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Your customer data is the lifeblood of your business. It is the fuel that enables a single customer view. 

Your marketing team uses it to engage with prospects across a variety of channels, personalize communications, and automate marketing campaigns. Your sales team uses it to identify opportunities, provide context, and speak to your prospect’s biggest concerns. Your customer support teams use it to better understand the needs of your customers and provide a better post-sale experience. Your finance team uses it to make forecasts and estimates.

Better customer data improves your ability to attract, close, and retain customers

Your ability to effectively collect, enrich, and use data impacts the customer experience at every stage of the customer lifecycle. Companies with effective customer data management operations provide better, more personalized experiences to their customers and make more effective data-backed decisions. 84% of CEOs report that they are concerned about the quality of the data they’re using to make decisions. 

Unfortunately, most companies have a lot of room for improvement in the ways that they collect, maintain, and utilize customer data. 

Worse, many vastly underestimate just how much bad data they have in their CRM tool, and how much that bad data is impacting their business. Sales teams lose an estimated 550 hours and $32,000 per sales rep from using bad customer & prospect data. That impact isn’t just in terms of sales, but the way that customers perceive their brand as a whole. 

In this article, we’ll cover some simple steps that companies can take to improve their customer data management operations to improve their data quality and improve their bottom line. 

1. Quantify the Problem With Data Audits

Did you know that up to 40% of leads in your average marketing database contain bad data?

What effect is low-quality data having on your business today? 33% of companies believe their customer data is inaccurate in some way. But do they know how? Or why?

Are you able to identify where your data problems lie? Is your customer database full of duplicate contacts? Do you have standardization issues that bottlenecking your ability to use the data in the way that you want? Are you able to directly connect data issues to lost revenue and estimate real-world revenue improvements?

The truth is that many companies don’t have any real idea of the reality of their data situation. Bad data — duplicate data, incorrect data, improperly formatted data — is much more common than most realize. 

But if you can’t quantify how much bad data you have on hand, you can’t begin the process of determining how that low-quality data is affecting your bottom line. 

A comprehensive audit is the only place to start. Not only for the reasons mentioned above but also so that you can set metrics for success. You can’t rely on a gut feeling when it comes to data quality. You have to track how your new processes are driving improvement. 

2. Quality Data Starts with Effective Data Collection

Bad data in, bad data out. Having the right processes in place to ensure that the data that you collect is being stored in the right place, using the right format can be more difficult than you’d initially think. 

Take phone numbers for instance. 

You might collect customer phone numbers on many different forms. Contact forms. Checkout forms. Lead capture forms. All of that data ends up in the same place — your CRM. 

But you don’t have any control over how a prospect might format their phone number when they input it into the form. There are many different ways a phone number can be formatted:

  • 123-456-7890
  • (123)-456-7890
  • 1-123-456-7890
  • 1234567890

This same issue applies to almost any field in your customer database. Addresses need to be formatted consistently. First names should be properly capitalized. Last names too, and without added suffixes. Emails need to be properly formatted. 

So how do you fix this? With proper form validation. When you mandate that data is input in a certain way, or use tools to clean and standardize data before it hits your CRM, you can greatly reduce the amount of “bad” customer data in your CRM. 

The same validation or data cleansing practices need to be installed across all channels through which customer data goes into your CRM. 

3. Understand the Reasons Behind Data Errors

In many cases, there may be a very identifiable reason behind many of the errors in your CRM. Commonly, companies will import data from a variety of sources. 

They have their automated data collection channels like the contact form on their website. Then there will be other sources as well. Purchased lead lists. Data from other platforms. Offline customer data. 

Every customer data source that you use to feed your CRM increases the chances of misalignment and data errors. Different platforms may format phone numbers or addresses differently. Some sources may deliver data that others don’t.

So identifying the misalignment between your CRM data and data from outside sources can open your eyes to how so much incorrect data is making its way into your system. With that understanding, you can take steps to clean or fix the data prior to import. 

The best way to keep your CRM data clean is to make sure that improper data is never imported to begin with. 

4. Train Your Team 

While a large portion of your bad data will come from customer input, don’t underestimate how much your team might be contributing to the problem — especially if they haven’t been trained in basic data management principles. 

A little education can go a long way when it comes to improving data quality. 

This is especially true for employees that are involved with inputting data, but not utilizing the data. There will always be an unawareness around the different ways the data is used and how even small formatting differences can have a huge impact on your team’s effectiveness and processes for certain tasks. 

Standardize how you want data collected and format. Teach those standards to your team. Your employees won’t make data quality a priority on their own. That is often something that needs to be conveyed from the top down. It’s worth it too, as data quality has such a profound effect on customer experience. With 86% of buyers willing to pay more for a better customer experience, you can’t undervalue its importance. 

5. Regularly Clean, Standardize, and Maintain Your Data

Even with a well-designed data preparation system in place, you are still going to have some bad data slip through the cracks. It’s simply impossible to account for all of the different ways that incorrect data can present itself. This is especially true in larger companies with tens of thousands of customer records and many data sources.

You’ll need a system for cleaning, standardizing, or maintaining your data. Of course, you could enlist data entry staff to help you correct mistakes by hand — but that would be time-consuming and lead to more data input errors. Employees can waste up to 50% of their time dealing with mundane data-quality related tasks.

You could also use complicated Excel VLOOKUP functions to help you suss out bad data and correct it. Even then, you’ll still have to do a by-hand check or else a decent amount of bad data is sure to slip through the cracks. 

Instead, invest in a customer data management tool. Using a tool like Insycle, you can merge duplicate records, clean incorrect data, and standardize any field in your CRM for consistency. Insycle also allows you to create data cleaning filters which can then be automatically run on an hourly, daily, weekly, or monthly basis — putting your customer data cleaning on autopilot.

Invest in the Right Tools

Ultimately, your data quality will reflect the investment (both in time and money) that you put into it. By making customer data management priority, investing in tools, and taking the right preventative steps — you’ll be able to quickly cut down on the amount of bad data that hits your CRM.

 

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Ryan Bozeman

Ryan Bozeman

Ryan Bozeman is a content marketer who loves helping companies tackle their biggest data problem. Insycle is a complete data management solution that makes it simple to manage, automate, and maintain clean databases.

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