Skip to content

Customer Database Management Best Practices

Authored by ActionIQ Team

5 Best Practices for Customer Data Management in the Experience Economy

Consumer expectations have dramatically shifted in the past few years. Consumers now expect every experience they have with a brand to be tailored and personalized. In fact, 80% of consumers say that the experience a company provides is as important as its product or services.

Why do some brands excel at making the most of their customer data and using it to create differentiated customer experiences? One of the major reasons is that they have achieved excellence in customer data management through the use of customer database software. 

Netflix, for instance, uses customer behavior data like completion rate, stop and start time and even pause, fast forward and rewind behavior to personalize content and create new features and experiences that have driven the rapid growth and deep loyalty of their existing customer base.

Starbucks ingeniously combined its mobile payment app with a data-driven customer loyalty program and personalized product recommendations, making the Starbucks app the most popular mobile payment app ahead of Apple Pay, Google Pay and others. And, of course, delighting customers along the way.

What separates companies like Starbucks and Netflix from the rest of the pack? Let’s look at some of the key best practices that CX leaders employ when it comes to customer data management. From customer retention to marketing automation, this article is going to help you learn from your customer insights so you can grow your business and succeed in your field. 

 

#1 – Eliminating Data Silos for a Single View of the Customer

Customer data silos are everywhere in the modern enterprise. Brick and mortar locations have one set of systems. Online commerce and call centers have their own as well. The website for a single brand’s presence in just one country it serves may employ a dozen different vendors to track and measure online behavior—each its own additional silo. Add to that social media, mobile apps, advertising platforms, and a multitude of SaaS point solutions within the marketing stack, and the number of data silos in a modern enterprise becomes dizzying. Spread across each silo are multiple profiles for the same underlying customer, each carrying a fragment of that customer’s total behavioral picture.

The first order of business for any enterprise seeking to take control of customer data management is to eliminate data silos by unifying existing customer behavioral data. Rather than costly and complex point-to-point integrations, or disruptive rip-and-replace, leading brands opt for what Gartner calls a “smart hub” approach. With a smart hub approach, your existing systems can remain intact while a single, central, intelligent system brings all your client data together in one location. With data unified, these same systems then apply sophisticated identity resolution techniques to give you a single view of each and every customer, as well as their historical and in-the-moment behavior across every channel and touchpoint.

Eliminating data silos and unifying client data in this manner is probably the single most important, foundational aspect of customer database management. When it’s done correctly, every downstream customer experience action, process and application becomes more effective, easier to use, and more valuable to deploy.

 

#2 – Putting the Power of Segmentation in Marketers’ Hands

In most organizations, creating audience segments is cumbersome manual process involving time consuming back-and-forth between IT and marketing:

  • The business requests a segment
  • The request waits in the IT queue
  • The segment is pulled a few days later
  • The business needs a refinement
  • The refined segment comes back a few days later
  • The business wants to slice the refined data along a new attribute
  • A small project is kicked off to add the new attribute to the data model
  • New segments are pulled using the new attribute

Weeks later, marketing deploys a campaign using its new segments. Data from the field comes back indicating that another iteration of the campaign could greatly improve results. But the iteration never happens, because it would take too long to achieve, and the team has moved on to other challenges. According to McKinsey, it takes 4 to 5 iterations to achieve 80% of the value of a new personalization initiative. This organization will never get there.

The answer to successful segmentation lies in democratizing data for marketers and giving them direct, self-service access to the data, tools and processes they need to iteratively build, deploy and refine customer segments—so they can match the right segment to each experience. Without dependence on anyone else.

This is achieved by marrying a high performance analytical back end with an intuitive front-end user experience. Marketers using a customer data platform can interactively change audience parameters along an unlimited number of dimensions, receiving dynamic and instantaneous insights about the audience they’ve defined. They can then work iteratively to arrive at audiences of the ideal shape and size, allowing them to identify and seize opportunities that were never attainable before. The result is faster time to market, improved conversion, better customer retention, boosted loyalty and increased lifetime value.

 

#3 – Leveraging AI and Machine Learning to Improve Audience Development

Marrying marketer expertise with the ability to access and analyze all your customers’ behavioral data is a breakthrough for most marketing organizations. But there’s yet another level of excellence your teams can achieve: with the addition AI and machine learning.

By weaving in what’s called “human-in-the-loop” AI, where machine learning techniques work transparently alongside your marketers, you can deploy even more personalized, more effective experiences to larger audiences than ever before.

