The RealGreen fields that make the best automation triggers

The best automation triggers in RealGreen are seven ordinary fields your office and techs already maintain every day: customer status, program and service codes, condition codes, marketing source codes, invoice aging, visit records, and cancel reasons. That is the whole answer; nothing exotic is hiding in the system. What separates companies that automate well from companies that give up is knowing what each field actually means in practice, how much to trust it, and which plays it unlocks. We work inside these databases daily, and this is the dictionary we hand new team members.

Customer status: the master switch

Status (active, inactive, canceled, and your shop's custom variants) is the field every other automation must check first. It decides whether someone is a customer at all.

Reliability: high, with one caveat. Offices are disciplined about statuses because billing depends on them. The caveat is custom statuses invented over the years (hold, seasonal, do-not-service) whose meanings drifted. Before automating, get the office to write one sentence defining each status in use. Every status change is a trigger: active-to-canceled starts the win-back clock, new-to-active starts onboarding, anything-to-hold pauses marketing.

Program and service codes: what they buy

Program enrollment tells you what each household buys, which means it also tells you what they do not buy. That negative space is the trigger for every cross-sell: fert-but-no-aeration, lawn-but-no-perimeter-pest, program-but-no-tree-and-shrub.

Reliability: high for billing, messy for marketing. The codes are accurate because invoices depend on them, but most RealGreen databases accumulate dozens of near-duplicate codes over the years. You will need a mapping table that rolls raw codes up to a short list of marketable program families before white-space logic works. Build it once; it becomes the backbone of your segment definitions.

Are condition codes really trustworthy enough to sell from?

Condition codes are observations techs log on a visit: grubs, patchy turf, weeds in beds, vole damage. They are the closest thing you have to a lead generated on the customer's own lawn.

Reliability: medium, and it depends entirely on your techs. Some crews flag conscientiously; others log nothing for weeks. Two rules make the field usable anyway. First, treat a code as a signal to quote, not a diagnosis to bill. Second, pay attention to flag-rate by tech: if one route logs 9% of visits and another logs zero, that is a coaching conversation, not a data problem. One illustrative play, plug in your own numbers: Trigger: tech logs a grub code. Action: a priced quote texts the customer within the hour, follow-up until answered. Math: 4,800 visits at a 5% flag rate is 240 quotes; at 20% close and a $290 ticket that is about $13,900 a season from a field techs fill in anyway. Full setup in our condition-code guide.

How reliable are marketing source codes?

Marketing source codes record what brought each customer in. They power spend decisions more than message decisions, but they trigger one underused play: source-specific onboarding. A door-knock sale and an LSA caller start the relationship with different expectations and deserve slightly different first-90-day messaging.

Reliability: low to medium, honestly. Source codes are only as good as the person keying the sale, and busy offices default to whatever is first in the dropdown. If a third of your customers are coded as other or unknown, fix the intake habit before trusting the reports; our lead-source tracking post is the repair manual.

Invoice aging: the suppression field

Aging buckets (current, 30, 60, 90+) drive two opposite automations at once. Positively: each aging threshold advances the dunning ladder, with a pay link in every message. Negatively, and just as important: an open balance suppresses every promotional message you would otherwise send. Dunning-while-upselling is the most embarrassing collision in this whole discipline.

Reliability: very high. This is billing data; it is the truest field in the system. If you automate nothing else from RealGreen, automate off aging.

What do visit records and cancel reasons unlock?

Visit records (scheduled, completed, skipped, with dates) are the heartbeat of operational messaging: pre-visit notifications the day before, summaries after completion, recovery sequences when a visit is skipped. They also feed the at-risk math: a household whose completed-visit count is falling versus last season is drifting toward a cancel you cannot see yet.

Reliability: high for completions, medium for skip reasons. Completion timestamps come from Mobile Live and are solid. Skip reasons are free-text-ish and inconsistent, so trigger on the skip itself, not on why.

Cancel reasons are the last field written and the first one everyone ignores. Their automation value is routing: a price cancel gets a value-focused win-back later, a service-quality cancel gets an apology and a manager call now, a moved cancel gets suppressed entirely. The field is only as honest as the office's discipline in capturing it; a dropdown of eight enforced choices beats a free-text box every time.

How do you actually get at these fields?

Everything above assumes you can read these fields somewhere other than a RealGreen screen. The API exposes them, but keys carry monthly call limits, so serious integrations replicate the data on a nightly sync and run automation logic against the copy; see our API guide for why. Once the replica exists, the ceiling comes off: we can automate pretty much anything from these seven fields, and connect RealGreen to pretty much any platform out there with an API, a Zapier connection, or a native connection. Every field in this dictionary is degraded by duplicates, dead addresses, and drifted codes, though, so budget a cleanup pass before you wire anything. The sync-and-automate layer is exactly what our RealGreen API integration was built to be, if you would rather not assemble it yourself.

The takeaway: automation quality is data literacy. Seven fields, each with a known meaning and a known failure mode: trust aging and status completely, trust programs after a mapping table, trust condition codes as leads rather than facts, and treat source codes and cancel reasons as fields you must first make honest.

Build your dictionary this month

1. List every status, program code, and cancel reason actually in use; have the office write one sentence per value.
2. Build the rollup table that maps raw service codes to five or six marketable program families.
3. Pull condition-code flag rates by tech and set a floor expectation with the field team.
4. Audit source-code completeness; retire the junk values and retrain intake.
5. Pick the one field you now trust most and ship a single automation on it before touching the rest.