Multichannel Signal Engine: 20% sales time saved, double-digit faster cycles

The problem:

To scale ABM, the client needed a tailored approach per account — but personalization requires signals:

• •company events and changes (funding, launches, leadership moves, expansions)
• •buying committee activity (posts, comments, hiring, public priorities)
• •engagement with the client's content (site visits, downloads, email activity, webinar interest)

In practice, every rep was expected to monitor ~30 accounts, with dozens of contacts per account. That is a full-time job by itself.

Their current approach:

• •manually check company news for each target account
• •manually scan contacts across social channels for relevant activity
• •manually look for engagement with company content and outbound touches
• •bring "weekly intel" into meetings to decide who to reach out to

The consequences:

• •~30% of rep effort went into monitoring and research instead of selling
• •Signal coverage was inconsistent (depends on the rep, the week, the time they had)
• •Weekly meetings existed just to patch the process — and outputs were still mediocre
• •Good timing was missed, so outreach was either late or generic

The goal wasn't "more data." The goal was: turn scattered signals into prioritized, actionable outreach moments.

Here's the path we took:

1. Build a multichannel monitoring layer (company + people + engagement)

We introduced a Signal Engine that continuously tracks:

• •company events (news, announcements, strategic moves)
• •social activity from buying committee members (posts, engagement, themes)
• •engagement signals with the client's content and outreach (touchpoints across channels)

Instead of reps hunting signals, signals come to reps.

2. Normalize signals into a single view (so it's usable)

Raw signals are noisy. We structured them into consistent categories like:

• •"strategic change"
• •"active initiative"
• •"hiring / expansion"
• •"stakeholder movement"
• •"engagement spikes"
• •"intent alignment" (signal matches known pain points/use cases)

This made the data readable and comparable across accounts.

3. Create a custom scoring methodology to prioritize accounts and contacts

We worked with the client to define a scoring model that:

• •ranks accounts by momentum (not just size)
• •highlights the strongest signals and what they imply
• •shortlists high-potential targets each week
• •shows movement over time (warming up vs cooling down)

Outcome: reps stop guessing where to focus.

4. Translate signals into "who / what / when" actions

For each surfaced opportunity, the engine makes it easy to act:

• •Who to contact (which stakeholder is most active / relevant)
• •What to lead with (the specific trigger and angle)
• •When to reach out (freshness + urgency + engagement windows)
• •Suggested outreach context so the message is grounded, not generic

This is what actually speeds up conversations — not the data itself.

5. Visualize everything in a simple dashboard (so adoption sticks)

Finally, we made it operational:

• •one dashboard for account lists, scores, and changes
• •alerts / digests for new high-intent signals
• •lightweight workflow that plugs into weekly cadence without extra meetings