What is CRM data maintenance and how it affects B2B marketing

The quality of your CRM data affects your entire company from the bottom up.

Your marketing teams rely on quality data to segment contacts, personalize messages, and create targeted campaigns.

Your sales teams need accurate data to address your prospect’s biggest concerns.

Your customer support team needs accurate data for context in conversations with customers. Finance teams need accurate customer data for forecasting. Even your executive team relies on accurate CRM data to make strategic decisions.

Most organizations know that. Still, bad data costs US companies up to $ 3 trillion a year, and up to 60% of companies fail to calculate the real cost of their bad data.

This signals that there is still a lot of room for improvement in data maintenance in many companies. Businesses of all sizes would be affected by so much inaccurate data in their customer database, although they may not be aware of how painful the impact can be as much of the everyday problems remain under the radar.

So much “bad” data is a big problem for your marketing teams in particular. How do you address your customers’ biggest concerns when you can’t be sure who they are and what is important to them? You need accurate, reliable data to be able to rely on what you say.

Organizations today often rely too much on manual labor to troubleshoot data issues, which can be extremely time-consuming and stressful for your teams. Relying on your employees to export data, correct it with complicated formulas in Excel, and easily import it back into your CRM is a big challenge.

Let’s look at how poor data quality affects your marketing teams, slowing them down, and giving them less creative options when launching new campaigns.

How data quality affects your marketing efforts

While the effects of poor customer data quality can be felt across your organization, it is particularly fickle on your marketing teams.

Everything a marketing team does – every strategy deployed, campaign launched, messaging delivered, and creative production – is influenced by customer data. Or at least it should be.

That’s what great marketing teams do – understand their customers and speak to them directly in a way that resonates. You can’t do that if you don’t know them, and you can’t be sure that you know them if you can’t rely on your data.

Let’s look at some of the specific ways data problems and poor quality data can affect your marketing teams.


A big part of any marketer’s job is segmentation. Or the practice of analyzing long lists of customers and breaking them down into smaller lists so that you can more confidently address the concerns of each segment.

You wouldn’t market your B2B software product the same way to both CEOs and marketing managers, even though both of them may be targeted buyer personas for your product. They have different needs and concerns. If you try, the language you are using will never completely match either.

So you break things up. They make the list of people you talk to smaller and more manageable. Then you can use a specific language that resonates with that segment. However, if your data is not reliable, you cannot effectively break it down into these smaller groups.

Marketers cannot properly segment contacts with inconsistent data. If there are inconsistencies, creating even simple campaigns becomes a complicated analysis that requires experts who understand all the nuances. As a result, it prevents marketers from creating effective campaigns and hinders their ability to execute quickly.

Let’s look at an example. For example, let’s say you’re a B2B software company and you want to send an email campaign to CEOs in your HubSpot CRM.

If you don’t regularly standardize and format your job title field data, CEOs will be listed in your database in many different ways:

  • CEO
  • CEO.
  • Managing Director
  • founder
  • founder
  • owner and manager
  • Etc.

And there will likely be many other variations as well.

To run a thorough campaign, you need to bring all of these different job titles together as they are practically all the same title. To do this, you either need to run some creative Excel formulas, create complicated search filters to “catch” all the relevant titles, or get the help of a developer. Either way, you are still unlikely to see every single mistake in the field.

This doesn’t even include typos and other errors in your data. Some people may be listed as “CEOs” or have job titles that contain other data issues. And these standardization and data quality issues can potentially affect your entire database.

For example, this standardization problem would affect not just CEOs, but every job title in your database. Or how about if you want to segment your CRM contacts by city, country, area code or years of experience? There are data problems in every area.

Each data point in your database has a myriad of potential problems that could affect your ability to segment your contacts and deliver effective campaigns that meet your KPI goals.

Data problems make your segmentation efforts complicated and unreliable. Ultimately, your marketing teams will be forced to segment less and less creatively until the issues are resolved.


Data problems also affect your ability to personalize your messages. And personalized messages are critical to successful campaigns.

80% of consumers are more likely to buy a brand that offers personalized experiences. 72% of consumers say they only use personalized messages.

Your ability to personalize messaging is critical and relies on high quality, consistent data in your CRM. Have you ever received an email and your name was not capitalized or was incorrectly referred to by your last name?

By nature, you probably know this is simple data oversight. They didn’t want to address you by your last name. But it still affects how you feel about the company in question, doesn’t it? Maybe it’s not intentionally rude, but it is unprofessional to keep your customer data in a mess.

