Sign 1: High Email Bounce Rate
If marketing emails bounce over 2-3%, your database has meaningful invalid-address contamination. Over 5% and ESPs will start throttling or suspending you. This is not theoretical. It happens on a Tuesday.
Bounces come from two places: addresses that were never real, and addresses that went stale. Segment bounces by source and cohort. You will usually find specific campaigns or date ranges skew way heavier on invalid, and those are the ones contributing most to the problem.
Sign 2: Low Contact Rate Despite High Lead Volume
If reps are reaching actual humans less than 40-50% of the time, something is wrong. Real people who submitted a form are usually expecting the call. They pick up.
Break contact rate out by source. One campaign sitting at 15% while direct traffic is at 70% tells you where the junk is coming from. That single chart usually reorders the media plan.
Sign 3: Your SDRs Complain About Lead Quality
SDRs are the canary. When they keep saying the leads are garbage, the data usually backs them up. Anecdotes about wrong numbers, bad emails, and prospects who have never heard of you are not vibes. They are a metric waiting to be measured.
Grab a rep, spend ten minutes, and classify their last 50 leads: real and interested, real but not a fit, unreachable, or obviously fake. If unreachable plus fake is over 20%, you have a problem that is costing real money.
Sign 4: CRM Data Doesn't Match Reality
Pull 100 random contacts and look them up. Do the companies exist on LinkedIn. Do the people work there. Are the email domains legit. If 20-30% of a random sample does not hold up, your intake is systematically polluted.
Run the same test on the top-scoring leads from a specific campaign. A campaign with 500 leads at $30 CPL and 35% fake has a real CPL of $46. That alone changes which campaigns get the next budget bump.
Sign 5: Forecasting Is Consistently Off
Forecast accuracy rides on data quality. Expected close rate of 25% with an actual of 12% is either a sales problem, a lead quality problem, or both. Usually both, because bad leads make sales metrics hard to read.
Teams with clean data typically forecast within 10-15%. Teams with polluted pipelines miss by 30-50%. Clean the data, measure again. The delta tells you the story without needing anyone's opinion.