Category Archives: CRM Data Plan

Titles in CRM: The standardization debate

I’ve often talked about the importance of having a Data Plan for a healthy CRM. A data plan is simply a set of standards for CRM data. For example, review the following ways to write:

Director of Human Resources
Director, Human Resources
Director of HR
Director HR
Human Resources Director
HR Director

Lack of standards is a problem. Why does it happen? These examples are from a live CRM. What were the sources of data?

  • User entered data
  • Web form connected to CRM
  • D&B
  • Jigsaw
  • Trade show directory
  • Broadlook
  • InsideView
  • NetProspex

Each source provides valuable data, but what is the indirect cost? For example, the data tagged “source= D&B” had the title in ALL CAPS. The data from Broadlook was standardized to “HR Director”. Jigsaw and NetProspex had the title in different formats. The worst case scenario: each source provides titles in different format.

This is a CRM nightmare.

The solution is to provide a system that enforces data standards. Some companies get by with an agreed-upon set of standards, but that is not enough. Enforce is the key word. Having an agreed-upon data standard is a great idea, but in reality you:

  1. Can’t control how your vendors deliver data
  2. Can’t force your sales reps to enter data correctly
  3. Can’t force users filling in web forms to change what they type
  4. Can’t prevent marketing from importing that latest trade show directory

So, again, the only answer is *enforced* Data standards. How do you do this?

The only way to accomplish true standards is via technology enforcement. One example of this is Broadlook’s CRM Shield, which simply allows you to pick a set of standards. Once a standard is built, then all data passing through the CRM is standardized.

So what is the debate?

Recently, one of my partners pointed out that his clients “Don’t want to change the titles”. He believed to save the titles in the way that they were provided. A “Vice President of Sales” did not want to be listed as “VP Sales”. Thus the debate began about Normalization/Standardization of titles.

Now if the client is always right, we want to accomplish this, but what is the true, best solution?

First, there are two types of Normalization, destructive and non-destructive. Destructive means that when the data is changed, something is lost and the change cannot be reverted from. An example would be truncating a title from “Director of Systems Automation” to “Director of Systems”. Once the “automation piece is gone, it cannot be recovered. A non-destructive example would be changing “Vice President of Sales” to “VP Sales”. The litmus test for non-destructive normalization is whether the change can be reverted. Starting from the shortest form, “VP Sales”, normalization rules would allow any variation of the title to be modified and re-modified without any loss.

The Counter-Argument

“If someone lists themselves as ‘Chief Executive Officer’ on their business card or email signature, they don’t want to be listed as ‘CEO’.” This is the counter-argument; to maintain the original state of the data. This is a valid point. Yes, we can change ‘CEO’ to ‘Chief Executive Officer’ and back again, but once it is changed, how do we know what the original title was? The answer is you don’t know.

Personally, I don’t believe in the counter-argument, but my job is not to judge what my clients want, but to deliver results that help them run their businesses more efficiently. I don’t believe in it for two reasons. (1) Really? someone is that vain that they get miffed about their title being correct, but not in the exact format? (yes, opinion only). (2) Non-standard data is a culprit in creating Dupes in the CRM. Dupes are ugly. Dupes costs companies millions of dollars each year in lost efficiency, data cleanup costs, skewed KPI’s and failed analytical reports. You can’t report on non-standard data. Just think about it.

The answer

Take my advice and normalize your data. However, if you must retain the original source of titles, you are THAT fickle CRM admin, here is what you should do.

Have 2 title fields in your CRM.

Field 1: The normalized title. Use this for search, KPI’s, reporting and deduping. Having a single standard by which to measure. This will keep your CRM tuned and your sales and marketing teams will love you.

Field 2: The Original title. Use this for display, mailing and client/suspect/client outreach. If you mail, use this field.


Even the Original title should be normalized to a certain degree. Example: “Vice — President of Sales.”. This example has extra spaces, dashes and a period at the end. A simple clean up would yield “Vice President of Sales”.

Last word

Regarding Normalization. I am right, the detractors are wrong (especially Gregg) and eventually they will come around to my way of thinking. Until then, I’ve done my job and outlined best practices that can make everyone happy.

Donato Diorio

Chief*** Executive***Officer!! , Data Guru (yes, Normalization can fix this too)

Data decay: The WHY behind why your CRM data sucks.


  • “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?

Short answer:

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.

Long answer:

First, let’s establish a baseline from the US Department of Labor.

Employee attrition - base dec

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.

Employee attrition - base decay by year
When reviewing an entire year, data will decay at about 12%.  However, that does not take into account many additional factors including:

  • Change in title, promotion
  • Change in working location
  • Change of phone number
  • Add mobile phone number
  • Change of department
  • Change of area code
  • Change of email format
  • Change in email domain
  • Change in company name
  • Change in company website
  • Merger or acquisition

While I did my best to outline all the factors, there are some that I probably missed.  What I wanted to show is a picture of the WHY data decays.  For the past 10 years, I’ve been immersed in data, the acquisition of data, and the analysis of data.  From this experience, I know that data decays somewhere in the vicinity of 2.8% to 5.5% per month.  This is based on:  (1) Hand verified data-sets that I’ve been building and maintaining for 4 years (2) Poll-based responses from 5 years of live training webinars and (3) General industry sentiment.   When adding in all factors, the picture changes significantly.

