Skene
Playbook

Route accounts to human touch using product signals

Create routing rules that use product signals to decide when sales or success should step in, instead of treating all engaged accounts the same.

Job-to-be-done: Decide when automation is no longer enough and human intervention actually helps.

Updated 2026-01-05

Problem context

In many PLG motions every engaged account looks the same: the product sends the same prompts and campaigns regardless of real potential or risk.

Sales and success teams either chase every active account or none of them, because there is no shared, product-based definition of when humans add leverage.

This leads to noisy outreach, missed expansion opportunities, and accounts that stall because no one intervenes when the product alone is not enough.

What breaks if this is not solved

  • High intention accounts never get timely human help, so they churn or buy a competitor after a promising start.
  • Sales and success burn time on low value accounts that only ever needed self-serve support, reducing capacity for the right accounts.
  • The organization loses trust in PLG signals because there is no clear connection between product behavior and revenue outcomes.

When this playbook applies

  • You have both self-serve and human motions, even if the human motion is small, and you want them to complement rather than fight each other.
  • You capture at least a minimal set of product usage signals such as activation events, plan limits, or collaboration behaviors.
  • Sales or success regularly complain that they hear about promising accounts too late or not at all.

System approach

Treat routing as a system: clear product signals, explicit thresholds, and playbooks for what happens when those thresholds are crossed.

Start from concrete examples of successful expansions and painful misses, and reverse engineer which product behaviors should have triggered human touch.

Define a small set of product-qualified lead and product-qualified expansion conditions, and wire them into your CRM and workflows.

Execution steps

  1. List your existing product signals: activation events, feature usage, plan limits, invitations, billing events, and support interactions.
  2. Analyze a sample of accounts that expanded successfully and those that churned after early engagement; identify behaviors that reliably precede each outcome.
  3. Propose a first version of routing rules, such as “PQL when activation + thresholded usage + collaboration” or “risk alert when usage collapses after activation”.
  4. Align product, sales, and success on these definitions; write them down in plain language and make them visible in your CRM or documentation.
  5. Implement the routing logic in your data layer or event processor so product signals automatically tag or score accounts in your CRM.
  6. Design specific human plays for each route, such as consultative onboarding for high potential accounts or light touch nudges for risk accounts.
  7. Start with conservative thresholds and a narrow segment so you do not overwhelm humans; monitor volume and outcomes for each routed path.
  8. Iterate monthly on the rules and thresholds based on conversion, win rates, and capacity feedback from the teams running the plays.

Metrics to watch

  • Conversion rate from routed PQLs to expansion or paid plans

    Trend up as routing rules and plays improve.

    Measure separately for accounts that received human touch versus those that stayed fully self-serve.

  • Time from qualifying product signal to first human touch

    Trend down toward a target window (for example, 24–72 hours).

    Long delays after a strong product signal erode intent and can make outreach feel random.

  • Share of human capacity spent on high potential accounts

    Increase the proportion of time spent on routed PQLs versus unqualified accounts.

    Track as a rough split in your CRM or task system so you can see if routing is focusing the team.

  • Churn or shrinkage among accounts that met strong-product-signal thresholds but never received human touch

    Trend down as routing improves.

    This is the “silent miss” rate; it should fall over time as routing and plays mature.

Failure modes

  • Defining routing rules that are too broad, overwhelming sales and success with noisy PQLs that do not actually convert.
  • Relying on vanity signals such as simple login counts instead of behaviors that correlate with durable value and expansion.
  • Designing routing rules in a spreadsheet without involving the humans who will run the follow up plays.
  • Letting routing decay as the product changes, so new features and motions never emit the right signals.

Related concepts