And it’s not just about {FirstName} or {JobTitle}, although those are important. True, deep personalization may not be as directly related to the data, but it does use inferences from that data to guide your messages.

For example, a common personalization problem that arises from CRM data problems comes from associations. In HubSpot CRM, your B2B contacts are linked to companies.

If that association were absent and some of your contacts were floating, it would be impossible to run account-based marketing strategies. Additionally, it becomes difficult to personalize the message based on the account’s engagement when data is missing.

Inconsistent assignments also contribute to inaccurate lead scores in account-based marketing. Because account-level ratings are applied based on variables for the independent contacts within the account, missing contacts affect account ratings. Ultimately, the difference in lead scoring can impact the life cycle stage of the entire account, slow its movement through your pipeline, and potentially cause a deal to fail.

Customer experience

Segmentation and personalization issues ultimately affect the customer experience throughout their customer journey. With less specific marketing messages that get less resonance, their experiences and opinions about your brand will suffer.

92% of marketers see personalization as a “crucial” element of the customer experience. And personalization often depends on your ability to effectively segment customer data to deliver relevant news. All of these effects are interrelated and affect your overall marketing operations.

For example, duplicate data poses a customer experience issue that can potentially damage your brand reputation. If you don’t regularly merge duplicates, many of your customers will receive your messages multiple times. This drives up the cost of your campaigns, damages your brand reputation, and makes your reporting less reliable.

Deduplication helps achieve a single customer view when your data about your contacts and accounts can be reliably found in one system. With a single record of truth, your marketing teams can effectively segment and personalize communications. A single customer view gives your teams confidence in your data and allows them to focus on other areas.

The quality of your data affects your customers every step of the way. Without reliable data, each of these points of contact will be cheaper. Less or less reliable data limits what can be used and what your teams know about each contact. That adds up over months and dozens of points of contact.

The only way for companies to address these issues is to identify and implement a data management strategy and regular CRM data maintenance.

What is CRM data maintenance?

CRM data maintenance is the ongoing process of reviewing your CRM data, identifying problems, and fixing those problems in your database.

The broader process of maintaining your CRM data can be broken down into numerous focus areas, including:

  • Data quality
  • Data cleansing
  • Data operations
  • Data deduplication
  • Data cleansing
  • Data monitoring and KPIs

Data quality

Data quality refers to data that is accessible, consistent and relevant. Your entire organization is influenced by the quality of your data – from individual campaigns to major strategic decisions.

Accessible not only means that the data is correct, but that the right people in your company can access it when they need it. Isolated data creates bureaucratic redundancies that slow down your business.

Data consistency essentially refers to how consistently data is formatted and standardized in your database. Are your phone numbers formatted in the same way? Are your job titles standardized? Are your contact names capitalized appropriately? Consistency allows you to slice and dice data in interesting ways.

Then there is relevance. It doesn’t matter if you have a million perfectly accurate records in your CRM if none of them are in your target market. The data you collect must be relevant to be useful.

The data quality is achieved through other data maintenance processes such as data cleansing.

Data cleansing

Data cleansing is the process of correcting or removing incorrect, incorrectly formatted, duplicate, or incomplete data in your CRM.

  • Fixed issues with capitalization of first and last names (jane vs. Jane)
  • Standardization of addresses and telephone numbers (1234567890 vs 123-456-7890)
  • Standardization of job titles (CEO vs. C.E.O vs. Chief Executive Officer)
  • Remove redundant data
  • Remove false and falsified data
  • Remove special characters
  • Identify and fix marginal issues

The process of data cleansing can be time consuming. Often times it involves breaking out parts of your database and assigning fixes and tasks to members of your team. Then they load the data into Excel and use VLOOKUP and complicated formulas to identify and fix errors in your data. When done, the data must be imported back into your CRM.

It’s an imprecise process. If you don’t have a true Excel assistant on your team, you are likely to overlook a lot of issues and still need ongoing developer help to update data in bulk.

Data deduplication

All companies deal with duplicate data. Duplicate contact or company records can be created by manual entry, either by your customers in forms or by your team via your backend CRM. Or they can be created through data imports or integrations with other software.

No matter how duplicate records are created, they can be a thorn in the side of your marketing team.

