Bloated CRM data means data that is falsified, inaccurate, and inflated. Unfortunately, most organizations have flawed CRM data numbers that they only discover when they audit the data.
In one such instance, a customer we were working with discovered they had only 32 thousand records instead of the supposed 70,000 records! Imagine discovering this unnerving reality – you believe you had 70K records but in actuality, you only have 32K! The customer to pause all their business activities and have a sit down with their marketing and sales team to discover what went wrong. Ironically, the marketing and sales teams refused to believe the statistics of the report (which the customer obtained after using our data profiling feature) and downright denied the false records. Obviously, this discrepancy not only caused organizational conflicts but also affected the revenue streams and profitability of the business!
So how does bloated CRM data occur? Let’s find out.
Why is Bloated CRM Data a Consistent Problem?
There are multiple reasons why CRM data is never reliable and is almost always bloated. Don’t believe us? Check your CRM records for the past 6 months. Can you identify the number of duplicate, incomplete, or empty records that exist? All these unusable records not only take up valuable storage space but are useless in their efficacy.
For example, you want to run a direct mail campaign, but how do you do that when 40% of your data does not have valid addresses?
Bloated or inflated CRM data, therefore, is a consistent problem that affects all areas of your business processes. You cannot use flawed data for accurate insights, predictive analytics, or any ML/AI projects.
Some of the key causes for bloated CRM data include:
Bad Data Capture Forms
Most companies collect CRM data through web forms. These forms seldom have data quality control parameters. For example, a customer could enter random emails, or phone numbers and can still sign up. Most don’t even have controlled fields for entering address data.
Here is an example of bad data capture:
Image credits: OCR GCSE ICT Data Capture Methods
With forms like these, you are bound to get dirty data – and – duplicate data! Imagine having dozens of records of Bruce Summers with multiple emails and phone numbers. How do you ensure only one phone number or email address is associated with an individual? Through quality control parameters.
Good data capture should have:
✅ Data Validation Rules: Ensure that only valid and correctly formatted data can be submitted. For example, use regular expressions to verify email addresses, phone numbers, or dates. This helps to prevent incorrect or incomplete data from being entered.
✅ Unique Identifiers: Include unique identifiers in the form, such as customer IDs or account numbers, to avoid duplicate entries. These identifiers can be used to check for existing records and prevent the creation of duplicate entries in the database.
✅ Real-Time Data Lookup: If applicable, integrate the form with existing databases or systems to perform real-time data lookup. For example, if you have a customer database, the form can check if a customer already exists based on their email address or name. This can help prevent duplicate entries and ensure data consistency.
✅ Cascading Dropdowns: Use cascading dropdowns or dependent fields to streamline data entry and reduce the chances of errors. For example, if the user selects a specific country, the state/province dropdown will only display options relevant to that country. This minimizes the possibility of inconsistent or incorrect data being entered.
✅ Conditional Logic and Skip Logic: Implement conditional logic and skip logic in your form to tailor the user experience based on the respondent’s previous answers. This helps in streamlining the form-filling process and ensures that respondents only see relevant questions.
Below is a simplified example of a form table with five data capture fields, each demonstrating the form practices discussed earlier:
| Field Label | Field Type | Data Validation / Logic | Description |
|---|---|---|---|
| Full Name | Text (SingleLine) | Required Field | User must enter their full name. |
| Email Address | Text (SingleLine) | Required Field, Email Format Validation | User must enter a valid email address. |
| Phone Number | Text (SingleLine) | Required Field, Phone Number Format Validation | User must enter a valid phone number (e.g., +1 (123) 456-7890). |
| Country | Dropdown | Required Field | User must select their country from a list of options. |
| State/Province | Dropdown | Required Field (Conditional on Country Selection) | User must select their state/province based on the country selected in the previous field. |
Better data capture practices reduce CRM data bloating and duplicates.
Lack of Regular Data Audits
Over time, CRM systems accumulate vast amounts of data from various sources, including customer interactions, leads, and marketing campaigns. Without periodic audits to cleanse and validate this data, irrelevant, outdated, and duplicate entries can proliferate, resulting in a bloated database.
You can face a plethora of problems if you don’t audit your data! From conflicts between team members to deliberate foul play, from poor organizational performance to loss of actual revenue, and even the risk of legal suites, you can face unprecedented challenges!
Why put your organization through these high risks when auditing data is far easier than living with bad data?
You can literally take out just one week to work on the most basic data audit processes. Here’s a quick five day plan on auditing your data and establishing some ground rules!
