5 Common CRM Data Errors to Avoid

5 Common CRM Data Errors to Avoid

5 Common CRM Data Errors to Avoid

I don't need to tell you how much Salesforce account data errors can set your team back: the wasted time of a rep prospecting into an account that's already a customer, the gut-wrenching awkwardness of calling a VP of Marketing who'd moved on six months ago, the Sisyphean task of assigning accounts with incomplete or outdated location, headcount and revenue data.

Over the course of cleansing and enriching our customers' CRM data, we've seen our fair share of errors. Here are some of the most common ones.

5 CRM data errors plaguing Salesforce accounts

1. Subsidiaries, formerly-known-as or acquisitions

Let's say you have an existing account with ZenPayroll, the HR startup. One of your reps notices that Gusto is in her territory, searches Salesforce to see if it's an existing account, and sees no matches. She eagerly reaches out to Gusto - only to realize that Gusto was formerly known as ZenPayroll, and she's wasted her time calling an existing customer.

2. Generic company names

Companies with common names can cause problems. Take Insight Software - a pretty common name that maps to three companies in the DataFox company database:
Name Website Location
InsightSoftware.com insightsoftware.com Greenwood Village, CO
Insight Software myvisionexpress.com Weston, FL
Insight Software Solutions wintools.com Kaysville, UT
As we saw in the example above, simply matching on company websites isn't enough - you have to see whether each of these companies is a distinct account or is simply a duplicate, and ensure that any activity is mapped to the correct account.

3. Generic product websites

The previous example saw one generic name mapping to three different websites. This is the flip side: many names map to one generic website. For example:
Name Website
RandomApp itunes.apple.com/random-app
Apple, Inc. apple.com
This is potentially an even worse problem: a rep might, based on the company domain, assume that RandomApp corresponds to Apple, the billion-dollar company, and mistakenly prioritize what's probably a poor prospect (and might not even be in business anymore).

4. Misspellings or typos

Misspellings are inevitable when reps are manually entering information. If you're cleansing your account based solely on unique URLs, www.uber.com and wwww.uber.com will show up as distinct accounts; ditto for datafox.com and datfox.com (dat fox, doe...). You'll need to match on more than URLs to catch incorrectly entered data.

5. Parked domains or closed-down companies

Finally, Salesforce data can get stale - fast. Your accounts may contain thousands of companies that shut their doors, whose websites are technically active, but are simply a parked domain. Simply checking to see whether a URL is redirected or returns a 404 won't cut it: you'll get a ton of false positives.

But there's hope yet for your Salesforce data

DataFox fixes all of these errors and more. We don't just use an account name or URL match - we use a combination of machine learning algorithms and human auditors to make sure your data is accurate, up-to-date and de-duplicated. We're proud of our rigorous matching process, and we think you'll be ecstatic about the results.