Customer relationship management (CRM) systems have transformed, to a degree, the way businesses manage customers’ data. However, quality CRM data remains out of grasp for most businesses. According to Salesforce, 91% of CRM data is incomplete, and of that, 70% degrades and becomes less accurate within a year. Put it another way: “CRM data is a mess,” in the words of Oleg Rogynskyy, the CEO of People.ai.

This costs businesses dearly in many ways. Take money and time, for instance. Bad CRM data costs organizations an average of $15 million annually—or about 10% of annual revenue. And it leads to wastage of 27% of sales representatives’ time. Furthermore, nearly half of sellers say dirty data is the biggest challenge they face.

These are alarming statistics. They should—indeed must—spur anyone to action and begin working on ways to improve their CRM data. Right away. Maybe not that hasty. After reading this guide perhaps.

CRM Data And Its Types

CRM data is, as the full name indicates, any information related to customer relationships and client data you have in a CRM system. Prominent ones include customer contact information, demographic and geographic information, details about customer interactions, their purchase record, and even psychographic details like their interests and values.

These data are a treasure trove of insights that uncover the desires, preferences, and behavioral patterns of customers. They help you understand your clientele at an intimate level, and allow you to anticipate their needs, meet or even exceed their expectations, and cultivate a healthy relationship with them.

CRM data is vast and varied. It may be broadly classified into four types: identity, descriptive, qualitative, and quantitative. Data that identifies a customer—for example, name, email address, company name, phone numbers, social media profiles, etc.—are grouped into identity data. These data do not give much information about the person, and may of themselves be of limited help.

Source: Smart Insights 2024 Research

Descriptive data supplements the identity data. This information helps you understand who your customers are and what interests them at a more personal level. This can be information about their education and job, hobbies and interests, or family details such as whether they are married and have children.

Qualitative data are those that give you insights into customers’ perceptions of your business, their motivations, levels of satisfaction, and complaints they may have or had. Quantitative data pertains to information and facts that can be quantified and objectively measured. This can be information about the amount of time they spent on your website or how many times they visited, the number of purchases they have made, the average purchase amount, the number of times they reached out to a customer service agent, and so forth.

The above classification is informative but it may not help you much in improving customer relationships or increasing sales. For more practicality, you may categorize CRM data based on a few informal classifications, such as the ones below.

  • Customer contact information: This may include names, addresses, phone numbers, etc.
  • Demographic data: This category may include details related to marital status, educational level, gender, age, profession, company, job title, etc.
  • Behavioral data: This category may include the record of customer interactions, purchases, and page views—in short, data about engagements, online or offline.
  • Psychographic data: This pertains to information about a person’s interests, values, opinions, lifestyle, or attitudes.

There are different ways to categorize CRM data, and there are no good or bad ways per se. It depends, ultimately, on what you want to use it for and how granular you want the classification to be.

Source: WinPure

Why CRM Data Quality Cannot Be Overlooked

Data has often been compared to oil, not without justification. But oil is of different types and forms; and so is data. Raw data is much like crude oil. Without refinement (cleaning, enrichment, etc.), it is pretty much useless. The adage thus should be: Quality data is the new oil.

That is because good and accurate CRM data is one of the pivotal determining factors in the efficient and successful operation of a business. There are plenty of reasons. Here are a few.

  • It helps businesses better understand customers. Understanding customers’ behavior, preferences, and interactions, as quality CRM data makes possible, allows companies to tailor their products, services, and marketing strategies to meet their customers’ needs.
  • It enhances customer service. Quality CRM data makes customer service more streamlined and efficient. Customer support agents have detailed information about customers, such as their purchase history and communications history, which allows them to provide efficient and personalized support.
  • It boosts sales. With quality CRM data, identification and targeting of potential target customers is easier and more effective. It can also help you track sales pipelines and discover gaps in and opportunities for sales.
  • It leads to better business decision-making. Quality CRM data casts deeper insights into sales cycles, marketing strategies, and areas of opportunity and where improvements are needed. 

Source: VentureBeat

The converse is also true and is quite obvious. That is bad CRM data can be detrimental to businesses. It can lead to missed sales and marketing opportunities, wasted resources and reduced productivity, and poor customer service, leading to higher churn and stagnation or sliding of growth.

Challenges in CRM Data And Why Quality Issues Persist

Given the benefits and costs respectively of quality and poor CRM data, one may wonder why not enough is done to ensure their quality. However, there are compelling reasons why poor CRM data remains persistently rampant.

  • Outdated data

Data may be gold in terms of its value but it is anything but in how it ages. It decays quickly and becomes obsolete—at a rate of 34% annually. Old information becomes irrelevant and loses viability. This is because, quite obviously, customers may retain their name but nearly everything else about them changes over time. The information therefore unless updated periodically becomes not only less useful but positively harmful to the quality of the data.

