What SDRs Actually Do with Enrichment Data (A Workflow Teardown)

Most enrichment tool marketing talks about match rates, data accuracy, and provider counts. Almost none of it explains what happens after the data comes back. What does an SDR actually do once they have an email address and phone number? And more importantly, where in that workflow does bad or incomplete data cause the most damage?

I've watched enough SDR workflows up close to know that the way most reps use enrichment data is messier than any vendor demo would suggest. The gap between "tool finds an email" and "rep books a meeting" has about six steps in it, and each one is a place where data quality either saves time or creates work.

The typical SDR enrichment workflow

Here's what the actual daily process looks like for most reps using LinkedIn and Salesforce.

Step one is finding prospects on LinkedIn. The rep has a target account list or an ICP to work from. They're browsing profiles, checking who matches their criteria, and building a mental list. This step has nothing to do with enrichment. It's judgment work.

Step two is moving the prospect into the CRM. This is where enrichment enters the picture. The rep needs to get the person's name, title, company, email, and phone number into Salesforce. Without an enrichment tool, this is a copy-paste exercise: name from LinkedIn, manual email search, maybe checking the company website for a phone directory. It takes 3-5 minutes per contact if you're fast.

With an enrichment tool, this step drops to 10-30 seconds. Click a button, the tool pulls available data from its providers, and the contact lands in Salesforce with fields populated. The time savings here are real and easy to measure.

Step three is checking what came back. This is the step most people skip, and it's where problems start. The enrichment tool returned a result, so the rep assumes the data is good. They don't check whether the email is at the right company, whether the phone number has the right country code, or whether the title matches what they saw on LinkedIn.

Step four is sequencing. The rep adds the contact to an outbound email sequence, usually with some level of personalization based on the prospect's role and company. If the email is wrong, this is where the damage happens, but the rep won't find out for 24-48 hours when the bounce notification comes through. By then, they've already moved on to the next batch.

Step five is calling. If the tool returned a phone number, the rep calls it. A wrong number wastes 30 seconds. A disconnected number wastes 15 seconds. A correct number that goes to voicemail is at least targeting the right person.

Step six is logging and updating. After the call or email sequence, the rep updates Salesforce with notes, disposition codes, and next steps. If the data was bad at step two, everything logged between steps two and six is noise. The rep spent 15-20 minutes of activity on a contact who was never reachable.

Where data quality hits hardest

Not all data problems are equal. Some waste a few seconds, others blow up entire campaign segments.

Wrong emails are the most expensive per-incident problem. A wrong email doesn't just waste the send. It contributes to your domain's bounce rate, and if that rate climbs above 2-3%, every other email you send starts landing in spam at higher rates. One bad email costs you the credit. A pattern of bad emails degrades the deliverability of your entire outbound program.

Missing emails are less damaging but more common. If the enrichment tool can't find an email, the contact sits in your CRM with no way to reach them by email. The rep has to either call without warming up the contact first, try to connect on LinkedIn instead, or skip the prospect entirely. Most reps skip. They have a quota, and spending time on contacts with no email doesn't help them hit it.

Wrong numbers and stale records

Wrong phone numbers sit in the middle. A wrong number wastes a call attempt, but at least the rep finds out immediately and can move on. The problem is volume. If a tool returns phone numbers for 80% of your list but 10% of those are wrong (outdated, wrong country, personal cell instead of direct dial), you lose 8% of your call block to numbers that were never going to connect. Over a week of 40+ calls per day, that adds up to a couple hours of wasted dialing time.

Stale title or company info is the sleeper problem. The enrichment tool shows "VP of Sales" but the person was promoted to CRO three months ago. The rep writes a message referencing the VP role, and the prospect either ignores it because it's obviously templated or replies with mild annoyance. The data wasn't technically wrong at some point, it just wasn't current. This is hard to detect because the email might still work and the person is still at the company. The inaccuracy shows up in the quality of the conversation, not in a bounce report.

The 15-minute problem

Here's what a typical failed enrichment cycle looks like in practice.

They find a prospect on LinkedIn. They enrich the profile. The tool returns an email but no phone. The email looks like it might be a catch-all domain (company uses a format like info@ or sales@ rather than firstname@). The rep isn't confident enough to personalize heavily, so they use a lighter template. The email lands but gets no response because generic emails to catch-all addresses rarely do.

