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PATTERN Cited by 1 source

Proactive customer outreach on elevated error rate

The pattern

Reach out to affected or at-risk customers before they contact you when your managed-service observability shows elevated error rates — even when customer-facing availability remains within SLO. The outreach serves three purposes: (a) validate your own diagnosis against customer observation; (b) demonstrate support during an upstream stressor; (c) surface any corner-case impact your observability might have missed.

Canonical instance (Source: sources/2025-06-20-redpanda-behind-the-scenes-redpanda-clouds-response-to-the-gcp-outage):

"We proactively started reaching out to customers with the highest tiered storage error rates to ensure we were not missing anything, and also to show our support, as is customary. We fully manage these BYOC clusters on behalf of our customers and have complete visibility — we know the answers to the questions, but we ask anyway. These are complex systems, after all."

The 2025-06-12 GCP-outage outreach happened at 20:56 UTC — 30 minutes after delayed alert notifications arrived at 20:26, and ~2 hours 15 minutes into the incident window.

Why reach out proactively

  1. Signal validation. Your observability may miss customer- layer impact you can't see (their application logic around the stream, their end-user experience). Customer contact is a ground-truth check.
  2. Trust building. Customers who see you monitoring their infrastructure through an upstream outage trust your managed service more — the moment is a service-quality disclosure event. Converts operational competence into visible competence.
  3. Support over supervision. The "show our support" framing matters: during an outage the customer is distressed. A proactive "we see what you're seeing, we've got this" call is materially different from a silent-until-ticket posture.
  4. Corner-case discovery. The butterfly effect means your system may be affected in ways your monitoring didn't model. Customer contact sometimes surfaces "actually our pipeline broke at X" where X wasn't in your alert set.
  5. Sales + renewal compounding. Proactive outreach on incidents materially improves account health — the customer's on-call engineer remembers who reached out first.

The load-bearing qualifier

The pattern only applies if you have observability into the customer's infrastructure (managed service, BYOC with metrics forwarding, hosted product). For purely self-service platforms where you see nothing beyond your own service boundaries, proactive outreach is either intrusive or impossible.

Redpanda's canonical phrasing: "We fully manage these BYOC clusters on behalf of our customers and have complete visibility — we know the answers to the questions, but we ask anyway." The visibility is what makes the outreach supportive rather than invasive.

Trigger criteria

Not every blip warrants proactive outreach. Common triggers:

  • Highest-N error rate customers during an incident — triage customer priority by impact magnitude.
  • Customers in the affected region / cloud / service tier.
  • Customers with known-critical dependencies on the affected substrate (e.g. for Redpanda, customers heavily using tiered storage during an object-store outage).
  • Customers who explicitly opted-in to proactive notifications (some orgs offer it as a premium contract feature).
  • High-profile accounts. Named enterprise customers get outreach at a lower signal threshold; self-service tier may get a status-page update only.

The outreach script

The Redpanda post is brief, but the implicit script is:

  1. Acknowledge the upstream event ("there's a GCP outage happening").
  2. Disclose what you're seeing ("we're seeing elevated error rates on tiered storage for your cluster").
  3. State current posture ("no impact on your write path / primary data, we're monitoring").
  4. Ask if they're seeing anything different — the signal-validation step.
  5. Commit to updates ("we'll notify when GCP resolves / when we're out of incident").

The ask-anyway step is critical: it's the ground-truth validation against the customer's app-layer observability.

Variants

  • Customer-prioritised outreach — pre-ranked customer list by business priority, tier, or contract SLA.
  • Incident-specific outreach only — reach out only during active incidents, not for routine elevated-error windows.
  • Post-incident summary outreach — follow up after the event with an incident report even if no active customer contact was needed.
  • Status-page + direct-message combination — status page for all customers, direct message for the impacted subset.

Anti-patterns

  • Outreach without observation. Reaching out without knowing what the customer is seeing turns into a "do-you-have-a-problem" phishing call that wastes both parties' time.
  • Outreach during wrong incidents. Reaching out for a low-impact GCP regional hiccup spams customers and teaches them to ignore future contact.
  • Boilerplate-only outreach. Templated "we're monitoring a GCP issue" messages without customer-specific data read as noise.
  • Over-promise on response. Saying "we'll keep you updated" and then going silent is worse than not reaching out.
  • Customer-facing escalation without internal coordination. Different engineers reaching out to the same customer with different messages damages trust.

Caveats

  • Not scalable to self-service tiers. For a large fleet of small customers, proactive outreach is economically infeasible; the pattern typically applies to the managed / enterprise tier.
  • Customer-side contact-discipline matters. Some customers prefer status-page updates over phone calls; the outreach channel should match customer preference.
  • Pattern requires managed-service observability. For self-hosted products where the vendor has no visibility, this pattern is infeasible.
  • The Redpanda post frames it as customary — implying industry-normal managed-service discipline — but the post doesn't disclose frequency or customer-satisfaction metrics.
  • Overuse can desensitise customers. Reaching out for every hiccup trains customers to filter outreach as noise. The Redpanda post's threshold ("highest tiered storage error rates") is clearly a selected subset, not the whole fleet.

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