Transform your customers’ omnichannel experience with an analytics center of excellence

Creating connected and personalized experiences across channels and platforms requires more than just data — you need the right team and the right processes. Here’s how to get started:

• Identify the goals. Understand what purpose your analytics center of excellence (COE) will fulfill.
• Determine the roles. Based them on your goals and determine who you will need on your team.
• Organize your data. Identify gaps, address inconsistencies, and develop your schema.
• Iterate and improve. Document everything, review regularly, and continue to ideate based on results.

Digital transformation for customer experience begins with data. To create customer experiences that are real-time, personalized, and able to span across multiple channels, you need the right customer data to drive the decisions powering that experience. However, customer data isn’t enough — you also need a plan for how that data will be organized and how it will be used to make better decisions. Determining the customer data that will be needed, the best ways to organize it, and how teams will be expected to activate upon it is the work of an analytics center of excellence (COE).

What is an analytics center of excellence?

At the most basic level, an analytics COE is a group of specialized experts within your organization tasked with driving the development and adoption of data-driven strategy across all business units and teams. For most organizations, the analytics COE will be tasked with:

• Data strategy — understanding how and why data is used within your organization and where certain types of data will be most beneficial to drive decision-making.
• Data auditing — identifying what data is currently available, what data is needed, and making a plan to address the gaps.
• Reporting and reporting strategy — creating the reports necessary for key stakeholders to understand business performance and providing the required data to drive decisions in both the short term and the long term.
• Data-centric culture — evangelizing and supporting the data-driven initiatives within the organization and serving as a pillar for adoption and change management.

As you can see from the list above, creating an analytics COE requires specialized skillsets that span across a wide range of subject matter. Your team members will need a solid organizational structure and effective processes to move forward in each of these areas.

Defining your vision and setting the goal for your analytics COE

Every analytics COE begins with a vision. For some businesses, that vision is to use data-driven online marketing to optimize current and future marketing campaigns. For other companies, it’s a push to streamline internal operations or to enable true digital transformation that uses real-time data to create personalized omnichannel experiences. Your company’s vision of how data can grow and change your organization for the better is the first step.

Ideally, that vision comes from the executive level, but it is possible to build the foundation of an analytics COE within a smaller unit of the organization. Tie this vision to a SMART (specific, measurable, achievable, relevant, and time-bound) goal to determine necessary roles and where to focus your initial efforts.

How to determine your analytics COE roles

There are several roles within an analytics COE, and your organization may have some or all of these already in place. In some instances, these roles may be merged or there may be overlap in the skills and competencies of your team members. What’s important is to have a cohesive team structure with clearly defined expectations for how your analytics COE will operate. Some of the more common roles are:

• Executive sponsor. The executive sponsor role is critical for effective change management and adoption. This role will be tasked with communicating the vision, expectations, and new ways of working as they are developed. They will also serve as the driving force behind the analytics COE and the final decision-maker for prioritization and direction of the analytics COE as a whole.
• Change management lead. In partnership with the executive sponsor, the change management lead heads up the strategy and implementation that ensures adoption of the new processes that the analytics COE puts into place. Having a plan for enabling, measuring, and driving adoption improves the overall effectiveness of the analytics COE as a whole.
• Program manager. Because developing an analytics COE requires multiple workstreams, roles, and outputs, having a seasoned program manager is essential. The program manager will coordinate the day-to-day activities, track dependencies, and keep the COE on track.
• Data steward. Though the data steward role may vary from organization to organization, one of its key responsibilities is ensuring that business requirements are translated into technical requirements — including data policies and usage across the organization. They are a critical piece to ensure that the analytics implementation meets the goals of the COE.
• Marketing (or channel) leads. These key business stakeholders provide insight into how your organization is currently using data and how data will be used in the future. This team member helps support the vision with concrete business requirements, identifying gaps in the current data and how utilization of analytics can be improved.
• Analytics strategist. Determining what actions to take based on the data takes specialized skills in identifying opportunities, gaps, and trends within complex data systems. This team member ensures that measurement strategies, performance reports, and strategic recommendations are aligned to provide the best business outcomes.
• Analytics engineer. Building out the analytics infrastructure requires in-depth knowledge of the data within your company. The analytics engineer will be hands-on in the implementation — developing data pipelines, working on ingestion and transformation, and ensuring that the data your company uses is clean, accurate, and up to date.
• Decision scientist. Using data modeling, machine learning, and data analysis to surface insights, your decision scientist will be one of the power users of any analytics implementation, providing key inputs as you develop the structure for data collection.

One last note on team structure. Allocate hours for each role to work on the COE that are separate from any ongoing day-to-day expectations. Treat the analytics COE like a top-priority project and provide adequate time and resources to ensure the work can be done in a timely manner.

Get started with the ASA framework and the analytics COE

As you develop the team that will drive your analytics COE, it may be challenging to know where to begin. Adobe recommends starting with an analytics audit to understand what data you currently have, what data you need, and any silos that require integration for a full view of your customers’ data. As you work through the audit and develop a plan for the reporting that will be necessary to facilitate your digital transformation, think ASA — accessible, scalable, and actionable.

Make your data accessible

• Collect data in a schema that makes sense to everyone across your organization.
• Provide impacted teams with a straightforward way to give input into the process.
• Communicate new expectations early and often to give people a chance to adopt the new way of working.

Make your data scalable

• Develop data collection standards with appropriate quality checks in place.
• Document all processes (complete with update/refresh cycles) for all roles.
• Create a centralized repository for enablement to ensure all teams have access.

Make your data actionable

• Create consistent cross-team definitions for foundational metrics, KPIs, and success criteria.
• Develop clear roles and responsibilities for data activation and how data will be used in day-to-day processes.
• Ensure leadership support — at the executive or team level — in driving new ways of working and uncovering new opportunities.

Remember that an analytics COE is about creating a process that allows you to progress toward your data-driven customer experience vision for your organization. As a part of this process, plan for continual iteration and improvement. Once established, your business will have a powerful advantage in driving the kinds of insights that will transform your customers’ experience and empower your teams to develop new ways of engaging with customers to drive sustained value.

Click here to learn more about how Adobe Customer Journey Analytics can help you discover actionable insights for your business.

Ereika Collins is a dedicated principal digital strategist specializing in digital transformation, value realization, and personalization at scale. In her time working for Adobe, Collins has led complex strategic engagements to drive adoption, improve customer acquisition, grow CLV and establish data-driven, customer-centric methodologies for delivering personalized experiences at scale.

By approaching engagements through the lens of generating measurable, high-impact outcomes, Collins ensures that her clients have a thorough understanding of the people, processes, and technologies required to create and implement omnichannel initiatives that drive continued customer engagement and revenue.