Two weeks later, the sequence finishes with no engagement. The rep marks the contact as "completed, no response" and moves on.

The total time spent: maybe 15 minutes across sequence setup, monitoring, and disposition. The result: nothing. Not because the prospect was a bad fit, but because the data was borderline and nobody caught it before the sequence started.

Scale this across a team of 5-10 SDRs and you're looking at hours of productive time evaporating each week into contacts that were never set up to succeed.

The habits that reduce data waste

The reps who consistently hit quota aren't necessarily better at writing emails or making calls. The pattern that shows up when you look at what they do differently is that they spend more time on data quality before they start outreach, not after.

They verify before sequencing. Before adding a batch of contacts to a sequence, they spend 10-15 minutes scanning for obvious problems: emails at old companies, phone numbers from different countries, titles that don't match what LinkedIn shows. This catches maybe half the bad data before it enters the workflow.

They prioritize contacts with both email and phone. If a contact has only an email, it goes into a lower-priority sequence. If a contact has both email and a direct dial, it goes into the multi-touch sequence with calls. This means the reps who have access to better enrichment data don't just reach more people. They can run more effective sequences on a higher percentage of their list.

They track their own bounce rates. Not at the team level, at the individual level. If their bounce rate starts climbing, they know their data source has a problem before it hurts their domain reputation. Most reps don't check their individual bounce rate at all. The ones who do catch problems weeks earlier.

They re-enrich before re-sequencing. When a contact from six months ago comes back into a campaign, high-performing reps don't just resend to the old email. They run the contact through enrichment again to check if anything changed. Job changes, company acquisitions, or even just a new phone number can turn a dead lead into a live one, or save the rep from sending to a dead email.

Where enrichment tools stop and reps take over

There's a difference between what enrichment tools do and what reps need them to do that doesn't show up in feature comparison tables.

Most tools are designed for the enrichment step: you give them a profile, they return data. The workflow around that enrichment, checking the data, prioritizing contacts based on data completeness, flagging stale records, is left to the rep or to separate tools.

This means a rep using a tool with 85% email accuracy still has to build their own process for handling the 15% that comes back empty or wrong. At most companies, that process is "skip it and move on." The result is that 15% of the prospect list is functionally unreachable, not because those people can't be reached, but because the workflow has no way to recover from a data miss on the first try.

Tools that check multiple sources (waterfall enrichment) reduce this gap by finding data that a single source missed. But even waterfall tools don't solve the workflow problem. The rep still needs to verify, prioritize, and maintain the data after the initial lookup.

A realistic time audit

If you want to see where enrichment data quality actually affects your team's time, run this exercise.

Have each rep track their time for one day across these categories: prospecting on LinkedIn, enriching and saving contacts, checking data quality before sequencing, sequencing and personalizing, calling, handling bounces and bad data, logging and disposition.

For most teams, the split looks something like this: prospecting and sequencing take 60-70% of the day, calling takes 20-25%, and data cleanup takes 10-15%. That last category is the one that changes most with data quality. A team using high-accuracy enrichment might spend 5% of their day on data issues. A team with a mediocre tool might spend 20%.

The difference between 5% and 20% of an SDR's day is roughly an hour. Per rep. Per day. Over a month, that's 20+ hours of selling time recovered per person. For a team of five reps, it's 100 hours a month, or about 2.5 FTEs worth of added capacity, without hiring anyone.

What this means for choosing a tool

When you're evaluating enrichment tools, the match rate and accuracy numbers matter, but they only tell you part of the story.

Ask what the workflow looks like when data is missing. Does the tool retry with other sources? Does it flag uncertain results? Does it let the rep move on to other work while enrichment runs in the background, or does it force them to wait on the profile page?

Ask what happens with stale data. Can you re-enrich contacts in bulk? Does the tool track when data was last updated? Can it flag contacts where the title or company has changed since the last lookup?

Ask what the actual bounce rate is for teams using the tool. Not the vendor's claimed accuracy rate, because that's measured under controlled conditions. Ask for the bounce rate of real outbound campaigns built from the tool's data. If they can't answer that, the claimed accuracy rate doesn't mean much.

The best enrichment tool for your team isn't the one with the highest match rate on paper. It's the one that minimizes the time your reps spend dealing with data problems instead of selling.