Does your company CRM have data issues? Yes, it likely does. Everyone's CRM has data issues!
Having worked with thousands of operational strategists from several hundred forward-thinking and data-driven businesses, I can tell you that the median CRM's data health is very poor.
Why? I believe the root of the issue lies with the Empty Filing Cabinet problem and the Missing Company Problem. Together, these two problems mean that every CRM customer relies on user-generated input, which introduces entropy, which is both bad and compounding. This is the 6th in a multi-part series on the Intelligent CRM.
DataFox's foundational service, Account Data Management, connects DataFox's intelligence platform to client CRMs and diagnoses, enriches, completes, and refreshes our clients' Account data. We use our series of algorithms and APIs to help our clients surface and diagnose CRM data errors and surface duplicate and missing Accounts. We then fill their CRM with their best Accounts, and then automatically keep their CRM Account data up-to-date forever.
As a result, our algorithms have visibility into the data health of hundreds of CRMs (before they start using DataFox). To produce the following analysis, we aggregated the records from 1 month's worth of client CRMs, spanning 822k account records.
The Trouble With Your CRM Account Records
23% duplicate Account records
Wait, almost ¼ of all CRM Account records are duplicates? How?
With the way CRMs work, it's really easy to end up with dupes. There are several ways:
A company changes its name (for example, when ZenPayroll changed its name to Gusto). Then, a second version of the company gets entered into the system. In this case, you end up with two entries having different URLs, that are ultimately the same company but they are likely not linked. This causes problems and confusion in the long run. This is such a big problem that we productized our internal data audit systems so that we can help you surface these irregularities and link the accounts, available through our free CRM Data Diagnostic.
A business development team member doesn’t realize an account already exists (because their search was slightly different than the name that was recorded on the Account). This problem is either rooted in the user incorrectly typing the name when searching or the CRM containing an incomplete or incorrect name. In these cases, one small data problem (an incomplete Account name) begets another bigger problem (a user creates a duplicate version of the same account). This spawns further errors, an example of the many ways data problems compound, especially company data in CRM.
Someone on your team does a bulk spreadsheet import of target companies that when it’s imported. Then, the matching methodology only looks for domain name matches, which then auto-creates new company entries for any companies that don’t appear to exist in the CRM. If they forget that some of the domain names in the CRM have www and some do not, they accidentally spawn hundreds of duplicates.
Since every single CRM customer is responsible for building their own database of all prospect companies, all customers' CRMs have a lot of these problems.
Duplicate data leads to wasted time, mistakes, and ultimately, lost revenues.
28% incomplete or invalid Account records
Wait. More than 1/4 of all CRM Account records are incomplete or invalid? According to our data, yes.
Let's examine the biggest types of invalid Account records.
Are incomplete or invalid company profiles a problem?
Yes. If 28% of your company records have data problems, then high quality prospects will be missing from your market maps and sales territory management projects, they won’t show up in your total addressable market definition, and they won’t appear at the top of your target prospect reports.
If your reps are missing a large % of their total addressable market because they find all of their prospective customers in their CRM, then your company is likely missing a huge percentage of its total revenue. For example:
Missing URL - Means that you cannot reliably match and import other company data to inform your scoring models and if you do, you will likely produce duplicate records.
Parked domain - Likely an indication of companies that have gone out of business and should likely be purged from your CRM.
Name-URL mismatch - Indicates that either the name or web domain are wrong or that your profile is likely a mixture of company and subsidiary or company and product name.
Invalid URL - This problem tends to cascade into other problems (namely missed opportunities and duplicates getting created down the road).
51% of all Account records are either in a duplicate group or have a core name-URL problem.
That’s bad. Unfortunately, those are just the issues with the core identifiers for the companies you do have in your CRM.
More types of errors in your Account Data
Dead companies - Many of the records in our clients’ Accounts are for companies that no longer exist - they have either been acquired (and dissolved) or shut down. These just clutter things up and make your business development teams crazy when they waste time digging into a prospect only to do a bunch of searches and realize they shut down 18 months ago.
“Missing” companies - How many good fit companies are altogether missing from your CRM database that are in your territory? If you are missing 25% of your total addressable market (TAM) by it not being in your CRM, then you are not producing as much revenue as you could be.
“Hidden” companies - How many companies are in your CRM, but are missing accurate firmographic data, such as location, headcount, or industry? If an Account record is missing these data points, then the company does not show up in segmentation reports or territory carving exercises, and is therefore effectively “missing” as well. This is another slice of your addressable market that goes unaccounted for.
Records that aren’t up to date - When your Account level data is not kept up to date, it’s difficult to know which companies are growing and who to prioritize. The entire wave of predictive solutions hinges on having quality data in your CRM. You can’t be maximally efficient if you do not have a rank ordered list of your top target companies, which you can’t do if you can’t reliably sort and score companies based on their characteristics? Forget AI & predictive - those shiny objects won’t solve this problem. With bad data, the combined data science teams of Google, Amazon, and all of the predictive vendors combined won’t get you good results. In contrast, with clean data, you can get great answers with simple polynomials.
Hierarchy gaps - Sales reps love selling into parent companies and subsidiaries of existing clients, but if your CRM family trees are not connected, then you’re missing opportunities. To do proper territory assignment and give your reps the best chance of selling within large organizations, you need organizational hierarchies wired up; but they are hard to surface on your own and are continuously evolving.
If You Use Leads and Accounts in CRM - Well, You Already Know...
Bad company data has a big negative impact on Leads too (which results in frictionfull handoffs between marketing and sales development teams).
Leads not attached to Accounts - How can you find the companies from which you’ve received Leads that are not yet attached to Accounts in your CRM? How can you be sure that a lead who has used his/her personal email has already been assigned to a sales rep? If your CRM isn’t smart enough to know what company your Lead works for, then how does the rep who owns that Account get alerted?
Leads lacking Account-level data - If your Lead data does not include information about the company, it’s very difficult to nurture, score, route, and qualify Leads.
CRM is the now the largest of all enterprise software categories, passing ERP (which I covered in The Origin of CRM). Despite it’s massive adoption, most CRMs have a very important piece that is incomplete and partially corrupted - the companies that you can do business with - the vertebra of Your CRM.
CRM Accounts have nearly innumerable types of foundational data errors and I have tried to characterize them and illustrate how they originate - to help explain why such a big problem persists despite being so punitive for the teams directly using them as well as the other teams relying on this “System of Record” for their forecasts and decision analyses.
Everyone who heavily uses CRM sees the downstream effects of these data issues and yet the challenges continue to plague the sales, marketing, and business development teams of even the most data-driven enterprises.
Now that I’m all riled up (I started this company because of how much I dislike that people have to deal with these data problems), it’s important to emphasize the other half of the story:
First, bad CRM data is not your fault. Second, while you may feel like Sisiphys, we can help you reverse the entropy and start taking back your CRM. DataFox can help clean & rectify your account records and then automatically keep them clean and complete going forward through our data management subscriptions and software that helps your team be both more efficient and better stewards of your data going forward.
Is this a representative data set? I actually suspect that our clients’ #s are better than the norm, since our customers tend to be some of the more forward-thinking businesses across financial services and high tech firms. I suspect as we expand to serve more firms that are not market leaders, we’ll find the data error rates of new clients to get worse, not better. ↩︎