1. Use the property address you want screened.
Start with the installation address. A homeowner’s mailing address, office address, or post-office box may not describe the roof you are evaluating. Include the street number and street name, city, state, and ZIP code. Retain unit and building identifiers when they are part of the source address.
Use either a complete address in one column or separate address fields. For example, headers named lead_id, address, city, state, zip, and campaign give you a simple structure. If the address field already contains the full location, do not also map city and state fields that would repeat the same text.
2. Keep a stable lead ID and useful original columns.
A record ID lets you reconnect exported analysis with the right lead, even when two contacts share a property. Keep the ID your source system already uses. Include campaign or assignment details when they help the person acting on the results.
LeadAnalyze preserves original columns in the CSV export. It does not create a direct connection to your CRM. Plan the return step before uploading: know which field your existing system uses to match records, and make sure that field remains intact from the source file to the exported results.
3. Protect ZIP codes and address formatting.
Treat ZIP codes as text in your spreadsheet so leading zeros remain present. Check house-number suffixes and apartment identifiers instead of stripping them during cleanup. An aggressive find-and-replace can turn different addresses into what looks like one property.
Use a single header row with a distinct name for each column. Remove report titles, merged-cell headings, notes above the headers, and subtotal rows. Export the worksheet as CSV using your spreadsheet’s export function so commas and quotation marks inside values are handled correctly. Check the saved file, not only the original workbook.
4. Size the batch around unique properties.
Each batch can contain up to 1,000 unique properties, and the uploaded file must be no larger than 10 MB. Divide a larger list into named groups that your team can recognize, such as territory or campaign. Keep the source IDs consistent across those files.
A row count is not necessarily a property count. Several contacts can share the same address, and some rows can lack an address. Repeated addresses within one batch are grouped for analysis while original rows remain available in the export. Review the calculated unique count before deciding how many credits the list requires.
5. Check the import review before starting.
Upload the file and inspect the automatically selected address columns. Open Customize columns if a field is wrong or missing. The review screen recalculates the unique properties and credit reservation when the mapping changes.
- Confirm the address column contains the property’s street address or its complete address.
- Confirm separate city, state, and ZIP fields point to the correct columns.
- Compare unique and duplicate counts with what you expect from the source list.
- Resolve missing addresses in the source when you want those rows analyzed.
- Check the credits to reserve and the available balance before starting.
6. Verify the export before updating your source system.
After processing, export the results and inspect a few records from each outcome. Match them by lead ID, check that original details are preserved, and read the reason alongside the screening label. Keep technical failures separate from completed poor-fit or review results.
For a small example, imagine 12 source rows: nine unique addresses, two additional rows repeating those addresses, and one row missing its address. The import would have nine properties to analyze and reserve nine credits. The duplicate rows share the relevant result, and the missing-address row remains traceable in the export. If one analysis fails technically, its credit is released.
Test the update process on a small selection before applying an export to a large source list. Preserve the screening date and outcome in fields that will not overwrite your team’s existing customer notes. A property result should add useful context to the record.