Customer data is often a brand’s most valuable resource. Frequently thought of as the “oil of the digital era”, clean, reliable customer data is the key to forming a relationship that extends beyond that of your competitors. It’s the lifeblood of a company, but cleaning dirty customer data and maintaining data hygiene can be a costly and complex task.
Despite the undeniable value of amassing volumes of subscribers, records, and contacts, examples of mismanaged and poor quality customer data surrounds us every day. Enterprise companies such as Yahoo, Under Armour, eBay, and Uber have all experienced data breaches that have damaged reputations and cost millions in settlements.
Large volumes of data are cumbersome to manage at the best of times. And customer data can be downright impossible to manage without the right strategy in place.
Now throw in the threat of increasing regulations and penalties for when it is mishandled, and quality data is suddenly less of a luxury and more of a necessity.
Due to the dynamic nature of customer data, maintaining the quality of this data can feel a little like chasing your own tail. Regular data cleansing, and a sound strategy behind it, suppresses or modifies data that is incorrect, incomplete, irrelevant, or improperly formatted and is fundamental when working with customer data.
3 Steps to Cleaning Customer Data
An ongoing data quality strategy should include processes for cleansing existing data, merging accounts, removing duplicates, and establishing a benchmark in data quality. Creating a repeatable process ensures that accurate and timely data is maintained and minimizes the need for manual, time-intensive processes.
- Standardize customer accounts across multiple CRMs and databases
Before anything can be done with your data, the first step is to ensure its accuracy. To clean existing records, contact data can be “scrubbed” with record handling software that corrects, normalizes, applies standardization to customer details such as address, email, or phone number. Seamless record linking strategies will take on this step inherently in order to ingest the volume and variety of enterprise-level data.
Do this: At this stage, keep an eye out for gaps or inconsistencies in customer information that will help you in merging records and preventing future challenges.
- Merge and unify business accounts & customer records
Businesses today create and depend upon large volumes of data, and each department likely depends on their own segment of data integral for their field. As a result, data within a company inevitably becomes siloed and disparate, and merging quickly becomes complex due to the variety of databases, file formats, structure, schema, and outdated records.
And while joining various datasets seems like a fairly straightforward task at first glance, innumerable inconsistencies and challenges with customer data can make it a challenging one to fully automate.
To ensure a complete, 360-view of the customer—one that is accessible across the organization—software for maintaining quality data must be able to analyze and match with human-like perception so you don’t need to comb through results line-by-line.
To create an even more powerful view of your customer, ideal solutions should apply a hybrid of record matching algorithms, like that of matchit, that mimics an expert human user to add real-time information to existing customer profiles. An intuitive solution like matchit won’t require data that is cleansed, verified, or correctly formatted. However, downstream processes and users may benefit from standardized outputs. Look for a solution that offers a built-in normalization tool to do the heavy lifting in these situations and save on hours, if not days of pre-processing.
- Prevent duplicate records at point-of-entry
Cleansing and wrangling data is an integral part of maintaining a clean CRM, but point-of-entry implementations are integral to keep duplicate records out. Without preventative measures to check dirty data at the door, that freshly-cleaned data will quickly decay to the tune of 30% per year.
Fully embracing data quality means more than simply a one-and-go deduplication process – but an ongoing data cleansing strategy likely involves revisiting legacy technology or systems that can better process and analyze incoming data at scale.
If new systems are required, look for solutions that make data management accessible to everyone, regardless of coding experience, in order to empower and educate users across the business.
As companies look to acquire more automated, AI-driven solutions, customer data is being held to a level of quality previously thought to be unattainable. Now, this optimized, squeaky clean and regulatory compliant level of quality is becoming the expected standard as stringent policies such as GDPR become the norm.
With the right data cleansing strategy in place, enterprises can better lean on the customer data they already own to adapt rapidly-evolving technology at scale, foster new growth and operations within the business, and enhance customer relationships.