Businesses aim to connect with customers on a personal level to improve sales, retain more customers, acquire referrals, and achieve higher customer satisfaction scores. Making messaging more relevant and targeted is not easy. Connecting all the customer information companies have collected is a big challenge.

Corporations gather customer information on their own through business transactions, credit applications, surveys, customer service calls, sales force intelligence, and more. They also collect third-party data about their customers from sources such as credit reporting agencies, magazine subscriber lists, social media, and web browsers.

Acquiring Data Isn’t the Problem

Companies have plenty of access to customer information. In fact, they are drowning in data! But organizations are underutilizing much of the information because they can’t positively connect the data from disparate sources. There’s no universal identifier that can track and match customer data across all avenues of interaction, so connecting consumer data from multiple sources requires sophisticated filtering, reformatting, matching, and interpretation.

Consumers themselves make the task more difficult. An individual doing business with various companies might list their name using multiple formats. Some data may be associated with old mailing addresses, abandoned social network handles, long-forgotten email addresses, a roommate’s home phone number, or dozens of other data points. These items may be stored by various departments and systems scattered across an enterprise.

Format differences in customer names can make it difficult to match data records. Some systems may support separate name fields for first, last, middle, and suffix, while others allow all the name elements in a single field. Customers may use formal names in one case and nicknames in another, or include middle initials sometimes, but not always. Standard exact-match programs won’t recognize the variations, resulting in duplicate records.

The Most Difficult Task

What companies really need are comprehensive customer profiles that combine and validate all the personally identifiable information they’ve acquired. Before a company can accomplish that feat, they must standardize, verify, and match customer data. This is the hard part. Because consolidating and validating enterprise-wide data often seems like a daunting task, it doesn’t get done. This is especially true if businesses approach the problem manually, using general-purpose tools like Excel to perform the steps necessary to recognize matches and combine information.

Only after cleaning and matching the data can companies truly understand their customers and respond to their needs. Without doing the data quality work first, organizations can’t develop reliable analytics that reveal valuable details about their audience. Uncleansed and unstandardized data prevents companies from safely automating processes like digital onboarding, or using techniques like artificial intelligence to deliver the benefits these advanced technologies can supply. Companies can’t even leverage the power of personalized or triggered communications until they can count on complete and correct data.

Fortunately, specialized software for address correction, data resolution, and matching is available. Applications like these help companies get closer to those 360-degree customer views they want. Manual efforts take too much time and won’t produce the best results.

Data-Related Obstacles

Common organizational goals for customer data consolidation include achieving better customer relationships, gaining better insight on which to base business decisions, and developing more efficient business practices. Company leaders want to put data at the center of their organizations, move through digital migration, and gain cost efficiencies.

The chief obstacles that keep companies from accomplishing these outcomes are data-related:

·Too much data

·Too much data variety

·Too many data sources

·Lack of data standardization policies

·Lack of data oversight management

Your organization (or your client’s organizations, if you are a service provider) is probably battling with these issues right now. Almost all companies view data as the path to success in a digitally dependent environment. Take a look at the data your organization is using right now and adopt comprehensive data quality practices that will enable future growth, stability, regulatory compliance, and efficiency.

Ken Kucera is the managing principal of Firstlogic Solutions, delivering world-class address and data quality software to data-driven companies across the USA. With 40 years of industry experience, Ken leads the team that innovates and delivers address correction, data cleansing, data enhancement, and data matching/consolidation software at Firstlogic.

Reach Ken at firstlogic.com or follow him on LinkedIn.