Customer Relationship Management is a tool that collects, organizes, and analyses customer data to understand better the customer and how best to reach out to them. With the correct data, CRMs can improve workflows, increase customer loyalty, and boost profitability.
It’s essential to regularly clean your data to ensure that the information used by your CRM system for analysis and insight creation is accurate.
Here are some tips for making the process easier.
Why is it essential to have a CRM that’s “clean”
A CRM has many purposes. It can be used to gather, segment, and organize customer data. It also optimizes sales funnels and streamlines customer services.
The quality of data is crucial to the success of a CRM. Clean data allows it to run faster and more effectively. It results in:
Customer satisfaction can be improved
Better decisions
Cost Savings
As Forbes notes: As Forbes notes:
When sales and marketing professionals are drowning in dirty data, they can’t make data-driven decisions. Only 33% of marketers believe they can make informed decisions based on CRM data. The U.S. economy loses approximately $3.1 trillion annually due to poor data quality. “As the old saying goes, “garbage in, garbage out.”
For your CRM system to work at its best, and for the various tools it offers – especially those that are automated – they need fresh data. Like a supercar that needs premium gas, CRMs require high-quality, current data.
What is dirty data
Businesses use a variety of channels to collect customer or potential customer contact information, including:
Storefronts
Face-to-face contact through sales teams
Websites
Mobile websites
Mobile Applications
Catalogues
Orders can be sent by mail
Call Centers
Human error is inevitable when information is manually entered. The same applies to automated data entry and collection, but it happens less often. Machines can’t tell if the customer has entered incorrect data.
It is more common than you may think to have inadequate data.
Experian Data Quality Survey, which surveyed more than 1,200 companies from various industries and sizes, found that the average company believes 22% of their contact information is inaccurate.
How can data be bad
This can happen in four different ways.
Outdated Data- Customer Information decays with time. Information provided by a customer a year ago may not be accurate today. They may change jobs, get new phone numbers, or even their email address. Data Axle estimates that three accounts for approximately 4% of mailing list addresses. This results in an annual waste of $180,000 on undeliverable posts.
Duplicate Data- When there are duplicates, data can be contaminated in many ways. Even though the data might be accurate, duplicate entries can cause your CRM to miscalculate or result in double contact. This can be caused by:
Manual error
Merging of lists
Faulty Customer Relationship Management Software
Incorrectly formatted data_ The order and way in which data are input can be in any combination. You could enter the first name and last name followed by contact information. Or last name, first name, contact information. So on. If data is entered manually, there’s a chance that employees will fill out forms differently.
Customers enter their data incorrectly. Similarly, customers may misspell or enter the wrong information when asked to complete online forms.
How to clean up your data
Insufficient data can be expensive. Harvard Business Review estimates it costs the U.S. over $3 trillion annually.
“Bad data is expensive because decision-makers, managers, knowledge workers, data analysts, and other professionals must deal with it every day. It is also time-consuming and costly. Many people are forced to correct errors in data when they have a tight deadline.
It doesn’t have to be you. There are steps that you can take to optimize your system and save money.
Here are some simple tips for cleaning up CRM.
Standardize Data
Take steps to reduce the number of wrong data or at least slow down its accumulation.
Standardization of data is the answer.
Momentum data states: 5 If concrete and strict rules are not implemented, employees may input data in an unsymmetrical manner that is difficult to align. This becomes increasingly relevant as databases grow since data standards are easily lost.
You can replace fragmented methods with good ones by establishing rules and systems to collect and enter data in the CRM. It’s impossible to eliminate all wrong data inputs, but by implementing proper data cleansing and form validation processes, you can reduce the frequency of it.
Fix small formatting issues
When it comes to data, minor issues can cause significant headaches–especially when there are thousands (or millions) of data points.
Consider capitalization. Some people don’t capitalize their first or last name when filling out forms; others will capitalize it fully. It may not seem like an issue, but when you send out marketing emails and miscapitalize the recipient’s name, it can take away the personal touch.
A common problem with manual data entry is zip codes starting with zero. Thomas Bonneau, GB Sterling:
If you have an Excel data file containing the zip code, the Column, and the Column is formatted correctly (Special Zip Code/Text), everything will look good if the zip has a leading zero. 02739). When you save the file as a CSV and import it into your CRM, then reopen it to edit it, the leading zero will disappear because the CSV ignores the prior Excel formatting preserving the zero.”
These seem like minor errors, but they can greatly impact your bottom line, as these mistakes waste time and resources. You can save time and money by addressing these problems before they are imported into your CRM.
Purge Duplicates
Even if you are a loyal client, no one wants to receive a second marketing message from a business. It begins to feel like spam.
Hand purging is possible if your list of customers is not long. Do it regularly to prevent the problem from growing. Automation can help if your business is more significant. Many CRM systems offer automated features that allow you to create rules and conditions for detecting duplicates.
Set your system so that it automatically blocks duplicate content.