Customer Data Strategy: From Overload to Insights has been saved
Customer Data Strategy: From Overload to Insights
The road to elevated experiences is paved with data pitfalls—but with the right guiding principles in place, organizations can realize the potential in their customer data.
Fragmented support interactions, weeks-too-late retargeting ads, and incoherent experiences across channels and platforms can make customers feel like nothing but a number. There may be truth in that sentiment. Too often, the data companies collect about customers remains literally that: nothing but numbers.
Without a clear understanding of customer data, marketers can find it all but impossible to determine what motivates individual customers across time and touch points. As a result, brands can miss opportunities to deepen loyalty and glean insights that can inform more efficient engagement and improve customer relationships.
By formulating an enterprisewide customer data strategy—a plan that goes beyond cataloging information to acting on it—organizations can better translate specific datapoints into an understanding of individual customers. They can then leverage that understanding and make better decisions with the data in order to provide all customers with cohesive experiences across channels, improve operational efficiency, and deliver value against business goals.
Challenges of Data Management
Manual processes, siloed organizational structures, and legacy technologies simply can’t keep pace with the data quantities, speed, and scale that define business today. In recent years, many organizations tried to address data overload by implementing centralized data lakes. Without an underlying data strategy, however, companies too often found themselves wading through something more akin to a data swamp—a morass of uncleaned, disconnected information that provided few valuable insights.
Some marketing organizations have also bought into technology solutions that promised low-code or no-code functionality, in the belief that they can be managed directly within the marketing unit. Because those solutions typically aren’t owned, understood, and operated in connection with the broader application and data ecosystem managed by the IT organization, marketers can miss opportunities for more powerful insights and actions. When it comes to data, bringing IT to the table is critical to long-term success in delivering highly personalized experiences to customers.
As for customers, the question of ownership often gets in the way of connecting customer data (and thus experiences) across touch points and time. Traditionally, a brand’s customer acquisition team nurtures prospects through brand awareness campaigns, with the goal of bringing new customers in the door. Once those prospects are converted to customers, they are handed off to separate teams that handle the ongoing relationship, including service and loyalty.
But customers don’t want handoffs. They prefer handholding along each step of the journey—and they’re willing to provide the personal information needed to receive those connected experiences. Consumers say they are comfortable with brands knowing significantly more about them than they think the brands already know, according to a Deloitte survey. Most want brands to use that personal information to provide better and more unique experiences.
Effective Customer Data Strategy
An ad hoc approach to customer data is unlikely to solve the issues of disconnected experiences and data overload. What’s needed is a single, unifying strategy focused on driving greater value for customers, greater efficiency for operations, and greater impact for the business. While smart data strategies are as varied as the companies that create them, the most effective approaches typically have five principles in common:
Customers don’t want handoffs. They prefer handholding along each step of the journey—and they’re willing to provide the personal information needed to receive those connected experiences.
Put customers at the core. At the broadest level, customer data strategy should focus on two goals: increasing top-line revenues by improving customer acquisition and loyalty, and creating bottom-line value through operational efficiency and insights. These goals ultimately serve and amplify one another: Loyal customers are willing to pay a premium for products and services, boosting the margin that feeds the bottom line, while aligned and efficient operations improve customer experiences in ways that drive revenues.
Close the loop between insights and action. The connections between revenues and loyalty and between efficiency and value cannot simply be assumed; they must be measurable. Achieving these improvements—and showing their impact on the business—depends on identifying use cases with measurable KPIs that explicitly tie data to insights, actions, and outcomes. Making those connections is all but impossible when an operating model is fragmented or siloed. To address this, enterprises can organize teams around KPIs rather than around channels or capabilities, thereby fostering greater clarity, ownership, and accountability.
Not all data is necessary. Organizations can assess the current landscape of data, technology, and capabilities available both internally and through partners and providers, identifying any gaps in the system. It’s important to recognize that more data isn’t always the answer to better experiences—and collecting too much data can result in undue risk. An effective customer data strategy will provide clarity, discipline, governance, and justification for what data is collected and how it is stored and used.
Automation everywhere. Having the right automation tool set, principles, and governance in place is vital to achieving and elevating personalization at scale. It is important to consider how AI, machine learning, and robotic process automation can contribute to data collection and insights. These technologies can help brands orchestrate incrementally better customer experiences even before they have fully mastered their customer data.
Prioritize agility, privacy, and sustainability. Organize data in a way that keeps it readily available not only for existing use cases but also for opportunities that may arise in the future. Managing data in serverless or low-maintenance solutions that are integrated within the organization’s broader IT ecosystem can enable greater agility to adapt and grow at speed. Meanwhile, identifying the processes and protections that govern workflows and safeguard customer privacy is critical for building and maintaining customer trust as well as addressing regulatory requirements. Many people love to talk about scalability, but sustainability is most important. Increased scale may go to waste if an organization cannot maintain the tools and processes it has invested in implementing—so it is essential for leaders to approach data management with a practical mindset.
A brand’s ability to foster strong connections is directly tied to its ability to collect, connect, and learn from customer data. With the right elements aligned and with concerted, action-oriented effort, a modern data strategy can serve as the engine that drives an organization to become a more customer-centered enterprise.
*End notes - As seen on Wall Street Journal & Deloitte Consulting LLP