Poor data quality can have a negative impact on your business. It can make reporting time-consuming and inaccurate, skew decision making, and make the integration of new business systems and software more challenging, to name just a few unwanted side effects. However, poor quality data can also have a damaging impact on your customer relationships – affecting the quality and accuracy of your external communications, creating a negative brand perception, and causing your users to lose trust in you.  

A report by Experian found that, on average, organisations believe a third of their data (32%) is inaccurate in some way. If business leaders don’t trust their own data, why should they expect their customers to do so? Although I suspect the real percentage is much higher in most organisations, 32% equates to a huge volume of inaccurate data with the potential to damage customer experiences and dimmish trust.

Is your current customer data management process working against you?

When customers trust you with their data, they expect to receive relevant, highly targeted, personalised communications in return. Communications based on low-quality data can translate into a bad customer experience, devaluing your messaging and your business’ credibility. This pushes customers to opt-out of future communications – and most likely your products or services too.

A common culprit for poor quality data is duplication. If you have lots of duplicated contacts in your database, you risk sending contacts multiple versions of the same communication. Data duplication tends to cause undesired confusion for your customers.

Blocks with icons represent communication between brands and customers.

For example, you may have a record for a contact that has made an enquiry via the website, classifying it as a lead in your database. The contact may have since then purchased with you via another avenue (e.g. over the phone), creating another record with the same contact details, but this time marking them as a customer.

If you then sent the same customer an acquisition offer aimed at leads, the messaging is not only irrelevant but also confusing and frustrating. Worse still, you might be even promoting a better offer to the one the customer is currently getting from you, which can be quite offensive and cause people to leave your business.  

Your poor-quality data can be working against you, undoing all your hard work and undermining the resource and effort your business piles into building your brand and improving customer service. People are becoming more informed about what it means to have their personal data out in the world. If your data is unreliable and used in a fashion that surfaces that fact, you’ll lose customers.

Practical tips to help organisations overcome data distrust

There are some simple steps you can take to help avoid making these costly data mistakes:

  • Limit your data requests – If you’re using data entry forms on your website, consider whether you really need all the information you’re requesting. Long contact forms can put people off or cause them to enter quick and inaccurate responses that can affect your data quality down the line. Narrow fields down to the name and email address to increase your contact form conversion rates – the detail can always be built up later.
  • Minimise duplication – Repeated data is a common find in business databases, often because modern data systems and CRMs don’t tend to merge well together. Making sure your different systems are integrated is an important step to cutting down multiple records for one contact. Then, identifying and correcting these duplications can make a huge difference to your data quality. There are lots of tools on the market that can speed up the process and save your team working through each one manually.
  • Ensure data is consistently formatted – Make sure aspects such as letter casing and the format of telephone numbers is consistent across your database. Setting simple business rules across all data input fields takes almost no effort to implement and can make a big difference in how the information is presented and used across the organisation. This would include, for example, making sure the telephone number field is populated with numbers only or ensuring the email field contains a real @ address.
  • Establish data quality processes and responsibilities in your team – Improving data quality can seem like a never-ending task, and it’s often one that organisations put off because it seems too onerous for their already time-poor workforce. Creating and implementing an effective customer data quality programme is key, allowing different teams across the business to recognise and record regularly occurring data quality issues, and work together to resolve them.
Two employees work on sorting out customer data.

The best approach is to tackle the data-related tasks incrementally. Assign clear responsibilities, set realistic targets, and review progress at regular intervals. There are dedicated tools on the market to help you manage and automate the process, but the team behind the project is the key success driver.  

Once you’ve identified what needs to be done to keep your customer data clean and useful, it may be helpful to invest in software to take care of the ‘heavy lifting’, saving your team from sifting through it manually. Not only should these measures help you have a better relationship with your data and customers, but they’ll also help your organisation improve productivity capability and decision making – investing the time in data quality is a win-win.

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