may involve:

  • Price Range Matching: Identifying properties within the price range specified by users in their saved searches.

  • Property Feature Matching: Identifying properties that match the features and amenities users have shown interest in.

  • Location Matching: Identifying properties located in the neighborhoods or areas users have explored on the website.

By continuously analyzing MLS data, the website can ensure that the suggested properties campaign remains up-to-date and relevant to user preferences.

2. Personalized Property Recommendations

Based on the analyzed MLS data and user preferences, the website will develop personalized property recommendations. These recommendations may consider:

  • Recent Property Views: Properties similar to those viewed by the user in the past.

  • Saved Search Criteria: Properties that align with the criteria specified in the user's saved searches.

  • User Demographics: Properties that match the user's family size, lifestyle, or investment goals.

By delivering personalized recommendations, the website can increase the likelihood of capturing the user's interest and potentially converting them into a client.

3. Automated Email Delivery

The website will implement automation to periodically deliver personalized property recommendations via email. This automation may involve:

  • Frequency Scheduling: Sending emails at regular intervals, such as weekly or bi-weekly, to keep users informed of new listings.

  • Dynamic Content: Generating email content that dynamically adapts to the user's preferences and recent property views.

  • Performance Tracking: Monitoring the open rates and click-through rates of the suggested properties emails to refine the campaign's effectiveness.

By automating the delivery of personalized property recommendations, relators can consistently engage with prospective buyers and encourage them to explore new listings that align with their needs and preferences.