Is your data constantly encountering errors? Here are five ways to improve its quality

Marketers today are spoiled for choice when it comes to the channels available to reach and engage customers — with many now using LinkedIn, chatbots, Pinterest, YouTube, and even TikTok to capture attention.

While this has unlocked an abundance of new opportunities for marketers, the more platforms businesses use to interact with customers, the more likely errors will occur when entering data into their CRM system. 

Poor quality data cripples revenue and business growth, losing companies millions of dollars each year. In fact, according to a new report by Validity on CRM data management, 42% of Australian respondents estimated that their company loses over 10% in revenue due to poor quality data. 

So why does this happen? Within businesses, there’s often a misalignment between departments regarding who is responsible for the quality of data as it goes into a CRM system. 

While everyone in an organisation should do their part to help maintain CRM data quality, the onus typically falls on the CRM admin to champion the program.

To ensure success from the outset, CRM admins should start by combining the right data quality tools with a strong strategy to implement them.

This generally involves devising and enforcing quality standards, providing training to all users, delivering ongoing data monitoring and cleansing, and being hyper vigilant in maintaining open lines of communication with all CRM stakeholders.

Anyone who has maintained even a small database knows that data quality deteriorates quickly.

And despite a CRM administrator’s best efforts to prevent mistakes, they can happen.

But with the right preparation and execution, all CRM users can avoid finding themselves in a data dilemma.

The importance of monitoring

No matter the size of a database, the data housed within it decays quickly. In fact, 79% of respondents agreed that data decay accelerated at an unprecedented rate as a result of the pandemic.

Why? This is largely a consequence of the major shifts in the way people were forced to work and live amid the pandemic. In a relatively short period of time, a huge number of people changed jobs, home addresses and even work locations. As a result, a lot of their existing data —such as phone numbers, office addresses, email addresses and job titles — was rendered obsolete. Additionally, when CRM admins leave jobs often their knowledge of how to integrate clean, accurate data, monitor the system and remediate issues is lost to the next user that will fill the role. Making an already complex process even moreso.

But data decay occurred well before the pandemic and the Great Resignation. This is because mistakes can happen very easily and often monitoring for those mistakes falls off the radar. With existing records, users may accidentally replace valid information with erroneous data, or simply change or delete information by mistake in human error. On the other hand, new records — whether imported directly into the CRM or entered manually — will inevitably include a number of errors, including data that is entered incorrectly or fields that do not map to the original framework and therefore throw off the entire look and efficacy of the system.

When users can easily view and edit accurate data, their jobs are made easier and they’re more likely to trust and value the CRM system they have in place. Which is why ongoing monitoring is so important.

Prioritise standardising

Even the most superior CRM monitoring processes can be undermined by poor standardisation. It is therefore vital that data administrators devise a quality standard that clearly classifies ‘low-quality data’ from ‘high-quality data’.

This standard is really a collection of rules or tests that, when applied to the database, identify bad data and, in some cases, automatically rectify it. It is preferable that these standards help to mitigate against bad data entry from the outset, which is possible if CRM admins establish clear guidelines around using abbreviations and set rules for formatting addresses, states, postcodes, and names, among other information. To ensure data quality is well maintained, admins should regularly revisit their standards to see if improvements or updates can be made as their priorities and dataset evolves.

Avoid duplication

Duplicate CRM records are painful to witness and even worse to deal with — they cause confusion, waste time, and make it tricky for users to get a true, holistic view of a customer relationship.

While it’s possible to manually find and correct these records, users will achieve far better outcomes using a duplicate prevention tool. Effective solutions will automatically search for duplicates in a database and create scenarios to tackle them. Examples include automatically merging them, removing them entirely, or sending alerts to relevant users to flag them.

Validate, validate, validate

The world is ever evolving, and people are always on the move, therefore contact information that was valid only a few months ago, could be redundant today.

Administrators should introduce processes that guarantee data is relevant and timely, and where possible, verify contacts against trustworthy outside sources. An easy way to ensure that email addresses remain valid is by using an email verification solution that automatically does this for you.

Once all the aforementioned tasks have been performed, admins should verify that records have been properly updated and that they adhere to the required quality standard. From here, the whole organisation will be empowered to make smart decisions that are backed by reliable data.

Educate and enforce

Your data quality efforts will be futile if nobody understands or follows them. Educate users and leaders on the importance of upholding good data quality practices and provide proper training. Regularly communicate any changes or updates to teams and foster a supportive environment where users feel comfortable to ask questions and flag issues.

While businesses would like to put a band-aid on data quality issues and instead focus on innovating new products and services, the reality is that data is your business’s most valuable asset and should be protected and treated as such.