  • Inaccuracy and inconsistency

Erroneous and inconsistent information plagues CRM data. Inaccuracies may creep in due to various reasons; human error during data entry and extracting data from unreliable sources are two common ones. It can also be due to failure to capture completely all the necessary information. Inconsistencies may be due to variations in customer names or date and address formats, which leads to confusion—eg. Joseph Fisher, F. Joe; 12/10/22, 10/12/22; 100C Open Street, Open St. 100C.

  • Duplication

Often multiple entries of the same customer occur several times in the system with or without variations. This can happen when a new record is created for a customer whose data is already there, or when data is imported from different sources, or when databases are merged.

  • Data silos

Different departments may store their concerned data in different locations. The data may not be accessible inter-departmentally and even if they are, they may have inconsistent formats or may be incomplete. The systems and data architecture may also be incompatible. The result is fragmented and lower-quality data.

  • Lack of data governance policies

The absence of unclear data governance policies and procedures for data management can impact the quality of CRM data in several ways. Yet a majority of businesses do not have a strong data governance policy, according to a Harvard Business Review report. It can lead to haphazard and inconsistent handling of information and confusion on who is responsible for maintaining and updating CRM data leading to a lack of accountability and neglect.

  • Integrations with other platforms

Your customer data may be siloed or they may be acquired from multiple sources, like billing software, email marketing apps, or customer support systems. This requires integrating the disparate data so that they are more detailed and useful. But the trouble is that they may be inconsistent, of poor quality, incomplete, or outdated. This can lead to discrepancies in the data.

  • Automated data entry

Automation of the customer data entry process has made it much less of a bane than it was. But it has introduced banefulness in other ways. Over-reliance on automation can be counterproductive. Automation systems may misinterpret certain information, incorrectly map data to specific fields, create duplicates, or overwrite existing data leading to loss of valuable information.

These are but some frequently encountered issues that hamper ensuring CRM data quality, and make it challenging to improve it. It is no wonder that CRM data quality is tatty. But where one sees a gorge and takes a detour, another sees an opportunity to construct a bridge and takes up the challenge. And that (the latter) is exactly what we will do.

Methods To Improve CRM Data Quality

The many issues and challenges associated with ensuring CRM data quality also mean that, from another perspective, there are myriad ways to improve it. This for us is good news. It will allow us to improve the quality of CRM data incrementally even if we do not possess ample resources or have the time to follow through with all the methods. And the cumulative effect of the incremental changes will result in a substantial improvement.

Capture relevant and reliable information from the outset

There is a temptation to collect as much data as possible without taking into account their relevance or reliability. But quantity must not be conflated with quality. Large volumes of irrelevant and dirty data can make analyzing them difficult; the enormity of data can become counterproductive. So it’s important to be mindful of what is relevant and separate the wheat from the chaff from the outset.

Starting with relevant information ensures that only pertinent details about customers are recorded, minimizing the chance of erroneous or extraneous data entering the CRM database. This will also reduce the need for extensive and intensive cleansing of data later on.

The initial data serves as the basis for the CRM database. When this foundation is built on reliable and relevant information, it sets a high standard for data quality. This makes subsequent operations and analyses more streamlined, less tedious, and more accurate.

Implement data entry validation rules

Inaccuracies and inconsistencies plague CRM data. Implementing data entry and validation rules helps mitigate these and aids in maintaining and improving the quality of CRM data. It ensures that information is entered correctly and consistently. By incorporating validation rules in the data entry process, you create a structured and reliable framework for capturing and maintaining high-quality CRM data.

This calls for defining data entry standards and establishing clear guidelines for how data should be entered into the CRM system. This may include formats for names, addresses, phone numbers, and email addresses. For example, you can make the ampersand (@) mandatory for email fields.

It also helps to incorporate drop-down menus and picklists for fields where there are predefined options to eliminate errors due to manual entry—Mr., Ms., or Mrs., for instance. Identify the key information you require and make the fields for those mandatory. 

Having a robust set of validation rules helps streamline the process of data entry, standardizes diverse information, and reduces inaccuracies and inconsistencies, making the CRM data more reliable and useful.

Establish feedback loops for verification

It is not always possible to gather all the information we need nor do we need every possible detail. But certain pieces of information render the other details futile. This could be, for example, contact information collectively.

For this and others that are crucial for your purpose, use customer feedback mechanisms to verify and update the information. Ask them to confirm their information during interactions. Besides, have some internal verification processes that outline the methods, assign responsibilities, set the criteria and frequency; and then implement corrective actions.

Enrich CRM data with additional sources

We may have a lot of information about customers—existing or potential—but we always need more. This is especially so because customer acquisition has become more convolved. More than four persons are involved in 63% of sales. This requires targeting customers from different angles, and therefore more data.

Enriching existing CRM data expands their scope by providing a more comprehensive view of customers while enhancing their quality. Enriched data also provides additional criteria for lead scoring and prioritizing leads based on factors beyond the basic information.

