Signal Intelligence

Signal Intelligence is the capture, interpretation, scoring, and routing of individual behavioral, sentiment, and commercial signals across the customer lifecycle. It is the layer beneath customer intelligence: signals are the inputs, customer intelligence is the synthesis. Without strong signal intelligence, every downstream decision (health score, churn prediction, expansion flag, advocacy invitation) runs on bad inputs.

Why Signals Are the Foundation

B2B SaaS customer motions run on signals. A login. A feature opened. A community post. A support ticket opened with a specific tone. A review published. A champion leaving the company. Each signal, on its own, is usually not enough to trigger a decision. Combined into the right composite score at the right threshold, they predict outcomes more reliably than any single metric.

Companies that instrument signal capture well see the payoff directly in retention and expansion. Benchmarkit's research on health-scoring implementations shows 6 to 12 points of NRR lift in mid-market SaaS, and signal quality is the largest input into score quality. Garbage signals produce garbage scores, no matter how sophisticated the model on top.

What Signal Intelligence Captures

• Product signals: logins, feature events, session depth, usage breadth, first-value events, power-user behavior, dormancy.
• Engagement signals: email opens and clicks, community posts and replies, webinar attendance, event participation, content consumption.
• Sentiment signals: NPS, CSAT, review content, support ticket tone, sales call sentiment, community post sentiment.
• Relational signals: QBR attendance, executive touchpoints, champion changes, stakeholder map shifts.
• Commercial signals: renewal proximity, payment history, contract amendments, expansion pipeline activity.
• Intent and external signals: research activity on third-party sites, competitive content consumption, analyst report engagement.

Where Signal Programs Underperform

• Too many signals. Capturing 200 distinct signals without prioritization buries the ones that matter. Start with 20 high-signal inputs per segment, and add more only when the model demonstrably needs them.
• No normalization. Signals from different sources arrive in different shapes and scales. Without normalization, the score becomes dominated by whichever system produces the most events, not by which signals matter.
• Noise treated as signal. A marketing email auto-opened by a spam filter is not engagement. Systems that do not filter noise produce inflated engagement scores and poor predictions.
• No write-back to source systems. Signal intelligence that stays in one layer does not help CS, marketing, or sales unless it reaches their tools. Insight has to travel.

How Base Runs Signal Intelligence

Base captures, normalizes, and scores every signal across product, marketing, CS, support, community, and commercial surfaces, and routes prioritized insights into the tools where marketing, CS, and sales already work. Segment-specific thresholds prevent the one-size problem. Noise filtering keeps the signal clean. The signal layer is not a separate dashboard, it is the underlying fabric every customer decision draws from.