Operational vs. Analytical CX – What’s the Difference?
June 28, 2019
Over the past several months, as I’ve talked with customers, prospects, and partners, I often probe to understand what they mean when they describe their customer experience (CX) programs and aspirations.
I’ve come to see two discrete types of programs – Analytical, and Operational CX. In both types of programs, professionals will use operational data (such as customer attributes, financial data, product data) along with customer interactions, feedback, and social conversations, plus agent and employee feedback, to understand and improve the customer experience. Programs are designed to help understand and improve experiences and drive financial or market advantages to the company. But there are significant differences between the two approaches.
Analytical CX programs are often outgrowths of traditional customer insights, market research, or even agency-led initiatives designed to look for specific answers to specific questions about the customer. Analytical CX programs typically display these attributes:
1. Analytical CX is project based
A project has a discrete objective, desired outcome, and deliverable designed to answer a specific type of question about a product, a service, a marketing campaign, or a customer service initiative. Often the project is timed to a strategic initiative about to be taken or under way.
2. Analytical CX is driven by hypothesis-testing, scientific method approaches
A customer insights analyst might want to test assumptions about customer affinity to a new product. Or hypothesize on the reasons a website is or isn’t meeting customer needs. Using customer feedback, surveys, social content, or conversations, that analyst will test his hypothesis, adapt it if it fails, and iterate until proving or disproving the theory.
3. Analytical CX is performed by analysts or data scientists
This is typically done in a dedicated customer experience function, or marketing, operations research, or even IT – personnel whose day job is to live in the data and analyze it. These are people fluent in many types of research, comfortable with advanced tools, and comfortable wrangling data from diverse sets, often on a whim, to answer the pressing question of the day.
There is a home for Analytical CX in most large organizations. As businesses become more complex, data becomes more diverse, dynamic, and increasingly more unstructured, analytical CX professionals provide the technology, tools, and process skills to answer the strategic questions of the day.
Operational CX programs, by contrast, are designed to help organizations manage their own performance to achieve operational goals, business improvement goals, or successful transformation goals. Operational CX programs typically display these characteristics:
1. Operational CX programs are “always on” because businesses are always on.
They are visible to employees for whom analyzing data isn’t a full-time job, but for whom receiving insights to drive continuous improvement and performance to goals is valuable and useful both personally and organizationally.
2. Operational CX programs go through long-term evolution
Just like how performance objectives evolve, and as business practices and customer interaction channels evolve, Operational CX programs evolve but it is measured in quarters and years, not days and weeks.
3. Operational CX programs aren’t projects with a start and end
They’re initiatives that persist until goals are achieved, and then often persist further to ensure goals are maintained.
Typical CX goals that are aligned to operational CX programs would include:
- Increasing customer satisfaction, or NPS scores
- Reducing churn
- Achieving and/or maintaining product quality, and competitive differentiation
- Streamlining support procedures to improve first call resolution, increase contact center deflection from high cost to low cost channels, or to maximize utility and capability of newer support channels such as self-service, mobile apps, or online chat, chatbot, and messaging clients
- Driving increases in marketing outcomes like brand equity, loyalty, or market awareness
- Driving increases in sales outcomes like rep sales achievement, or improving sales organizational performance
- Reducing product safety, financial or regulatory risk
In an Operational CX paradigm—regardless of the type of outcome an organization is aiming to achieve the program is structured using a programmatic approach:
- Outcome measures are established (NPS, call center performance metrics, sales goals)
- Input drivers are collected from operational, feedback, and conversational sources. Typically input drivers would include cost information, performance characteristics of agents, sales reps, calls, and most importantly, conversational attributes and feedback attributes extracted from call transcripts, surveys, social conversations, chats, chatbots, and more.
- Where needed – input drivers, if contained within unstructured sources such as calls, transcripts, text, etc, are extracted and tagged using text analytics solutions designed for such purposes, such as Clarabridge’s CX Analytics Platform.
- Lastly – reports, and dashboards, based on performance management best practices and templates, are created and disseminated across the organization to provide performance feedback to staff, management, and executives. These dashboards provide a feedback mechanism so that staff can identify ways to improve to achieve outcomes, and provide management tools to help management and executives manage their teams to achieve their goals.
To drive CX maturity in an organization, it’s often easier to start with an analytical approach, but sustainable cultural alignment to CX best practices, and more importantly financial return on CX initiatives (through saving money, improving loyalty, or reducing risk) is most likely to come from an Operational CX approach.
Request a Demo with Clarabridge if you’d like to learn more on how we can support you on both types of initiatives.
Sid Banerjee (@sidbanerjee) is Clarabridge’s Founder & Chief Strategy Officer.