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Lead quality signals - what makes a good lead

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Quality is slippery. Difficult to define. But you know it when you see it. A lead from organic search feels hot. A lead from a competitor ad feels cold. A lead that visited your pricing page three times feels ready. A lead that just downloaded your guide feels far away. These feelings are usually right. Turn them into signals.

What quality signals are

Lead quality signals are behavioral and characteristic clues that predict if a lead will convert. Strong signals are predictive. They show lead will likely buy. Weak signals are not. They are noise. Measure signals. Build quality on strong signals.

Most businesses guess at quality. Sales rep says this lead is hot. Another rep says this one is cold. Guessing is unreliable. Measuring is reliable. Track which signals predict conversion. Build on those signals.

Engagement signals

More engagement means more interest

Engagement shows how interested a lead is. Multiple page visits. Multiple form submissions. Long time on site. Watching videos. Reading whitepapers. Downloading resources. More engagement means more interest.

Set engagement thresholds

Set engagement thresholds. Three or more page visits. Engaged. One page visit. Not engaged. Opened email three times. Engaged. Never opened. Not engaged. Spending five minutes on your site. Engaged. Thirty seconds. Not engaged.

Different businesses have different engagement thresholds. A SaaS with complex product needs more engagement to show fit. A simple service needs less. Calibrate thresholds to your business.

Buying cycle signals

Early-cycle signals

Some behaviors indicate where they are in buying cycle. Early cycle signals: downloaded beginner guide. Visited product overview page. Downloaded comparison to competitors. These show early research.

Mid-cycle signals

Mid-cycle signals: Requested demo. Visited pricing page. Read case study. These show active evaluation.

Late-cycle signals

Late-cycle signals: Requested pricing. Scheduled a call. Asked technical question about implementation. These show near-decision stage.

Late-cycle signals are hottest. Lead is close to buying. Early-cycle signals are coldest. Lead just started research. Mid-cycle signals are in between. Treat them differently. Hot leads get immediate follow-up. Cold leads get nurture sequences. Mid-cycle gets medium urgency.

Fit signals

Demographic fit

Demographic fit. Right company size. Right industry. Right location. Right budget range. Behavioral fit. Using tools that integrate with yours. In a market segment you serve. Have problems you solve. Fit signals show if lead is a potential customer.

Intent data

Use intent data. Do they search for your keywords. Do they view your competitor websites. Do they read about your product category. Strong intent signals show fit.

Negative quality signals

Red flags that indicate low quality

Some behaviors indicate lead is low quality. Filled form but wrong company size. Filled form but wrong industry. Filled form from free email address when you sell to enterprises. Downloaded your guide but competitor's guide same day. Clicked your ad but immediately left. Viewed your page once and never returned.

Negative signals are red flags. Not disqualifying. But worth noting. Lead with negative signals gets low priority. Lead with positive signals gets high priority.

Conversion correlation analysis

Measure what actually predicts sales

Track which signals correlate with conversion. Review leads that converted. What signals did they have. Converted leads average three page visits. Three is signal. Converted leads visited pricing page. Pricing page visit is signal. Converted leads average time on site ten minutes. Ten minutes is signal.

Compare to non-converted leads

Reverse analysis. Review leads that never converted. What signals did they lack. Non-converted leads average one page visit. One is low signal. Non-converted leads never visited pricing. Pricing visit is strong signal for conversion.

Build quality system on conversion correlation. Not on guess work.

Implicit versus explicit signals

Explicit signals are stated facts

Explicit signals are stated. They filled a form and said they have hundred employees. They said budget is fifty thousand dollars. Explicit signals are clear and reliable.

Implicit signals are inferred behavior

Implicit signals are inferred. They visited pricing page. Implies they might afford you. They visited case study. Implies they might be considering. Implicit signals are probabilistic. They usually mean something. Not always.

Weight explicit signals more than implicit. Someone said they have a thousand employee company is stronger than they visited your enterprise page. Explicit is fact. Implicit is inference.

Quality scores

Separate quality from intent

Combine signals into a quality score. Separate from purchase intent score. A lead might have high purchase intent but poor fit. A lead might have high fit but low purchase intent. Track both.

Two-dimensional scoring

Quality score fifty and above is good fit. Below fifty is poor fit. Intent score seventy five and above is hot. Below fifty is cold. Lead with quality seventy five and intent eighty is hottest. Lead with quality thirty and intent thirty is coldest. Two-dimensional scoring is more accurate than one-dimensional.