Choosing a Scrape.Email tier for a city property team

Before you compare tiers, settle the number that drives everything else: how many owner and developer contacts your team needs to reach each month. That figure, not the tier name, decides whether a lifetime level fits. A team working one city and a handful of postcodes needs far fewer contacts than a team covering several districts at once.

Scrape.Email filters by role, industry and location, so your list starts from your city rather than a national export. Public page and profile scraping then builds the candidate list, and contact enrichment and verification clean it before any campaign runs. The workspace holds saved leads, so a list can be reused and refined rather than rebuilt.

Because credits are used by successful work, your monthly contact target maps directly onto how much of a tier you will consume. Write that target down before you look at the tiers, and keep it beside the comparison. It turns a vague question about which level to choose into a plain volume check.

Three lifetime levels are published on softwareprogrammer.app: Prospector, Agency and Enterprise. Each is a one-off purchase rather than a subscription, which suits a property team that wants a fixed cost line. What separates them is capacity, so read each level against the monthly contact target you set, not against its label alone.

At every level you get the same core work: filters by role, industry and location, scraping of public pages and profiles, enrichment and verification, plus saved leads. The higher levels exist for teams that push more volume through those steps or need the API and webhooks to connect the workspace to their own systems.

For a single-city team, the practical question is whether Prospector covers your monthly list. If your team contacts owners and developers in bursts, such as around new instructions, note the peak month rather than the average. Size the tier against the busy month so you are not caught short mid-campaign.

The real worry is not price. It is whether any tool returns local detail rather than a national list with your city buried somewhere inside it. A property team lives on local knowledge, and a list that cannot be narrowed to the streets and roles you actually work is of little use, however large it looks.

Scrape.Email answers that through location filters combined with role and industry filters. You define the city, then the type of decision maker, and the scrape works from public pages rather than a bought national file. Saved leads let you keep the result and add to it, so local coverage builds up over time instead of resetting each month.

Test it on one district first. Run a narrow search, check how many usable contacts come back, and confirm they match the owners and developers you already know about. That small trial tells you more about local fit than any feature list, and it costs little of your allowance.

Moving up a level buys capacity, not a different product. The filters, scraping, enrichment, verification and saved leads work the same way at each tier. What changes is how much successful work you can run before you reach the ceiling, which matters most for a team with a steady outreach rhythm across several districts.

The higher levels also matter if you want the API and webhooks. A property team that pushes verified contacts into its own systems, or triggers work when a new lead is saved, will find that connection more useful than raw volume. Decide whether that integration is part of your plan before paying for a larger tier.

If your city list is small and steady, the entry level may cover you for a long time, and the next tier mainly buys headroom. Buy headroom only when you can name the extra contacts it will serve. Otherwise the larger purchase sits unused while your actual bottleneck stays elsewhere.