Duduping a CRM is like an equation waiting to be solved. Just like a high school math, the more variables you know the values for, the easier the problem is to solve and the more likely you will come up with the correct answer.
CRMs include is a vast number of “Data Markers”. These markers are a literal road map to filling in missing data. For example, if 100% of the emails for a particular company have the email format of Firstname.Lastname@domain.com, then you can probably fill in missing emails for other contacts with confidence. If you have the email domains for contacts, but the account record is lacking a website, that can be filled in too.
A company’s website address is a unique identifier. It is more important than the company name. Look at Peoplesoft as an example: long after the company was acquired by Oracle, you could navigate to www.peoplesoft.com. A few years later, it was totally absorbed…but the website did outlive the company.
Done properly, leveraging data markers within a CRM allows the properly trained consultant to pre-fill data for a more complete picture…BEFORE deduping. In addition the data should be standardized (sometimes called Normalized) before the dedupe process is done.
If you take the correct measures of: (1) data normalization and (2) data fill before deduping, your dedupe process will be greatly improved.
- “Wrong phone number”
- “The company was acquired”
- “Wrong title”
- “I have duplicates”
- “The data is old”
- “The emails don’t work”
- “My CRM data sucks”
- “I just called someone who’s been dead for a year”
If you’ve heard any of the above comments or something similar; you have a CRM problem. Are their solutions to these problems? Yes, however; today I will give you a deeper understanding of WHY the problem occurs. Many good vendors exist to solve the problems listed above. I want to arm you with a deeper insight, the WHY.
If you understand the WHY, you will be able to:
- Have a deeper understanding to the nature of the problem
- Remove unrealistic expectations (solve the problem, don’t chase a rainbow)
- Define best practices to minimize bad data
- Be informed when choosing a vendor (flashy interface does not solve THE PROBLEM)
- Understand how your CRM decisions effect CRM data
- Help you be an advocate for change management within your organization
- Make you a more informed client (some vendors will like this, others will not)
So what is the WHY?
Contact data decays
If you have a short attention span, if you are brilliant, or have limited reading time, we are done here. That’s all you need and you know what I am going to say in the long answer. Thanks for reading.
First, let’s establish a baseline from the US Department of Labor.
The national average tenure across all jobs in the US is 54 months. That breaks down to 1.85% per month of job attrition. For high-demand IT workers the tenure is shorter with 3% monthly job attrition rate. The rate for Silicon Valley start-ups is almost ridiculous with the average tenure being just over a year.*
*Not from DOL. garnered from several Venture Capital blogs…take it as an extreme example.
A full year of data decay – base factors
A month at a glance does not show the full picture when factored across an entire year. Look at the picture across a year’s time.
When reviewing an entire year, data will decay at about 12%. However, that does not take into account many additional factors including: (more…)