Why Your Salesforce Data Decays Faster Than You Think

Pull up any Salesforce report filtered to contacts created more than six months ago. Pick ten at random and check their LinkedIn profiles. How many still work at the company listed in your CRM?

If your org is like most, at least two or three of those ten have moved on. Their email bounces, their direct line goes to voicemail or someone else entirely, and the account they're tied to in Salesforce no longer has your champion in it.

This isn't a one-time cleanup problem. It's ongoing. Contact data decays at a steady rate, and most sales teams don't have a process for dealing with it until something breaks.

How fast data actually decays

The Bureau of Labor Statistics puts the median employee tenure in the US at about 4.1 years. That means roughly 24% of the workforce changes jobs every year. For younger workers (25-34), median tenure drops to 2.8 years, which pushes annual turnover closer to 36%.

Sales and business development roles turn over even faster. LinkedIn's own data has historically shown sales rep tenure averaging around 18 months. If your Salesforce is full of SDR and AE contacts at other companies, a meaningful chunk of those records go stale every quarter.

Here's what that looks like in practice. Say you have 10,000 contacts in Salesforce. At a 24% annual decay rate, about 2,400 of those contacts will change jobs this year. That's 200 per month, or roughly 50 per week. Every week, 50 of your contacts are quietly becoming unreachable at the email and phone number you have on file.

If your CRM has been accumulating contacts for two or three years without systematic re-verification, the math gets worse. A contact created 18 months ago has had time for roughly 30-35% of similar contacts to churn. A contact created three years ago is approaching 50-60% staleness for the cohort.

What goes stale first

Not all fields decay at the same rate.

Work email addresses are the most fragile. When someone leaves a company, their email either stops working immediately (if IT deactivates it) or continues accepting mail for a few weeks to months before getting shut off. Either way, the address becomes useless for outreach within days of the person leaving. Some companies redirect former employee mail to a general inbox or their manager, which means your carefully personalized email lands in front of someone who has no idea who you are.

Job titles change frequently even when someone stays at the same company. Promotions, reorgs, and title standardization can all make your CRM data wrong without anyone changing employers. A "Senior AE" who got promoted to "Enterprise AE" or "Sales Manager" is the same person, but if your sequences segment by title, they might fall out of the right bucket.

Phone numbers are the most durable. People keep their cell numbers across jobs, so a direct mobile number stays valid longer than a work email. But office direct lines and company switchboard extensions die the moment someone leaves. If you're storing desk phones in Salesforce, those go stale at the same rate as email.

Company data decays too, just more slowly. Companies get acquired, rebrand, change domains, or shut down. A contact at "Acme Corp" might now work at "Acme, a BigCo Company" with a new email domain. The contact didn't change jobs, but your CRM record is still wrong.

Why most teams don't notice until it's too late

Data decay is invisible until you try to use the data. A stale contact sitting in Salesforce doesn't throw an error. It doesn't flag itself. It just sits there looking like a valid record until someone tries to email it and gets a bounce, or calls the number and reaches a confused stranger.

Most sales teams discover decay reactively. A rep runs a campaign, gets a 12% bounce rate, and realizes the list is full of dead contacts. Or an AE goes to follow up with a champion from six months ago and discovers they left the company two months after the last conversation.

By the time you notice, the damage is already done. The bounced emails hurt your sender reputation. The wasted sequences burned time and credits. The lost champion means the deal might be dead and nobody flagged it.

The other reason teams don't notice is that Salesforce doesn't surface decay proactively. There's no built-in "this contact might be stale" indicator. No automatic check against LinkedIn or email verification services. The CRM stores what you put in and gives it back unchanged, whether the data is two days old or two years old.

What decay actually costs you

The cost isn't just a few bounced emails. It compounds across every part of the sales process.

Outbound campaigns hit harder. If 20% of your contact list has decayed, you're spending the same amount of time and money on sequences that reach 20% fewer people. Your reply rates look worse, your pipeline looks thinner, and it's hard to tell whether the messaging is bad or the data is bad.

Forecasting gets unreliable. If an AE has 30 opportunities in their pipeline and 5 of them have stale champion contacts, those deals are probably dead but still sitting in the forecast. The AE doesn't know the champion left because nobody checked. The manager doesn't know because the CRM shows an active contact.

Renewal and expansion suffer. Customer success teams rely on contact data to manage relationships. If your primary contact at an account churned six months ago and nobody updated the record, the CS team is emailing someone who doesn't work there anymore. The account is effectively unmanaged until someone notices.

Re-engagement campaigns fail silently. A marketing team pulls a list of leads who went cold 9 months ago for a nurture sequence. One fourth of the emails bounce because the people changed jobs. The campaign metrics look terrible, but the real problem happened months ago when the data went stale.

What you can do about it

There's no way to prevent data decay. People will always change jobs. But you can build processes that catch it before it causes damage.

Set up bounce monitoring and act on it. Every bounced email is a signal that a contact record is stale. If your email tool reports bounces, route them back into Salesforce as a field update. Flag contacts with bounced emails so reps know not to waste time on them. Most email tools can do this with a simple integration or Zapier workflow.

Re-enrich your database on a schedule. Pick a cadence that matches your sales cycle. If your typical deal takes 3-6 months, re-enrich your active pipeline contacts every quarter. Run the contacts through your enrichment tool again and compare the new results against what's in Salesforce. If the email changed, the contact probably changed jobs.

Monitor job changes through LinkedIn. If you have Sales Navigator, you can set up alerts for saved leads who change positions. This is the earliest signal you'll get. When a champion changes jobs, you know within a week or two instead of finding out from a bounced email three months later.

Build a decay dashboard. Create a Salesforce report that shows contacts by creation date and last activity date. Any contact created more than 6 months ago with no activity in the last 90 days is a candidate for re-verification. This doesn't require any additional tools. It just requires someone to look at the report regularly.

Use enrichment tools that check multiple sources. A tool querying one database might not have picked up a job change yet. A tool querying 15-20 databases has a better chance of finding updated information because different providers update at different speeds. This doesn't eliminate staleness, but it reduces the window.

Clean on entry, not just on exit. When reps add contacts to Salesforce, verify the email before saving. An enrichment tool that checks validity at the point of entry prevents bad data from getting into the system in the first place. This is cheaper and easier than cleaning up later.

The baseline you should measure

Before fixing anything, measure where you are now. Pull a random sample of 50-100 contacts from Salesforce that were created more than 6 months ago. Check their LinkedIn profiles manually (or using a tool like ShareCo SalesSync). Count how many still work at the listed company, how many have changed jobs, and how many profiles you can't find.

That number is your decay rate. If it's under 15%, your data is in decent shape. If it's 20-30%, you need a quarterly re-enrichment process. If it's above 30%, you've been accumulating stale data for a while and need a one-time cleanup before any ongoing process will help.

The number won't be zero. It's never zero. The goal is to keep it low enough that your outbound campaigns, pipeline forecasts, and customer success workflows aren't making decisions on bad information.