How do you build a customer data strategy? It’s harder than it sounds and many enterprises struggle with the process, avoid it altogether, or relegate it to a departmental function (usually IT). But customer data is cross-departmental and cross-functional. There’s often no common or shared understanding of what it is, why it’s important, and who is responsible for its curation and elevation. And decades-worth of technology investments and past data initiatives (some successful, some not) create a healthy dose of skepticism and confusion at any organization about the utility, potential and direction of a customer data strategy.
Fortunately, there is a method to creating alignment, and a roadmap for customer data fluency that provides organizational clarity, a clear technology roadmap, and – most important – clear, tangible business applications for customer data that drive revenue, save money, and improve overall customer experience.
At Actable, we call this approach a Customer Data Enablement Playbook. It’s a three-step discovery framework and process that creates a comprehensive view of an enterprise’s customer data readiness across technology, team and business use-cases and deployments.
Here’s how it works:
Step 1: Business Demands and Use-Cases
The first step – and most crucial – is to assess business demands for customer data. Here we explore questions such as: what does the business want to do through more effective utilization of customer data? What could they do, and what should they do if customer data was “solved for” and flowed freely across the enterprise? What are the key pain-points that can be solved for with better customer data utilization? And how can customer data be leveraged to create more relevant customer experiences in in-bound and outbound communications?
Defining these business use-cases lays the groundwork for the technology systems and processes required to fulfill these business needs. Additionally, outlining specific real-world use-cases for customer data creates a share understanding of the utility of what can sometimes be an amorphous “customer data initiative.” Real-world applications for customer data are also the foundation for ROI assessments that underpin the business case for investment in customer data initiatives.
Step 2: Current-State Tech, Team and Data Assessment
The next step is to assess the current state of data readiness across the technology stack, organization and team, and existing data assets. First and foremost, we evaluate the current data assets available: what customer data exists in the enterprise today? How is it captured and how is it accessible? Are there consistent data governance standards across the organization? And what opportunities exist to extend and expand on data capture where assets may be scarce?
Next, we evaluate the current technology stack for data capture, opportunities for customer data creation, and the deployment vehicles for activation. How well do these systems work together? Where are there dead-ends and roadblocks? Are constituents getting the data they need, and in a timely fashion?
Finally, an organizational assessment is key to building a comprehensive customer data strategy. Current enterprise organizational structures are often ill-equipped to deal with customer data at a strategic level, as customer data spans departments and functions. Organizational alignment seeks to create a structure where teams can work across departments in the service of customer data, creating clear ownership and accountability.
Step 3: Can, Should, Could Framework Recommendations
The last step in the process is the prescription phase: recommendations which we deliver as a “can, should, could” roadmap. The “can,” includes near-term customer data wins that can roll out with limited investment and have an immediate impact and pay-off. The “should” includes mid-term initiatives that have a high payoff but require coordination, internal resourcing and some investment in tech upgrades and integrations. While the “could” framework describes that ideal state and long-term aspirational goals that can guide the evolution of the strategy over several years. Keep in mind, the “could” is an attainable state, not a pie-in-the-sky aspiration, but a state that requires ongoing commitment to customer data maturity.
At the end of this process an enterprise walks away with three tangible assets: 1) a team and organization aligned around customer data, what it means and why it’s important, 2) a clear roadmap for short-term, mid-term, and long-term execution, across tech, data and team, and 3) an investment case for customer data strategies with tangible use-cases that drive ROI.
From here, Actable’s remit often evolves to bringing the Data Enablement Playbook to fruition and bridging from the consultative discovery and diagnosis phase to actual implementation of systems and processes to drive use case deployment. That is, bringing the “can, should, could” framework to life. While the execution will be different for every enterprise, the framework above has been tried and tested, and provides a clear path for a winning customer data strategy.