Day 1: Define Audit Objectives and Scope
- Identify the key data quality issues you want to address, such as duplicates, incomplete records, outdated information, or inconsistent formatting.
- Determine the scope of the audit by specifying which CRM modules, data fields, or customer segments you’ll be assessing. This will help you manage the audit within the one-week timeframe.
Day 2: Gather CRM Data and Sample
- Collect the CRM data from relevant modules or fields that fall within the defined scope. Ensure you have access to a representative sample of data for analysis.
- Organize the data into a format suitable for analysis, such as a spreadsheet or database table.
Day 3: Assess Data Quality
- Start analyzing the CRM data sample to assess its quality. Look for duplicates, missing values, incorrect formatting, and outdated records.
- Utilize data validation techniques and comparison checks to identify anomalies and inconsistencies.
Day 4: Cleanse and Correct Data
- Based on the audit findings, initiate the data cleansing process. Remove or merge duplicate records and validate data formats.
- Update incomplete or outdated information wherever possible. Implement standardization rules to ensure consistency.
Day 5: Establish Data Governance Practices
- Develop a set of data governance practices for ongoing data maintenance. Define data entry guidelines, assign data ownership roles, and establish data update processes.
- Document the steps taken during the data audit and create a plan for regular data quality checks in the future.
Day 6-7: Review and Finalize
- Review the results of the data audit to ensure accuracy and completeness.
- If time permits, conduct a second round of data validation and cross-checks to verify the effectiveness of the cleansing and correction efforts.
- Finalize the data audit report, outlining the key findings, actions taken, and recommendations for continuous data quality improvement.
P.S: You can perform these steps faster if you use a no-code solution like WinPure that allows you directly import, integrate, and clean the data. Watch the video below to see how this is done.
Poor or the Complete Lack of Data Management Knowledge
For decades now, data has inaccurately been attributed as an IT asset – therefore any decision or knowledge about data management would be handled by IT teams.
Things have changed.
Business users are the true custodians of CRM data. This means they need to know how to clean, process, and make this data usable. They also need to be accountable for the integrity of this data.
The only way to involve business users is by implementing company-wide data training sessions where business users are trained on the basics of poor data or dirty data. Additionally, they should be empowered with tools and solutions that can help them handle data challenges without having to rely on IT teams or third-party consultants.
When your business users begin to understand the importance of data quality, you will see an increase in operational efficiency and better use of data in downstream applications.
What are the Consequences of *NOT* Addressing Poor Data Quality?
Bloated CRM data can have various negative consequences for a business. Some key consequences incldue:
❌ Reduced Data Accuracy: Bloated CRM data often contains duplicates, outdated records, and errors, leading to reduced data accuracy. Inaccurate information can misrepresent customer profiles, purchase histories, and interactions, which can result in misguided marketing efforts and ineffective customer service.
❌ Impaired Decision-Making: When CRM data contains inaccurate or irrelevant entries, it can hinder informed decision-making. Business decisions rely on accurate insights derived from CRM data. Bloated data can lead to erroneous conclusions, potentially leading to costly mistakes or missed opportunities.
❌ Wasted Resources: This data takes up valuable storage space, leading to increased infrastructure costs. Additionally, employees spend extra time sifting through irrelevant or duplicated data, diverting their efforts away from productive tasks. The resources spent on managing and maintaining bloated data could be better allocated elsewhere.
❌ Poor Customer Experience: Poor data leads to poor customer service issues and a poor customer experience. Duplicate entries may cause customers to receive repetitive communications, leading to frustration and potential damage to the brand’s reputation. Inaccurate data can also result in offering irrelevant product recommendations or services to customers, undermining personalized customer interactions.
❌ Compliance and Security Risks: A bloated CRM database may contain outdated or obsolete customer information that is no longer needed. Retaining unnecessary data can pose compliance risks, especially with data protection regulations like GDPR or CCPA. Additionally, having more data increases the surface area for potential data breaches, exposing sensitive information to security risks.
To mitigate these consequences, businesses should regularly audit their CRM data, identify and remove bloated entries, and implement data governance practices to ensure data accuracy and quality over time.
How Does WinPure Help?
WinPure helps you profile your data to identify errors. The customer in this case study only discovered they had tens of thousands of unusable records after they used WinPure’s data profiling and data matching features.
Watch this video to see statistics that reveal the truth about your data.
Want to know how we can help?
Get in touch with us!
