Why “following signals” is important now

In fast-paced markets, static strategies become outdated quickly. Selin Kocalar argues that signals—repeated customer inquiries, specific channel demands, spikes in conversions—should guide decisions, rather than relying on past tactics. This mindset helped Delve evolve from early concepts into an AI-driven compliance platform used by numerous companies.

Selin’s journey in summary

Kocalar co-founded Delve in a dorm at MIT, later securing a $32M Series A with around a $300M valuation and serving 500+ rapidly growing clients. The key insight was that the market kept asking the same question about becoming HIPAA/SOC 2 compliant—a persistent signal that justified a definitive shift to compliance automation.

Signals versus playbooks (what’s different)

• Signals: observable patterns—such as the same buyer question appearing in calls, DMs, and comments; a channel that consistently books demos well; a segment that closes faster.

• Playbooks: inherited strategies that might not fit your audience or timing. Selin’s point: use playbooks as references, but allow signals to override them when data and momentum suggest it.

Specific signals highlighted by Selin

• Recurring customer pain across channels (the HIPAA/“how did you become compliant?” theme that ultimately drove Delve’s shift).

• Creative, high-impact go-to-market (innovative campaigns and fast experiments that exhibit measurable attraction, not vanity metrics).

• Operational results over opinions (e.g., demos booked and revenue outcomes overshadowing top-of-funnel noise).

Practical steps: operationalize signals in Notion (this week)

1. Create a “Signal Board” (Notion database) with fields for Signal type (customer inquiry, channel, segment), Source link, Frequency count, Severity, and Next action. Review weekly.

2. Establish a GTM experiment log with hypotheses, small budgets, and success criteria linked to demos booked—Delve considers demos the leading indicator.

3. Tag recurring issues across support tickets, sales notes, and social comments; elevate any signal that crosses a threshold (e.g., 10+ mentions/month) to a product/GTM test.

4. Conduct a weekly “Signals Stand-up”: decide on one build and one GTM test based on the strongest signal. Archive “nice ideas” unless supported by data.

5. Monitor the metric that matters (for many, demos booked). Integrate it into your dashboard and annotate with experiments for clear attribution.

Examples (inspired by Selin’s approach)

• Pivot validation: If 60% of incoming questions inquire about a compliance feature, launch a basic workflow and measure demo increase with that keyword.

• Channel adjustment: If a newsletter results in a demo rate that's 5× higher compared to paid social (same budget), shift funds for two weeks and re-evaluate.

• Message testing: Use the most frequently repeated customer wording as your headline; retain if demo conversion improves week-over-week.

The principle: Speed over perfection. Small, reversible experiments based on clear signals advance faster than extended planning against outdated strategies.

Outcomes to aim for

Selin defines progress by booked demos, time to insight, and cost per validated signal. Delve’s case study demonstrates how enhancing tracking transformed a two-person GTM team into a measurable growth engine—reducing manual reporting hours and clarifying what truly drives pipeline.

FAQs

What are “market signals”?
Patterns observed (e.g., repeated buyer pain, a channel that books more demos, faster closing rates) that indicate where to concentrate next. LinkedIn

Why can playbooks be less effective?
They’re generic and often lag reality; if your signals contradict the playbook, prioritize signals. YouTube

How do we implement this approach?
Track signals in a shared system (e.g., a Notion board), run small, time-boxed experiments, and tie success to a concrete metric like demos booked. Review weekly. HockeyStack

What’s the evidence it works?
Delve attributes progress to responding to repeated compliance pain, innovative GTM experiments, and rigorous tracking—culminating in a recent $32M Series A and a significant customer base. LinkedIn