In this world of hyper-personalization, customer data is gold and data governance is the vault where it gathers, is polished, kept safe from thieves, and secured from unauthorized or unethical use.Unfortunately, there are many examples of improper customer data management, some big enough to make the news and some on the smaller side that appears in our everyday lives. In April of this year, hackers posted the personal information of 533 million Facebook users on a hackers' forum; a huge and troubling data breach. If you don't remember or weren’t aware of the event, perhaps it's because incidents like these have become all too common.But poor customer data management practices don't always result in something as serious as leaked personal information; sometimes they just create slightly weird or inconvenient experiences. For example, my favorite charity used to mail me two monthly newsletters each month: one that used the correct spelling of my last name and one that transposed two letters of my name. A glitch in the Matrix, or ungoverned information at work?The two examples given above are vastly different in scope and severity, but both are results of data management issues.Managing the vast quantities of customer data produced these days is a complex task. When businesses undergo digital transformation, the biggest challenge many of them face is managing and integrating data from multiple, disparate systems. Deloitte found that “companies on average have 16 different technology applications leveraging customer data, with an average of 25 different data sources used for generating customer insights and engagement.”[1]Integrating, managing, analyzing, leveraging, and securing data is essential for organizations that want to deliver personalized customer experiences and gain valuable insights. One study found that “nearly 80% of executives surveyed concur that companies will lose competitive advantage if they do not effectively utilize data.”[2] A task this important and complex requires a detailed data governance plan.
What is data governance?
Data governance formalizes control and authority over how data assets are managed. It also provides standards for how data is cataloged, defined, collected, stored, used, safeguarded, and kept "clean." Not only does effective customer data governance enable satisfying CX, but it also allows businesses to comply with laws and regulations regarding the collection, use, and storage of personal information. This is imperative for contact centers, which normally handle vast quantities of information daily.Let's take a closer look at some of the individual components of this definition so you have a better idea of what should be included in a data governance plan.
Cataloging customer data
The first step in the management process is to catalog all of your customer data. Documentation should include information such as where data is stored, how it's defined, and what it's used for. Because of organizational silos─or, the separation of different types of employees based on the department in which they work─this activity can be both challenging and enlightening. Deloitte Digital found that “Only 39 percent of companies surveyed strongly agree that they know where all of their customer data is stored..."[3] Think of this activity as a treasure hunt for gold, and agents and management should know all of the different landmarks.
Defining customer data
If you're like the average business and have 25 (or more) different sources of customer data, there will likely be some discrepancies in how common data is defined. Inconsistent data definitions can prevent integrations that provide a single, holistic view of each customer. An important component of data governance is standardizing the way all systems define data or, at the very least, documenting the discrepancies so developers and end-users are aware.
Collecting customer data
It's tempting to collect or acquire as much customer data as possible. In fact, AI-powered analytics tools thrive on enormous quantities of data. But a high percentage of customer data goes unused. So, scrutinize every type of data you collect and make sure it's used for a valid business reason. Additionally, collect customer data ethically and transparently. Disclose to customers what information you're collecting and how you're using it.For an in-depth look at how you can best leverage AI-powered analytics, watch the on-demand webinar “Change the Game with AI-Powered Analytics In Your Contact Center.”
Using customer data
A data governance plan should outline who can access customer data and what they can use it for. Additionally, end-users should be educated about what the data means (another good reason to document data definitions). Wharton's research revealed that "57% of marketers are incorrectly crunching the data and potentially getting the wrong answer—and perhaps costing companies a lot of money."[5] Useful, accurate analysis requires users to understand the data they're working with.
Safeguarding customer data
If you want your customers to remain loyal, they need to trust you. One good way to build trust is by protecting customer data from security breaches and by having strong data governance in place. Limiting the type of data you collect is one way to safeguard customer data. After all, if you don't have it, unauthorized people can't access it. Additional steps you should take include:
Limit the number of internal users who can access customer data
Use strong password protocols
Establish security standards─including for all applications that use or store customer data
Ensure your networks are secure
Use encryption heavily
Keeping customer data clean
If your data isn't clean, you won't achieve an accurate single view of the customer, end-users won't trust it, and analysis will be negatively impacted. There are several ways data can become inaccurate. Through a process known as data decay, old email addresses, home addresses, phone numbers, and other elements of personal information can become inaccurate when a person moves, gets a new cell phone, etc.Additionally, sometimes multiple records are created for the same person. This is a risk when multiple systems are used to collect or generate customer information, and I'm pretty sure that was the root cause of the charity sending me two newsletters each month. A good governance plan will specify standards for keeping data clean, including updating outdated information and eliminating or merging duplicate records.
