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TDWI Upside - Where Data Means Business

Data Quality and the Single Customer View

A single view of the customer holds the potential for better customer experiences and stronger marketing relationships, but data quality poses a challenge to its success.

The concept of single customer view (SCV) has been around for a while, but lately it has been having a renaissance.

For Further Reading:

Data Quality Best Practices for Today's Data-Driven Organization

Data Quality Predictions for 2019

Data Quality Evolution with Big Data and Machine Learning

SCV originally grew from the intersection of two colliding trends: the rapid proliferation of customer touch points and the growth of customer experience (CX) as a branding tool and competitive differentiator. Now we can add a third factor: a new era of data privacy regulations and their related compliance issues.

This has strengthened the business case for a single, accurate, up-to-date source of data about your customers and prospects. Moreover, people are increasingly demanding a seamless experience with your organization, where customers are free from providing the same information repeatedly, prospects are not subject to multiple sales efforts, and your business has a 360-degree view of people and their preferences.

As this Pointillist article points out, achieving a single customer view has been cited by a Harvard Business Review study as one of the biggest challenges of customer experience management, impeded by causes ranging from legacy systems to organizational data silos. One of the issues that looms largest in implementing SCV is data quality, and here I would like to explore why data quality is often both the problem and the solution for SCV.

The Impact of Data Quality on SCV

Beyond the infrastructure of a common database, there are two key data quality considerations for maintaining a SCV approach: validating consistent contact data and linking each of your customer-facing touch points to this data. This article makes it clear that SCV is much more than funneling contact data into a common database; it is a commitment to customer data standards across the entire organization.

First, data in a multichannel world comes to you from different sources and under different circumstances. A customer may provide their contact data to you via phone or an online form -- then a subsequent order may use contact data that has been pre-programmed into PayPal. Still later, the same customer may reach out to your support team using their Twitter handle. Increasingly, customers and prospects expect you to realize that each of these contacts comes from the same person.

A related problem is that the same people may be in your existing database more often than you think. For example, I go by the name "Geoff" in real life, but the name on my credit card reads "Geoffrey." (If you've dealt with me by phone, there may even be a "Jeff" lurking around your data.) I may have had transactions with your company using both my business and personal email addresses. My home address may even be listed separately under a local municipality and a surrounding city.

These issues point to the need for a data quality and governance strategy fueled by best practices such as validating contact data at each point of entry, cleaning and rationalizing existing data, and linking a potentially wide span of associated data ranging from demographics to consumer preferences. Increasingly, deploying these strategies across an entire organization requires tools that integrate at an API level to a firm's existing CRM, business intelligence, and marketing automation platforms, together with business processes that consistently enforce data quality.

Making the Business Case for SCV

Perhaps the most compelling argument for SCV is that with today's growing needs for data quality, you may already have established a business case for it. For example:

  • You may already need to perform automated contact data validation to avoid stiff compliance penalties under recent legislation such as the European Union's GDPR, California's CCPA consumer privacy regulations (and a host of other state and federal privacy initiatives currently in the pipeline), or the expanded Telephone Consumer Protection Act (TCPA)
  • You may have calculated an ROI for clean, consistent customer data to reduce delivery problems or prevent fraud
  • Your prospects may currently be pushing back against disjointed, duplicate sales efforts

All of these issues require the kind of genuine, accurate, up-to-date contact data needed to drive a central customer database, as well as the automated processes needed to maintain this data. In short, you may already be doing -- or on your way to doing -- the heavy lifting that could justify a single point of reference for your contact data assets.

You could even make the argument that trends in data privacy and quality may someday mandate the use of SCV, as has already happened in the British banking industry. Since 2010, the UK's Financial Services Compensation Scheme (similar to the FDIC in the United States) has required this for financial institutions in that country. We are still far from the end of the road in the regulation of how businesses use and manage their customer contact data.

Either way, I have a more positive view of SCV: it holds the potential for better customer experiences and stronger marketing relationships. More important, I believe that in the not-too-distant future it will become a competitive necessity in many markets. Ultimately its bottom-line benefits, combined with growing competitive pressures to implement it, make SCV an important direction for every data executive to consider.

About the Author

Geoff Grow is the founder and CEO of Service Objects. Originally founded in 2001 to solve problems of inefficiency and waste through mathematical equations, Service Objects has validated and improved more than 3 billion contact records for over 2,500 clients. You can contact the author here.

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