A Signal Is Not Binary
The difference between an event and a signal
An event is a raw fact: “Company X posted a job listing.” An intent signal is that event interpreted, filtered, and contextualized for your specific offering: “Company X, an SMB with 150 employees in the consulting sector in the greater Paris area, posted 4 senior developer positions at once, which suggests rapid growth that could require a project management tool.”
Between the raw event and the actionable signal, there are 222 configuration parameters.
Why so many parameters?
Take a simple example: the “hiring” signal. Without configuration, it catches every job listing posted by every company worldwide. That’s unusable. With configuration:
- How many hires? One hire doesn’t tell you much. Five simultaneous hires point to rapid growth. The threshold is a parameter.
- What type of role? An intern hire doesn’t carry the same meaning as a CTO hire. The seniority filter is a parameter.
- Which department? A marketing hire is irrelevant to an accounting software vendor. The department filter is a parameter.
- What company size? Hiring 5 people at a 50-person company (+10% headcount) means something very different from 5 hires at a 5,000-person enterprise (+0.1%). The size filter is a parameter.
- Which industry? The same hire has different value depending on whether the company operates in your target sector. The industry code filter is a parameter.
- Which geographic area? A hire in London is irrelevant to a local provider based in Manchester. The geographic filter is a parameter.
Multiply these dimensions across 100+ signal types and you get to 222 possible configurations.
Parameter Categories
Trigger parameters
These parameters define the threshold at which an event becomes a signal:
- Volume: minimum number of hires, minimum funding amount, minimum headcount growth rate
- Timing: reference period (growth over 3 months vs. 6 months), detection frequency
- Intensity: distinction between incremental change and a disruption (new CEO vs. new manager)
Filtering parameters
These parameters define the scope within which the signal is detected:
- Industry: industry codes, activity keywords, sector exclusions
- Company size: headcount (min/max), revenue, category (small business, SMB, mid-market, enterprise)
- Geography: departments, regions, countries, distance from a point
- Tech stack: technologies mentioned in job postings or on the company website
- History: first detection vs. recurring signal, company already contacted or not
Scoring parameters
These parameters feed the Balance model:
- Signal weight: value assigned to each signal type based on its relevance to your offering
- Recency coefficient: decay rate after 48 hours
- Tier thresholds: minimum score for Tier 1, Tier 2, Tier 3
- Combinations: bonus when multiple signals converge on the same company
Contextualization parameters
These parameters enrich the signal before delivery:
- Enrichment: Deep Search level (basic, standard, in-depth)
- Contact: type of decision-maker to target (CEO, CTO, HR Director, CFO)
- Channel: recommended outreach channel (email, LinkedIn, phone)
- Template: suggested message template based on signal type
Concrete Configuration Examples
Configuration: “SaaS vendor targeting post-funding scale-ups”
| Parameter | Value |
|---|---|
| Signal | Funding round |
| Minimum amount | €2M |
| Industry | Tech, SaaS, Digital |
| Headcount | 20-500 |
| Geography | France + Belgium + Switzerland |
| Contact | CTO / VP Engineering |
| Scoring | 10/10 |
| Tier | 1 (ABM) |
Configuration: “Generalist recruitment agency”
| Parameter | Value |
|---|---|
| Signal | Multiple hires |
| Threshold | 5+ simultaneous postings |
| Industry | All (excl. temp staffing) |
| Headcount | 50-2,000 |
| Geography | Greater Paris area |
| Contact | HR Director / Talent Manager |
| Scoring | 9/10 |
| Tier | 1 or 2 depending on volume |
Configuration: “Insurer targeting growing companies”
| Parameter | Value |
|---|---|
| Signal | Headcount growth |
| Threshold | +20% over 6 months |
| Industry | All |
| Headcount | 50-500 |
| Geography | Metropolitan France |
| Contact | CFO / HR Director |
| Scoring | 8/10 |
| Tier | 2 |
Configuration: “The competitor-connections signal, simple input, complex engine”
This one deserves a closer look, because the gap between what the user sees and what runs underneath is the widest of any signal in the catalog.
