CRM Cleanup Tips
One of the primary purposes of CRM management, or customer relationship management, is to harvest, organize, and analyze customer data to better understand who they are and how to reach them. Armed with the right data, your CRM can streamline workflows, improve customer loyalty and retention, and drive profitability.
But with a constant deluge of customer information pouring in, it’s vital that you perform regular data cleaning to ensure that the info your CRM system uses for analysis and insight generation is accurate and up to date.
CRM data cleanup can be a complicated process—here are some tips to make it easier.
Why Does a “Clean” CRM matter?
A CRM serves several purposes. It helps gather, organize, and segment customer data, nurtures customer relationships, optimizes the sales funnel, and streamlines customer service.
But a CRM’s ability to produce successful results hinges upon the quality of the data. With clean data, it can run faster and more efficiently. This results in:
- Enhanced customer satisfaction
- Better decisions
- Cost savings
But when the data isn’t clean, it hinders your employee’s ability to perform their job at a high level. As Forbes notes:1
“When sales and marketing professionals are swimming in heaps of dirty data, they are shackled from making informed data-driven decisions. A mere 33% of marketers feel they can rely on their CRM data to make decisions. Poor data quality costs the US economy approximately $3.1 trillion annually. As the old adage goes, ‘garbage in, garbage out.’”
To get the most out of your CRM system and its various tools—particularly those that rely on automation—they need clean data to fuel them. Just like a supercar needs high-quality gas, a CRM requires first-rate, up-to-date data.
What Is Dirty Data?
Companies use various channels to collect contact data from customers or would-be customers, including:
- Physical storefronts
- Face-to-face contact via sales teams
- Websites
- Mobile websites
- Mobile applications
- Catalogs
- Mail orders
- Call centers
When the information is entered manually, this inevitably leads to human error. And the same can be said for automated data collection and entry—it just happens less frequently. But even then, if a customer inputs incorrect data, there’s no way for a machine to know that.
Bad data is a more common issue than you might initially assume.
According to the Experian Data Quality survey—which polled more than 1,200 organizations across a range of sectors and company sizes—the average company estimates that 22% of its contact data is inaccurate in some way.2
So, how does data become bad?
Typically, this occurs in one of four ways:
- Outdated data – Customer information decays over time. The information they provided a year ago, may not be the same today. People move; they get new numbers, change their email addresses, or switch jobs. According to Data Axle,3 approximately 4% of mailing list addresses, resulting in $180,000 wasted annually on undeliverable mail.
- Duplicate data – One of the most common ways data gets dirty is when there are duplicates. So, even though the information might be accurate, a duplicate entry skews your CRM’s analysis or results in double contacting. Common reasons for this include:
- Manual error
- Merging lists
- Faulty CRM software
- Improperly formatted data – The way and order in which data is input can be done in one of several combinations. It could be first name, last name, then contact information. Or last name, first name, contact information. And so on. When data is input manually, it opens up the possibility that one employee fills out the forms differently from another.
- Customers input their data incorrectly – Similarly, when customers are asked to fill out online forms, they may enter the wrong info in a certain box, or misspell something.
Ways to Clean Up Your Data
Having bad data is expensive. According to Harvard Business Review, it costs the U.S. $3 trillion every single year:4
“The reason bad data costs so much is that decision-makers, managers, knowledge workers, data scientists, and others must accommodate it in their everyday work. And doing so is both time-consuming and expensive. The data they need has plenty of errors, and in the face of a critical deadline, many individuals simply make corrections themselves to complete the task at hand.”
But just because it hampers other businesses doesn’t mean that the same has to happen to you. Fortunately, there are steps you can take for data cleaning, thus optimizing your system and saving you money.
Some simple CRM cleanup tips include the following.
#1 Standardize the Data
First, take precautions to prevent more bad data from accruing, or at least reducing the rate at which it occurs.
Data standardization accomplishes that.
As Momentum data notes:5 “If concrete and strict rules aren´t implemented, employees will be unsymmetrically inputting data that will be difficult to align. As databases become larger, this becomes more and more relevant as data standards can quickly be lost.”
By setting rules and systems for data collection and input within the CRM, you can instill good habits that replace fragmented methodologies. Implementing proper form validation and data cleansing processes won’t completely eliminate bad data inputs, but it will reduce how frequently it occurs.