Duplicate data leads to increased campaign costs and a loss of productivity. Your teams spend time ironing out data issues instead of focusing on other areas, resulting in missed opportunities. Every second they spend searching through records to find the “right” or most complete record is wasted time. Duplicate data shakes your single customer perspective as there is no single “source of truth” that can be relied on.

When you have high duplication rates, your marketing teams will always be aware of this fact. They know they need to deduplicate each list of prospects or customers before new campaigns are published, and add a new task each time the campaign starts.

Most critically, duplicate data degrades the customer experience. Not just because they are likely to receive mixed messages and redundant messages. But because your ability to understand them is halved throughout the customer lifecycle, it leads to less fulfilling interactions over and over again.

Data operations

Data operations include the ongoing day-to-day tasks required to maintain your CRM data and ensure the usability of this data in your company.

Data operation tasks include mass updating data on a daily basis, consolidating fields and redundant data, migrating free text fields in pick lists, importing data (from events or third-party sources), and other tasks.

These tasks are a necessity for high quality data and to get your data in a position where data cleansing can be as effective as possible.

Data cleansing

Data cleansing involves removing junk data, outdated data, redundant data, and poor quality data that is only used to clutter your database and negatively impact your reputation and email open rates.

There are many types of data problems that could potentially make records a good candidate for cleanup. Examples include:

  • Undelivered emails
  • Obviously fake data
  • Obsolete records
  • Unqualified prospects
  • Bad records of integrations
  • Incomplete contact details
  • Free and role-based email addresses
  • Not engaged contacts
  • Unqualified contacts
  • Duplicate contacts

Cleansing this data is critical to improving the overall usability of your CRM data. Without having to constantly search and remove junk data for campaigns, your productivity improves.

Without the clutter, you can keep data storage and contact-based CRM fees down, as well as the time your teams would normally spend processing the deleted records.

With no poor quality data affecting your email delivery and open rates, you’ll avoid penalties and enjoy an improved reputation for the sender.

Data monitoring and KPIs

To troubleshoot problems in your CRM database, you need to be able to determine where those problems are. Between the various data problems that you find in your database, understanding what those problems are and what kind of problems are there will help you prioritize troubleshooting the most serious problems.

Of course, you can monitor your KPIs and generate reports manually. This includes running reports or exporting data to Excel and analyzing them. However, some tools can automate the diagnosis and collection of KPIs.

For example, the CRM Data Grader is a tool that connects directly to HubSpot, analyzes the CRM database, and uncovered specific issues that you need to troubleshoot. This ensures that you have visibility into the quality of your data and actionable insights into how to fix these issues.

With a clear key performance indicator, such as the percentage of clean records in your database, you can track your progress and quickly assess the overall health of your customer data.

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Differences between data maintenance and standard cleansing projects

Standard data cleansing projects are short term and tactical. When you find a fire, you put it out. Data cleansing projects are reactive because they have to be. Sometimes unexpected data problems can bring things to a standstill and need to be resolved immediately. These needs will always be there, but less often with a data maintenance strategy.

In contrast to one-off cleansing projects, data maintenance is an ongoing strategy. It requires continuous investment and attention, but with the help of modern data management tools, you can automate much of your data maintenance tasks and improve the operations of your teams.

As your customer data grows, managing that data becomes more complicated. It takes more focus and planning to make sure your data is accessible, consistent, and relevant.

Companies usually go through several phases on the way to real optimization of data maintenance:

  1. Undefined and chaotic. No understanding of problems and no processes to deal with them.
  2. Visibility. Knowing about data related problems gives insight into the specific problems in your database, with regular automatic reports.
  3. Standardization. Established data quality standards and coordination between cross-functional teams with regard to data expectations and goals. Standards must be automatically enforced for effective implementation.
  4. Optimization. Use automation to proactively cleanse and maintain data, avoid repetitive manual work, streamline data corrections and collaboration, and warn of exceptions.

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Data maintenance is not something you do once and then never again. This process is something that you have to do over and over again. You need precise documentation and processes in order to minimize your expenditure of time.

New data is constantly flowing into your CRM database, and with that data comes a number of problems and errors that have the potential to slow down almost every team in your company. Tools like Insycle help you review your existing data, identify common data problems, and fix them on an automated, set schedule.

By improving your CRM data maintenance processes, your marketing teams can generate more marketing-qualified leads through improved segmentation, personalization and maintenance.

Quality data means that you can professionally present your brand in all communications with customers while improving their experience throughout the customer lifecycle.

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