 A full year of data decay – all factors

Employee attrition - decay across all factors

Don’t make the “additive mistake” 5.5% decay per month does not equal 66% over 12 months (it’s actually 36%).  You must add each month and it’s data age and then factor in the average data loss across a year.  It’s still not a pretty picture.  In 2011-2012, the numbers are sitting at the high end of the continuum, somewhere in the 5 – 5.5% range. This means that you can expect data decay to be sitting somewhere around 36% a year.

The implications for CRM data health

So what is the health status of your CRM data?  Using the Estimator, find out where you stand.  Start by finding your CRM Data Update Cycle.  This is how often you go through a CRM full data update.  Next, based on what you know about the sector you sell into, pick a Monthly CRM Data Decay Rate.  As a guideline, in 2011-2012 the average is 4.5-5.5%.

CRM data health estimator

Based on the 2011-2012 numbers, to stay under the 10% threshold, the CRM should be refreshed every 60-90 days.

“Hold on Donato”, I get this statement often, “I’m updating contact information in my CRM all year long”.   My response is a question: What percent of all accounts in your CRM do you connect with each year and update their contact info?  Typically the response is somewhere in the 15-20% range.  Again, you must apply the data decay principal to data updated over a year time.  For example, if you connect with 1200 accounts per year, 100 per month, then at the end of the year, the first month’s data is 11 months old,  or somewhere in the neighborhood of 33% outdated.   If you are doing a fully updating contact information (name, title, email, phone, company URL company phone, etc) , each time you connect with an account, then you can give yourself a 5% data health increase.

This begs a question: Who should be in charge of updating CRM contact data?  If you expect sales to do it, I have bridge to sell you in New York.  My answer is Marketing or IT, based on how your organization is structured.

Where does this leave us?  At this point you should have a solid understanding of the WHY behind data decay.  In addition, you should have a more accurate understanding of your CRM Data Health.  Lastly, you should want to take action.  In this blog, I want to leave it as pure educational vs recommend vendors.  Vendors come and go, the percents of data decay may fluctuate, but the concepts should hold true for some time.

Questions to ask prospective vendors:


There are many products out there that can solve CRM data issues.  Solutions include data companies, deduplication vendors, offline data cleanse services, preventative data-technology and data import technologies.  Here are a set of questions to prepare yourself with.

  • How old is your data?  Just because you are buying data, does not mean that is it fresh.  Data vendors suffer from the same data decay as everyone else.   If they guarantee it updated every 6 months, that means it’s somewhere between 15 and 20% outdated the day you get it.
  • What is the source of your data?  If you’ve experience crowd sourced data, you’ve felt the pain.  Make sure the source of the data is consistent with what you need for your sales & marketing model.
  • Data standards: how will you deliver the data?  As an example, there are over 20 ways to write “The Container Company Inc”.  Which way will the company names, URL’s, names, titles, emails and phone numbers be delivered?  Will it match the data standards that you have?  I am shocked that you will still see data coming from large data providers in ALL CAPS.
  • Transparency:  Can I see the source of the data?  Not the same as where it came from.  True data transparency gives you the source of each piece of data.
  • Record by record data age?  Does each record show when it was last updated.  This is a tremendous confidence booster for sales.  Ask yourself: why are sales reps excited by new leads?  Answer: they are fresh.
  • How to prevent future duplicates?  If you don’t stop new duplicates from entering your system, you will have to keep doing it over and over again.
  • How to handle historic duplicates?  Stopping future duplicates is important, but full CRM data health means cleaning up the historic data.
  • Can you consult and help me with data standards?  If they can’t guide you here, for free, something is wrong.  This is the absolute first thing that needs to happen.  Pick a data plan.
  • How to enforce data standards?  Technology can enforce data standards so people don’t type in the wrong stuff.  How do they solve this?
  • How to import lists AND conform them to my data standards?  Ok, you just cleaned your CRM and now you have a list from a Data vendor in ALL CAPS…how will this be handled?
  • How to prevent human data-entry errors?  If you give them the opportunity, people will type anything into the CRM.  What is the solution?
  • Can you help with change management?  Changing the way a company works with data takes experience.  Are they thought leaders or product peddlers?  Can you show me the bumps in the road?  What is the next big thing?  What should I be planning for?

I hope you now have a solid understanding of data decay and it implications.  Understanding the issue is the first step towards instituting change.  If you get one of the vendors on the horn, be nice, you already have answers to questions that they are not asking.  They may also be excited to have a well-informed prospect as a customer.


Donato Diorio is a writer, blogger, speaker and the Founder & CEO of Broadlook Technologies, a company that specializes in CRM data health.  He can be reached at ddiorio at broadlook dot com.