#2 Fix the Small Formatting Issues
When it comes to data, minor issues can cause major headaches—especially when there are thousands (or millions) of data points.
Take capitalization for instance. When filling out forms, people may not capitalize their first and last names; others may input their name fully capitalized. This may not seem like a problem, but if you’re sending out marketing emails and the email mis-capitalizes the individual’s name, it can remove the feeling of personalization.
Another common manual data entry issue involves zip codes, namely, zip codes that start with a zero. According to Thomas Bonneau from GB Sterling:6
“[I]f you have a bad data file you could be ignoring nearly 10% of your nationwide dataset…If you have a data file in Excel containing zip code and the Column and the column is formatted correctly (Special Zip Code or Text), everything will look great if you have a zip with a leading zero (e.g. 02739). However, when you save this file into a CSV to import into your CRM and re-open it again to edit, the leading zero will drop off because the CSV ignores any prior Excel formatting that was preserving this zero.”
On the surface, these are small problems, but those tiny errors can hurt your bottom line by wasting valuable time and resources. By addressing these issues before they are imported into your CRM, you will save money and time while reducing employee frustration.
#3 Purge Duplicates
No one wants to be targeted twice by a company—even one they are a loyal customer to. It starts feeling like spam.
If your customer list isn’t long, duplicate purging can be done by hand. Just do so regularly to prevent the issue from building up. However, if you’re a larger business, automation can assist. Most CRM systems have automated features that allow you to set rules and conditions that detect duplicates.
From there, be sure to set your system to automatically block duplicate content from being input.
#4 Archive Your Data
Do you have data that you aren’t currently using but may need in the future?
One common issue with companies is that they are required by compliance regulations to store historical data. The fix for this is archiving that information. In doing so, you can ensure that the data is stored safely but no longer impacts the system. This helps you get more disk space, speeds up processing times, and makes it easier to search current records.
Similarly, older data that you can delete should be deleted.
#5 Consolidate Fields
In some cases, there may be several fields for data entry that contain similar if not redundant information. By cutting down input options, you reduce the chance of duplicate or inaccurate data entry.
#6 Enrich Your Data
In some cases, data may be considered dirty when it’s incomplete. It may be missing a zip code. Or you may have an email address but no phone contact. Flag your contacts that lack information, and then see if you can fill in the blanks.
#7 Outsource Your CRM Efforts
One of the easiest ways to manage your CRM data, especially if you’re a larger company, is to outsource the process to a data cleansing company. These experts have the skills, experience, and tools needed to clean up data.
Many of these entities also provide data enrichment services. This means you can clean up and enhance your data in one fell swoop. By outsourcing you can save your internal teams time and money, empowering them to focus on what they do best.
CRM Cleanup Made Simple
Better data means more useful insights. By performing a regular CRM cleanse you can ensure that your system is being fueled by actionable customer information.
From there, you can jump to the next item on your to-do list. Want to use the actionable CRM data to improve your marketing efforts?
We can help with that.
Here at Power Digital, we’ll create a holistic picture of your marketing strategy and then employ tried and true techniques to help you achieve your KPIs—including using your now-clean CRM data effectively. Reach out today to get started.
Sources:
- Forbes. Best Practices For Data Hygiene. https://www.forbes.com/sites/falonfatemi/2019/01/30/best-practices-for-data-hygiene/?sh=1fde3d072395
- Econsultancy. The Cost of Bad Data. https://econsultancy.com/the-cost-of-bad-data-stats/
- Data Axle. Infographic: How data decay is tanking your sales and marketing strategies. https://www.data-axle.com/resources/blog/infographic-how-data-is-tanking-your-sales-and-marketing-strategies/
- Harvard Business Review. Bad Data Costs the U.S. $3 Trillion Per Year. https://hbr.org/2016/09/bad-data-costs-the-u-s-3-trillion-per-year
- Momentum Data. 9 CRM Data Cleanup Tactics for Sales Productiveness. https://momentumdata.com/9-crm-data-cleanup-tactics-for-sales-productiveness/
- Incycle. The Ultimate CRM Data Cleanup Checklist. https://blog.insycle.com/the-ultimate-crm-data-cleanup-checklist