Two high value AI techniques easily employed by marketers include lookalike modeling and channel optimization modeling. With lookalike modeling, marketers can create a definition of “best customers,” and then use AI to find new and prospective customers with similar characteristics. Marketers can then create specific experiences for these audiences, with an increased likelihood of converting and retaining high LTV customers.

With channel optimization modeling, marketers can create an experience, and then allow AI to automated how the experience is deployed based on the preferred channel of the customer. Some customers, for instance, may prefer to research products in store and then make a final purchase online. Others do the reverse—researching online then buying in store. With channel optimization modeling, the customers’ experience is automatically tailored to their preference based on their historical and in-the-moment behavior. Resulting in higher conversion and LTV, as well as greater overall customer satisfaction.

 

#4 – Achieving Excellence in Data Governance

Working with customer data means working with personally identifiable customer information (PII). There are a number of reasons this information must be handed with care. These include:

  • All individual customers expect and are entitled to privacy, and various regulations such as CCPA, GDPR, HIPAA and more are in force to ensure businesses comply
  • Customer data is likely the most valuable data within your enterprise—therefore is is an asset you should nurture and protect
  • The recency, accuracy and completeness of your customer data will affect how you deploy experiences, translating into a direct effect on satisfaction, loyalty, retention and revenue

As a result, a critical aspect of customer database management is data governance. Most organizations have already made significant investments in governance. Any new solutions you introduce to build and deploy customer experiences should leverage all existing investments in data governance, and meet or exceed compliance standards set by your company and regulatory agencies.

They should include and/or integrate with enterprise-grade tools for managing data access, user permissions and privileges. Seek to work with vendors and partners who regularly enhance and update their privacy and security practices, make them transparent, and subject themselves to periodic audits such as SOC 2 Type 2 examination.

Lastly, your solutions for managing superior customer experiences should support the notion of a “single version of the truth.” This means they shouldn’t house conflicting copies of data or conflicting KPIs that can become out of sync with the enterprise’s systems of record (such as an EDW or BI system). Rather, your customer experience solutions should help paint a single, consistent, detailed and accurate picture that enables every department across the organization to have a single view of the customer, placing the customer at the center of everything you do.

 

#5 – Taking Advantage of CDP Technology for Activation vs. Storage

Leading brands are increasingly leveraging customer data platforms (CDPs) as their smart hub approach for breaking down data silos, empowering marketers with self-service segmentation and AI-enhanced audiencing, and helping manage governance of customer PII. CDPs deliver a range of benefits and capabilities for marketers and other customer facing departments across the enterprise.

But, from a customer data management perspective, many organizations ask: “why can’t I use my data lake or EDW as the basis for my CDP?”

A CDP and a data lake are significantly different. (That said, a well-architected CDP leverages all the investment an organization has made into its data lake and/or EDW—always staying in sync and utilizing those platforms as the single source of truth.) 

A data lake is used as a catch-all customer data platform for storing a broad range of enterprise data. While this data may arrive in the data lake with great velocity, volume and variety, it is nevertheless data at rest that is stored in its raw or near-raw format. Skilled technical users—data scientists or IT pros—are the typical users of the data lake, using it to source data for analytics or for integration to other systems and applications. Similarly, EDWs house static, carefully modeled data used by technical BI specialists to build repeatable, consistent reporting applications.

In contrast, the CDP puts data in motion, so businesspeople can utilize it and activate it around the specific purpose of creating, deploying and evaluating personalized customer experiences. 

A CDP enables the creation and management of a semantic layer atop raw data (which may come from the data lake, EDW or other sources) in order to apply business-specific meaning to the data so it can best be used in the context of marketing and customer experience.

The CDP also adds a user interface layer that enables business users and line-of-business analysts to build attributes, design and orchestrate campaigns & customer journeys, connect to marketing channels and activation engines, and measure the results of campaigns with customer insights. 

 

Bringing Your Customer Data Management Strategy to the Next Level

Powered by the only complete enterprise-scale CDP, ActionIQ gives you the intelligence and agility you need to deliver memorable customer experiences that drive loyalty and growth. If you’d like to enact a customer data management strategy that puts the customer at the center of everything you do, contact ActionIQ today.

Share:

Share:

Get the latest updates sent straight to your inbox.

Ready to get in touch?

Scroll To Top