Enriching with second and third-party data helps fill the gaps and corrects inaccuracies and inconsistencies in the existing data. This provides a more accurate and detailed customer profile. 

Regular data cleansing

CRM data gets dirty and loses relevance over time requiring periodic cleansing to remain useful. In the case of B2B contacts, the pace and magnitude of data decay are staggering: 70.3% per year according to a report by Gartner. Regular cleansing ensures that the CRM data remains accurate, up-to-date, and reliable.

Cleansing involves correcting inaccurate information, identifying and removing outdated and redundant information, standardizing old data formats maintaining consistency across the board, and/or merging duplicate records. 

As data is gathered most of the time from multiple sources and platforms, duplication, inaccuracies, and inconsistencies will inevitably abound. Cleaning data is thus imperative. It not only helps identify and resolve inaccuracies and deficiencies but also makes the data more useful and improves customer communication.

Segment and tag CRM data

Segmenting and tagging CRM data involves categorizing and labeling customer records based on specific criteria. They make the data easier to read, map, and analyze. They also help enhance customer experience.

Customer data may be segmented according to various criteria, depending on the end goal, such as demographic, geographic, psychographic, or behavioral—or it can be a combination of these, eg, psychosociological.

By grouping customers with similar characteristics, it becomes easier to identify prospects, and prioritize and target specific groups with tailored messages. This will likely lead to increased conversions and customer satisfaction and also better resource allocation.

Regular audit of CRM data

Audit CRM data regularly and review how data is collected, stored, and used. Doing so gives an idea of the accuracy of the data and how often it is updated, its effectiveness, whether there are gaps in how it’s been used, how it can be optimized, and so on. A comprehensive audit helps you make the most of your CRM data.

Auditing helps identify inaccuracies and discrepancies in the CRM data and allows for timely corrections. It ensures that the data is in compliance with current regulatory and privacy laws and facilitates quick rectification in case of violations. Periodic auditing may also help detect unusual patterns in the CRM data which may be indicative of errors, fraud, or security breaches. And it will give you insights into whether your business goals are fulfilled.

How often CRM data is audited varies. Some recommend a quarterly audit, others half-yearly. And some even daily or weekly. However, doing it every so often may not be worth the time invested. At any rate, once a year is due.

Establish data governance policies

A clear and detailed set of guidelines that outline how CRM data is collected, stored, and utilized—in other words, a data governance policy—is key to maintaining the integrity and security of the data. The policy lays out the roles and responsibilities of different stakeholders and specifies who has access to the data and at what levels. The policy should also define the quality standards CRM data must meet, including accuracy, consistency, and completeness, providing a benchmark for assessing and maintaining quality.

A data governance policy promotes accountability and, by limiting access based on roles and responsibilities, reduces the risk of unauthorized access and data breaches. It also ensures that only those with the requisite knowledge and expertise can view and modify the data. A centralized data governance policy also eliminates data silos. As data from different departments are not compartmentalized, it conduces the sharing of insights. 

Process documentation and audit trails

Documenting processes and maintaining audit trails are crucial in ensuring the high quality of CRM data. Documentation of processes enhances transparency and ensures that teams and individuals are accountable for the integrity and accuracy of the CRM data. But for this, it is important that each individual knows the data hygiene processes and practices and adhere to them. Documentation also helps the transfer of knowledge between team members and ensures that inconsistencies are handled uniformly and maintained even as different persons take over.

Audit trails help determine changes and identify records associated with them. An audit trail lets you see when an update is made in the CRM data, what is changed, and to what value. This helps you understand and backtrack the revision. It serves as a valuable tool for conducting root cause analysis in the event of quality issues and discrepancies.

Provide proper training to employee

This goes without saying but it needs emphasizing because everything ultimately depends on the humans in charge. Moreover, humans are often the weak links in improving and ensuring the quality of CRM data. If, for example, there is an incorrect input in the CRM data entry, then quality is compromised from the outset, tainting subsequent processes. 

Training equips the employees with the best practices and helps them understand the ins and outs—not just of CRM data management and governance policies but also of pertinent laws and regulations. And as these constantly change, training must follow—or better still, lead the changes.

Quality CRM Data Is The Magic Key In Modern Business

Many things are required to efficiently and successfully run a business. But few are as integral as high-quality CRM data. Without it, businesses would be operating blindfolded. It is what provides firms with accurate and reliable information about customers, giving them invaluable insights. This enables them to make informed decisions, provide excellent customer experience, and target potential customers with a high degree of personalization.

The rewards are vast and varied—and you know them very well. But, as you also know, there is no reward without effort. And improving CRM data quality requires a lot of it. You’d do well to get CRM data entry services from a third party, especially if you do not have the tools or know-how. Doing so can help you expedite the process of improving your CRM data quality and help you establish a solid foundation. If you do, given you choose a reliable one, then you are more than halfway there.