Bad CRM data. 

It happens to everyone. But why?

Let’s explore how bad CRM data happens to good teams—and how to fix and prevent it.

What is “bad data” and why does it happen?

What exactly is “bad data” in the context of your CRM? Defining bad data can seem like a rather subjective exercise. That being said, from a marketing consultant perspective, bad CRM data usually comes in one of the following forms:

Errant records

Example: A lead record contains the wrong email address and/or phone number.

Incomplete records

Example: An organization record contains zero relationship links to related contacts.

Inconsistent records

Example: Some lead records use proper capitalization, while others are all lower case.

Overlapping records

Example: One customer relationship is being tracked across duplicate contact or organization records.

Valueless records

Example: Your company runs an online ad campaign that generates dozens of non-business email addresses that are clearly fakes.

As illustrated by the previous examples, bad data does not originate from a single source. Rather, bad data usually creeps in over time as a result of lax business processes, broken systems and integrations, and subpar decision-making. In short, bad data comes in many forms and from many places, which is why fixing it can be such a challenge.

Identify good data sources, identify the biggest data issues, and fix all data management process that contribute to bad data.

Fixing bad CRM data

There’s not a magical solution for fixing bad CRM data. There are, however, several steps that you can take to improve your situation:

1. Identify good data and its sources

Before you get bogged down with negativity, it may be wise to first identify the success stories in your CRM. For example, is there a specific advertising campaign that consistently produces large amounts of highly qualified leads with zero (or minimal) data discrepancies? Or, perhaps the use of drop-down menus has streamlined data entry and minimized oversights. Carefully study what works and plan to do it more in the future.

2. Identify and fix largest sources of bad data

We’ve already established that bad data comes from countless sources. But, is it possible that a few sources are responsible for the largest chunk of your problems? If you struggle with duplicate records, perhaps your web-to-lead integration is misconfigured and requires an adjustment. Or, perhaps your CRM administrator does not understand how to properly import trade show attendee lists and could benefit from additional training. Spend time investigating the situation and look for easy fixes that could eliminate hundreds or thousands of issues with minimal effort.

Fixing the largest bad CRM data sources is a quick and easy way to stop bad data from creeping in over time.

3. Identify and fix less frequent sources of bad data

After correcting the largest issues, it’s time to move on and address the myriad of other less obvious causes of bad CRM data. Here are just a few examples and fixes:

Sales reps don’t have time to worry about entering clean data: Sales reps are very busy people and not everyone is always “detail-oriented.” On the other hand, good data is essential for modern sales teams. Data integrations can simplify the collection of key business information, thereby freeing up sales staff to focus on what they do best—sell.

Customizations are out of control: There are many stakeholders to keep happy when your CRM is your central source of truth. Sales reps want to know everything possible about their leads and contacts. Support agents need a way to track customer satisfaction and prevent churn. Accounting wants the ability to flag problematic accounts. These wants and needs can often manifest themselves as CRM customizations, which can clutter your CRM with unused or misused data fields. Unused and misused data fields lead to bad data. Therefore, be strategic and selective when agreeing to implement a stakeholder’s request for CRM customization. Consider all possible use cases of a customization feature and find a solution that will stand the test of time. 

Lack of structure for free-form text fields: VP of marketing. VP Marketing. Vice President, Marketing. These variants essentially mean the same thing, but, when expressed differently, can create confusion and muddy your reporting and segmentation data. Look for ways to standardize the assignment of job titles or consider using tags to categorize contacts by persona.

Deduplication is too complicated (or risky) to mess with: Deduplication can seem like a scary thing, especially when you do not have a formalized lead disposition process. In reality, deduplication prevents staff from wasting time by keeping your data clean. Deduplication workflows vary by CRM provider, but, if you’re an Insightly user, be sure to check out the SmartMerge guide

No one is validating the data: Banks hire auditors to ensure their financial data is in compliance. Your CPA reviews your business and personal financials to help you file an accurate tax return. But, who is auditing your most valuable business asset, i.e. your CRM data? Ongoing data validation in your CRM is a key step for maximizing business insights and identifying new sources of bad data. 

Here are a few tips for setting up proper data management processes.

Preventing bad CRM data

In addition to ongoing data validation processes, what other steps can you take to prevent bad CRM data? Here are a few ideas:

Rely on data-driven indicators, such as MQL-to-SQL: What percent of your MQLs (marketing qualified leads) are actually accepted by sales? If the number is low or on a downward trajectory, you may be dealing with an underlying data issue. Does sales have the information it needs to accept a certain type of lead? Is marketing attracting leads that are in the wrong stage of the buyer journey? Sales and marketing metrics, such as MQL-to-SQL ratios, can serve as leading indicators of data-related issues before they become bigger problems. Check your lead disposition process. Use Insightly’s quick guide to learn how to set up and/or improve your lead disposition. 

Leverage AI in your CRM: It’s easy to get overly excited about your CRM’s sales and marketing features at the expense of lesser-known technical capabilities. For example, Insightly users will be glad to know that their CRM automatically checks for duplicates each time contacts are added or a data import is performed. Be sure to fully understand and use your CRM’s duplication prevention safeguards.

Frequently reinforce data cleanliness: Your teams can play a pivotal role in maintaining data cleanliness, but only if they understand how to do so. Create standard operating procedures for entering data into your CRM and distribute them to staff. Keep these procedures up-to-date and require managers to ensure their staff have read and understand them. When you hire new staff, make the CRM data management training a mandatory part of onboarding. Look for other ways to make data and data cleanliness an essential part of your company culture.

Customer data is a key building block of your business, take good care of it.

Out with the bad

No doubt, bad CRM data is a real problem for businesses of all sizes. However, with the right approach, it’s a problem that can and should be resolved. By proactively rooting out bad data and implementing sound business processes, you and your team can maximize the usefulness of a CRM and, as a result, make better decisions, align teams, and grow business faster. 

Learn more about the importance of proper customer data management and pick up a few best practices from Insightly’s data management blog series:

To learn how Insightly CRM can help you solve your customer data management needs, request a demo.

 

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