From the user’s side, the configuration looks almost trivial: paste a LinkedIn company URL, set a minimum daily connection delta, pick a target seniority. Done. Most people assume that’s also what happens on the back end. It isn’t.
When that URL lands in the system, Rodz runs a PeopleSearch scrape against the company’s LinkedIn profile to retrieve its current employee list. That list is scored for relevance: not every employee qualifies as an active prospect, so the scoring layer filters by profile activity, connection recency, and seniority match. A delta check then runs daily, comparing today’s connection count against the prior snapshot to surface only the accounts where meaningful new connections are forming. The result is a signal that fires only when a competitor’s team is actually expanding its network in your direction, not simply when the company exists.
The user input is one URL. The internal process involves scraping, identity resolution, relevance scoring, and a daily differential calculation. That’s four distinct operations behind a single configuration field.
| Parameter | Value |
|---|---|
| Signal | Competitor connections |
| Input | LinkedIn company URL |
| Internal ops | PeopleSearch scrape + relevance scoring + daily delta |
| Threshold | Minimum daily new connections (configurable) |
| Target seniority | Decision-maker roles (e.g. VP Sales, Head of Growth) |
| Scoring | 7-9/10 depending on delta volume |
| Tier | 1 or 2 |
Why does this matter for the 222-parameter argument? Because 99% of users want to simply paste a URL and get results. That instinct is correct. But the signal only stays useful if the complexity underneath is handled precisely: who at that company is adding connections, at what rate, and whether those new connections match your ideal contact profile. Rodz knows, across its monitored accounts, which profiles have the most consistent connection activity and which ones are effectively dormant. A raw connection count would miss that entirely. The configuration the user sees is simple; the data work behind it is not.
The Creation Phase: Configuring with Method
Configuring the 222 parameters isn’t done at random. It’s the first phase of the Rodz framework: Creation.
- Offering audit: understand precisely what you sell and what need you solve
- Existing client analysis: identify the signals that preceded purchases from your best clients
- Initial configuration: set up 3 to 5 signal configurations covering the most frequent use cases
- Testing: run detection on a limited scope for 4 to 6 weeks
- Validation: process at least 274 prospects per configuration before drawing conclusions on effectiveness
The Rodz interface abstracts most of that complexity and walks you through configuration step by step. The Rodz team’s expertise supports clients through the decisions that actually matter. For a hands-on walkthrough, see the tutorial on configuring your first signal via the API and the complete Rodz API reference for full details on endpoints and available parameters.
For deeper context on how intent signals are defined and used in practice, Lincoln Murphy’s work on customer success and timing and the Forrester research on buyer intent data both provide useful grounding.
Frequently Asked Questions
Do I need to configure all 222 parameters myself?
No. The Rodz team guides each client through the Creation phase. Default parameters cover 80% of needs. The remaining 20% get adjusted based on your offering, your market, and your initial results. The initial configuration takes 1 to 2 weeks.
Can configurations be modified along the way?
Yes, and it’s worth doing. The Capitalization phase is specifically about analyzing results by configuration and adjusting parameters. A signal that doesn’t generate positive responses after 274 prospects can be reconfigured or replaced.
Why 222 and not 50 or 500?
The 222 parameters cover every necessary dimension without redundancy. Each one has a measurable impact on signal relevance. Below that number, detection would be too coarse. Above it, the added complexity wouldn’t deliver meaningful gains.
The competitor-connections signal looks simple. Is it?
The input is simple by design. The detection work behind it is not. What the user configures in one field triggers a multi-step process: profile scraping, relevance scoring, and a daily differential calculation. That hidden complexity is exactly why a raw LinkedIn follower count or a manual check would give you the wrong answer most of the time.