Customer data debt is an important concept for all businesses. It impacts an organization's profitability through direct costs, such as unused storage expenses, and indirect costs, including job management for updating unnecessary tables. Let's learn how to avoid costly inefficiencies that can escalate rapidly when customer data debt is not addressed proactively.
Imagine this scenario: You have a ton of customer data pouring in from various sources, but it's not up to enterprise standards. You don't trust the customer insights you're working with because they may be inaccurate, outdated, or not a precise representation of the customer.
Sound familiar? This is what happens when you don't manage your customer data debt — and it's a growing concern for many businesses.
At its core, customer data debt (sometimes called technical debt) is the money required to fix data problems. Those problems include a lack of quality control, data security, governance, and repeatable processes. In a sense, they are caused by “borrowing” data before it’s paid for — and the debt builds up over time.
Businesses "borrow" data when they use data from different sources without ensuring the data is up to enterprise standards for quality, consistency, availability, and security. It's like taking out a loan — you may get what you need now but deal with the consequences later.
With customer data debt, the build-up hinders the delivery of a personalized customer experience (CX). Marketers can’t trust they have an accurate, precise representation of the customer, preventing the delivery of a hyper-personalized CX. Having to “pay off” (fix) customer data debt uses valuable resources that could otherwise be used, creating optimal use of incoming first-party data or building generative AI models.
Just like getting rid of any debt, you need first to recognize whether it exists and how pervasive the problem is. Profiling customer data to understand how fast it’s coming in, its cadence, and its state is important. It’s easy to observe the detritus, but you need to go upstream to determine the origin of the problem.
Is the data coming from a customer or transaction table? Was it transformed, identity resolved, and 100% accurate? Having two records of the same customer with different email addresses is an example of customer data debt.
Relying on a customer data profile (CDP) architecture to integrate customer data can be frustrating if the debt is not fixed first. Many vendors will offer their services, but it’s up to you to ensure that the incoming data is properly transformed, identity resolved, compliant, and secured. Tackling debt requires understanding where (or whether) transformations in real-time have been completed following the cadence of the customer.
To solve customer data debt, you first need to understand the four “Ds”: Dark Data, Duplicate Data, Dirty Data, and Decayed Data.
Think of this as data that no one is using or as information that is stored haphazardly and never look at again. Your data needs a central place with documentation to be useful and helpful. That way, everyone in the organization has the same source of truth.
This is a partial copy of a primary data source that’s often redundant and hard to keep track of. It can be removed if it’s not necessary to the process.
Poor data quality can make it difficult to understand what information is accurate, complete, consistent, and reliable. If you can’t trust the data, customer insights can be unreliable.
The decaying process is when data assets sit in your warehouse or BI tools, and are unused either directly or indirectly. This can take up valuable storage space and computation resources to manage something you don't need.
Having data debt affects businesses' bottom line in both direct and indirect costs. There's the storage cost for data that’s not being used and an execution cost for managing jobs to update tables no one uses or needs. This creates inefficiencies that are hard and costly to tackle — it’s a problem that can spiral quickly if not taken seriously.
On top of this, there’s the issue of interpreting data correctly. With poor governance and the organization of data sources, it can be difficult to determine which information is trustworthy and correct. Data teams can become overwhelmed as they try to make sense of the chaos, and it can slow down onboarding new data analysts.
By managing their data sources more effectively, cleaning up dirty or duplicate data sets, and using compliant, clean sets gives companies the opportunity to improve efficiencies across a variety of departments, including improved efficiency in company marketing campaigns. With better audience targeting for marketing, companies will see improved campaign engagement (more clicks, likes, etc) and improved on-site conversion rates.
Better audience targeting means the people seeing data-driven marketing are already known to be in-market making their path to purchase more expedient and decreasing the overall cost of acquisition for a new customer. This will lead to better overall marketing cost efficiencies delivering more return on ad spend than without a data-driven approach.
The key is to have a governance process in place to manage all data sources. It’s essential to document and identify where the data is coming from, as well as the transformations necessary to maintain a high level of trustworthiness and accuracy.
If you’re not careful, duplicate data sources can quickly pile up. The best way to tackle this is by establishing a single source of truth where all the current and correct information can be found at any given time.
When data isn’t accurate, complete, consistent, and reliable, it can be damaging to customer experience initiatives. Delivering personalization and personalized customer experiences means relying on clean data.
Data security is increasingly important in today's digital world. That means implementing the appropriate security protocols and monitoring compliance requirements to make sure that data isn’t being stored inappropriately.
It's a pain to update and organize customer data manually. Automating processes with bots and APIs helps streamline this process, making your team more efficient.
Whether it’s a data scientist or a marketer, everyone in an organization needs to understand the importance of data governance. Without proper education about their roles and responsibilities, your data debt will continue to grow.
No one likes debt, yet data debt is an inevitable reality. With the appropriate tools and processes in place, businesses can take ownership of their data debt and empower employees across the board.
According to a study by Boston Consulting Group (BCG), approxmiately 90% of business leaders have identified data activation, collection, and scaling to be significant challenges to meeting their marketing objectives. In order to stay ahead of the curve when it comes to upcoming obstacles in digital marketing, utilizing cutting-edge data-driven tools for marketing campaigns is key.
Overcoming data debt is going to make the biggest impact for your return on marketing investment, and the BCG study also determined that data-driven marketing can lead to a 5% sales growth for a company. This means that data-driven marketing could help a $10 billion business obtain a $500 milllion growth in profits.
Taking control of customer data debt means improving trustworthiness and accuracy, future-proofing valuable customer insights, reducing organizational inefficiencies, and ultimately delivering a better experience for customers. Is your organization ready to face this challenge? If so, DrivenIQ is here to help by offering our enhanced platform called VisitIQ™. Request a demo today to learn more about our solutions.