Authority and control
A data governance plan is only effective if everyone in the organization complies with it, which means the data governance effort needs strong leadership support and systems and processes in place to enforce the standards. Many organizations establish a data governance team and a steering committee to establish policies, educate end-users, and ensure data standards are being followed.A strong customer data governance plan is designed around the goals of removing data silos, protecting personal information, and delivering clean data that provides a single view of the customer. This promotes seamless omnichannel interactions and personalized customer experiences.Related: For tips about personalizing contact center interactions, download our eBook, 14 innovative personalization ideas for your contact center.
Practical tips for customer data management
How you manage your customer data will ultimately impact your business results. Data breaches, for example, can result in costly fines, damage to your reputation, and lost business. On the other hand, if clean, secure data is leveraged to know your customers better and anticipate their needs, it can lead to significantly higher revenue and customer lifetime value.Here are three tips to help you get the most out of your treasure trove of customer data.
1. Universally unique identifiers
If you really want to have a single view of each customer, a data governance best practice is to assign them a unique identifier, such as a customer ID, and every system should use it. This enables analysts to associate each customer data point back to the customer in any system, resulting in more accurate analysis and a more comprehensive understanding of customer behavior.Additionally, an identifier enables organizations to provide the omnichannel experiences customers expect. If all the contact center systems use the unique identifier, the customer is known everywhere they interact, regardless of the channel. This means they can seamlessly move across channels and receive personalized offers and experiences in each one of them.Figure 1 Source: NiCE CXone: 14 innovative personalization ideas for your contact center
2. Implement the right technical tools to manage customer data
Managing and leveraging so much customer data requires tools that simplify data management and transform raw data into useful information. For customer service operations that generate and depend on accurate and complete customer information, a unified contact center platform and AI-powered analytics will provide the capabilities needed to provide personalized, omnichannel experiences.Contact centers often use a "best of breed" approach for software selection, which means their applications could be from multiple vendors and may or may not integrate well. This approach can generate data silos within the contact center, which can prevent optimizing the customer experience and providing omnichannel service.Our recent consumer research revealed likely symptoms of inadequately integrated contact center solutions:
64% of customers had to repeat information
59% had to repeat information even though they were communicating with the same agent
70% had to repeat information when switching to a new agent[9]
[10]Figure 2 Source: NiCE CXone: Cultivating a future-proof customer journey strategyA unified contact center platform can resolve the issues caused by poorly integrated contact center applications. When the unified platform includes applications such as an automatic call distributor (ACD), interactive voice response (IVR), workforce management (WFM), and digital and voice channels, the systems are integrated out of the box and don't create any data silos. This cross-channel interaction makes customer data management much easier, provides agents with relevant context and history, and enables seamless omnichannel experiences.Additionally, artificial intelligence provides the power to analyze customer service interactions and provide meaningful insights that can be shared with the rest of the enterprise. For example, interaction analytics tools can analyze 100% of interactions from voice and digital channels and provide information about customer sentiment, contact drivers, and trending topics. This is useful information for contact center leaders, but can also lead to improvements in other CX-impacting areas such as product features and marketing campaigns.
3. Implement a data-driven customer success program
To get the most out of your clean, integrated, secure customer data, consider implementing a customer success program. Customer success programs predict customer needs and questions and proactively address them. "Proactiveness" is a key difference between customer success and customer service, which is typically reactive. Customer success programs also help customers to get the most out of the products and services they purchased from you.The ability to predict needs is based on customer data and requires analytics tools infused with artificial intelligence. These tools provide an integrated view of operations and customers and enable businesses to optimize CX and quickly adapt to ever-changing consumer expectations and preferences. Additionally, data-driven customer success programs allow businesses to tailor customer journeys to the individual.Organizations that have fruitful customer success programs have effectively adopted data governance principles, including:
Standardizing customer data across the organization
Establishing a single view of customer data by integrating all enterprise systems
Leveraging the data to measure customer experiences across all stages of the customer journey
Data-driven customer success programs yield important business benefits, such as:
Higher revenue
Increased customer lifetime value
Improved Net Promoter Score (NPS)
Increased employee engagement
Decreased customer service costs
Figure 3 Source: NiCE CXone: The ROI of Operationalizing Data to Boost Customer Success
Learn more about the value of data-driven customer success programs
