How Accurate Are Your Data-Driven Decisions?

Tom Montag 8 min read May 5, 2021

One of the main reasons companies invest in a CRM is data handling. Once the amount of data your company has on leads and clients gets to a certain number, manual sorting and crunching just won't cut it anymore. Most businesses start out small, and in many cases, spreadsheets would have been in use before migrating to a CRM solution. While there's nothing wrong with that, it is one reason there's so much dirty data flowing around.

Just because you have an entry in your CRM, it doesn't mean that data is accurate. The numbers for dirty data can make for depressing reading. According to some industry reports, businesses may be losing as much as twelve percent of their revenues through dirty data. The statistics for incomplete data are even more depressing. Up to ninety-one percent of CRM data is incomplete, and over seventy percent of CRM data goes stale within a year.

Not all of that is malicious, of course. Take telephone numbers, for example. You can walk into your local mobile provider and walk out with a brand new phone number in minutes. While that's convenient for the user, the CRM operator now has stale data for that user's phone number.

A prime example of dirty data impacting operations comes from the financial services industry. Most financial services companies will operate under some kind of regulatory framework. That framework will call for certain mandatory communications with all the firms' clients at regular intervals. Dirty or incomplete, or stale data may well get in the way of these mandatory communications, opening the doors to potentially expensive settlements. Client misclassification is another prime example. Regulations are stringent on client classification. Get it wrong, and you could be looking at another costly settlement. Lots of data is good. Dirty data certainly isn't.

So What Do We Mean By Dirty Data?

Dirty data are CRM entries that are fraudulent, invalid, stale, incomplete or duplicate. Some dirty data is purely accidental. Misspellings are an excellent example of unintentionally incorrect data. Innocent enough on the surface, but even an innocent misspelling can have grave consequences. Say a client in Germany misspells his town as Jamburg instead of Hamburg. You run a filter to send an essential communication to all your clients in Hamburg, and that particular client won't get the message.

Fraudulent data typically aim to overload your systems and reduce your work efficiency. Bot registrations are a prime example here. The software sends thousands of automated registrations through your signup forms, choking your systems. Similar to a DDOS (Distributed Denial Of Service) attack on a website but in this case targeting your CRM.

Invalid data are entries in the wrong field. One of the most common examples of this is for First Name and Last Name fields. All too often, we see registrations with a full name in the First Name field. That's innocent enough, but send out an email with an informal [first_name] salutation, and you'll look unprofessional, to say the least.

Duplicate data is just that. Multiple duplicate entries for the same client. While this can be an attempt by a client to register multiple accounts, it can also be down to operator error. One of your staff runs a query on your CRM and files the resulting data as new entries. Take it from us. That's a lot more common than you might think.

Stale date is simply data that's out of date, like the mobile phone example we touched on earlier.

Incomplete data are any entries that lack any of the required information.

How Dirty Data Affects Your Business

Automation is supposed to make your business run smoother and more efficiently. That efficiency can take a bit of a pounding, though, if your sales teams have to wade through oceans of dirty data. Let your CRM fall prey to auto-registrations, and your sales teams could end up dialling incorrect numbers all day long. That's not the best use of their time by any stretch of the imagination.

In the case of regulated financial entities, dirty data can become extremely expensive. Miss one of those mandatory communications through bad data, and you could well end up with yet another costly settlement, not to mention the adverse impact on your company's reputation.

Incorrect data can have a very detrimental effect on your client engagement. Get a client's time zone wrong and see how they react when your sales teams start calling them at 3 am! How about addressing an email to "Miss Richard?" How well do you think that will go down? Probably get you a quick unsubscribe and a lost lead.

Your CRM and the way you handle your data are the driving points for your business decisions. Your data will influence any business move you make to a considerable extent. For example, based on the data, you might be thinking of opening a new desk for Spanish speaking clients. All good and dandy till you check the data and figure out all your Spanish speaking signups are bots.

Dirty data means not all data-driven decisions will be good ones. To get to the actual data, you need to run regular maintenance on your CRM. That might seem like a lot of work, but the effort you put in today will pay dividends tomorrow.

Set your own internal procedures for screening any incomplete, duplicate and inaccurate data. If you set the rules from day one, it's much easier to maintain the quality of your data over the long term.

Are you stuck with a CRM full of dirty data? Are your data-driven decisions falling flat? Speak to one of our success managers, and we'll get your data squeaky-clean before you know it!

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