Developing a strategy to optimize and improve customer experience (CX) is not new. After all, Burger King launched its famous “Have It Your Way” campaign many decades ago.
Legend has it the global fast-food chain even went as far as replacing all of the doors in every single one of its international locations so they opened both ways. (Let the customer decide if she wants to “push” or “pull” before ordering her tailor-made Whopper.)
Times have changed quite a bit since this campaign initially launched. The deep bench of customer data management technologies brands now have at their disposal to support their strategic CX efforts and initiatives has evolved substantially in recent years.
Customer experience is now the ultimate competitive differentiator. But mastering it means mastering customer data management — or risking loss to competitors who already have.
It’s now a race to level the playing field with companies like Amazon and Netflix.
These businesses, and other major players like them, have already invested in best-in-class customer data management practices that make the single customer view (or 360-degree customer view) the center of their entire marketing and data infrastructure.
5 keys to customer data management
You’ve probably read dozens of studies on the topic of customer data management and its related technologies, such as the comprehensive Gartner report, “Use Customer Data Management Technologies to Deliver Better Customer Experiences”.
But you’re probably swimming in acronyms that do little to provide clarity on a path forward. So, consider this post the CliffNotes® on the topic of customer data management.
1) Customer data management is a practice, not a type of technology.
When it comes to customer data management, the tried and true framework of ‘people, process, technology’ certainly applies.
Although a chief data officer (CDO) or head of CX may lead the effort, it should be a collaboration between the business stakeholders responsible for marketing and CX and their counterparts in data, analytics, and marketing technology.
It’s important that both sides work together to define the overarching data strategy and clearly define use cases that map to business outcomes. Otherwise, you’ll end up with incomplete, disconnected, or uncoordinated outcomes that don’t support your use cases.
Analysts also caution companies not to ignore the importance of establishing internal data governance standards — especially in light of ever-changing privacy regulations.
Without this, you undermine your larger goal of unifying data into a single customer view to improve customer experience. These standards should answer questions like:
- How will you maintain your customer data management practices?
- What checks will you put in place to enforce them?
- How often will you revisit the policies?
- Who is responsible for flagging necessary changes?
Once you have the people and processes in place to inform your data strategy and use case roadmap, then it makes sense to start evaluating customer data management technologies that will enable them.
2) Stop trying to build a single customer view for everyone.
Most analysts will say attaining a 360-degree customer view that captures every possible data point you have on your customers to be used in every function of your business (marketing, finance, fulfillment, customer service, BI, etc.) is neither practical, nor necessary.
Instead, start building a single customer view that is explicitly designed for marketing and CX purposes. Brands that do this well don’t lose sight of what they are trying to do with the data — both in the short and long term. That means being thoughtful about unifying the data that actually matters for marketing and CX use cases.
Does marketing really need to know every shipping address a customer has ever used when ordering online from the business? Or, is it more important, from a data activation perspective, to instead simply know where the customer lives and shops?
Does marketing really need to know every single page a customer has visited on the website? Or, is it more helpful to know that a customer’s browsing behavior exhibits a high affinity for a specific type of content or product, and the intensity of their behavior indicates a high propensity to buy now?
3) Identity should be the core of your customer engagement model.
Whether you’re thinking about customer data management opportunistically for a digital transformation, or because you have an immediate need to address privacy compliance, you should be thinking about it in the context of your ideal customer engagement model.
Design a customer engagement model that puts the construct of “identity” at its core. That means it must account for two key factors:
- 1) Managing customer data at an individual level
- 2) Designing for optimal customer experience
To improve customer experience, Gartner research from its analyst team claims it hinges on the ability to recognize individuals and their interactions with your brand across channels.
However, not every type of customer data management technology today is designed to do this with the speed, accuracy, and scale necessary to succeed.
This is why the pairing of confidence and utility is especially key when it comes to a unified customer profile or a single customer view.
It’s not enough to bring on a technology that checks the box on providing a unified customer profile with a high degree of confidence (in terms of its accuracy and completeness) if the data cannot be used when and where you need it.
Conversely, there is limited value in activating lifecycle marketing programs and personalized experiences based on a unified customer profile that is incomplete.
(Or, even worse, activating based on a customer profile that is inaccurate or lacks the ability to recognize individual consent preferences across marketing channels.)
4) A best-of-breed technology approach is preferable to a one-size-fits-all strategy.
A recent Gartner report stresses that your customer data management infrastructure and processes must be flexible and adaptable. That means the enabling technologies you use as part of your customer data management practice must be flexible and adaptable too.
So it’s no surprise the results of Gartner’s marketing technology survey reveal that the majority of brands prefer a best-of-breed marketing technology stack, as opposed to the walled garden limitations of an integrated suite – often referred to as a “marketing cloud.”
The preference for a best-of-breed martech approach has led to increased adoption of customer data platforms (CDP) as the foundation for establishing a single customer view for marketing that both sources from and informs the rest of your martech ecosystem.
When you make the single customer view the center of your marketing and data infrastructure, you can orchestrate individualized marketing for every stage of the customer lifecycle to improve customer experience.
5) Customer data management technologies don’t fit into distinct buckets.
Despite everyone’s best attempts, customer data management technologies can’t be easily mapped to distinct buckets. There are overlapping capabilities between them, which is why there is so much confusion.
That’s why it’s important to clearly define your use cases before evaluating martech. It’ll help you better understand what you already have and what you still need.
This confusion has been particularly pervasive with customer data platforms. Gartner highlights this confusion in their (somewhat old) Gartner Market Guide for CDPs, which states that much of the functionality of a CDP is not new because it exists in some form or fashion across various legacy technologies.
However, it is the productization of these features, along with the optimization for real-time use cases, that makes the customer data platform distinct and valuable as a modern customer data management technology for enterprise brands today.
Adding to this confusion is the fact that CDP, as a category, is not homogenous.
Heritage and architecture matters. Some CDPs over-index on operational data management but offer little value when it comes to utility and activation of the data.
On the flip side, some CDPs over-index on marketing personalization, particularly in web and mobile channels, but lack the ability to truly unify customer data across channels and sources with the high degree of confidence that is required in today’s landscape.
Only a pure-play CDP is designed to deliver on both.
The right customer data platform — one that is built on a profile database architecture — won’t lock you into a narrow set of use cases.
In fact, a CDP should enable you to get value quickly from immediate use cases, while also offering the flexibility to adapt to future use cases that may not yet be known.
And, most importantly, the CDP should be able to adapt without punitively taxing you for it — whether that tax comes in the form of time or money.