How do I keep Fit Scores current as data changes?

Keep Fit Scores current by recomputing them on a schedule and whenever the underlying fields change, and by keeping a history of every change, so the score reflects the account as it is now, not as it was at import. A stale score quietly sends reps to the wrong accounts.

This guide is about keeping the score fresh. For keeping the underlying enrichment data fresh, which is a different job, see how do I keep enrichment data fresh.

Why do scores go stale?

Healthcare accounts change constantly. Groups get acquired, practices add locations, technology gets swapped, and enrichment data is refreshed. Each of those can move an account across a threshold, so a score frozen at import time misses all of it.

Are data freshness and score freshness the same thing?

No. Enrichment freshness is about the inputs being accurate. Score freshness is about the score being recomputed after those inputs move. You can have perfectly fresh data and a stale score if nothing recomputes it, which is why the two are handled separately.

How often should I recompute?

Recompute on a regular cadence and immediately when a key field changes. The point is that a rep opening an account today sees a score that reflects today's data. SignalRx pulls external public changes in alongside your CRM, resolves ownership so a physician group or MSO still reads as one account, writes the updated fields onto every account and contact in Salesforce, your system of record, and refeeds the Fit Score, both on schedule and on change. What keeps the scores honest is that they combine that external public context with your own internal signals, your revenue, support, and product-usage data tracked over time, with inference deciding which source to trust when they disagree, so the refreshed scores and Tags keep driving the right Revenue Plays and Salesforce lists.

Why keep score history?

History turns scoring into something you can report on, which accounts rose, which fell, and how the population is shifting. That history powers movement dashboards, covered in seeing which customers are slipping.

What does this look like on one account?

Illustrative, based on how SignalRx customers work. Grandview Surgical Partners was a physician-owned group, 4 locations, specialty general surgery, scored Moderate Fit at 61 with a health tier of Maintain. A private-equity acquisition adds 7 locations. On the next recompute the ownership type and location count change, the score moves to 82, and the tier flips to High Fit, with the prior value retained in history so the jump is visible on the movement dashboard.

Key takeaways

• Recompute on a schedule and on change, not once at import.
• Data freshness and score freshness are separate jobs.
• Keep score history so you can report on movement.

See it in action: Close Better Leads with SignalRx.

Related: How do I keep enrichment data fresh? and How do I see which customers are slipping?