Opportunity Framing is the systematic collection and assessment of data to provide insights on a business’s “felt need to act.” It starts with gathering data around an issue, ordering it into themes / categories, then refining the insights into clear problem statements. These problem statements become the framework for effectively communicating to the business what the issues are, and what will be solved.
Insight
True Human-Centered Design means including the end users of your product or service early and often. Relative to gathering “Insights,” specifically the understanding of how customers both positively and / or negatively interact with a company’s products and services, data collection can be accomplished through multiple methods. Quantitative data analysis is a normal first step - the assessment of customer experience through Net Promoter Scores, Customer Satisfaction Surveys, Market Surveys and business metrics are some quantitative insight sources. However, qualitative insights derived from ethnographic studies (observational techniques) are also critical to providing a behavioral understanding of the customer’s true product or service interactions.
Data gathering and analysis often validate (or invalidate) the assumptions of business stakeholders on why issues exist, but more importantly, it can also uncover new issues not known by the company on what is creating product or service friction with customers. Especially when ethnographic methods are applied, the insight step initiates the transition from inside-out thinking, to outside-in thinking. These gathered insights become a library of actual issues advancing or detracting from the customer experience.
There are a myriad of approaches to collect insights. Each has a particular value relative to the information they provide about people’s attitudes and behaviors when engaging a company’s products or services. Collecting disparate data elements and processing them into clear, definitive statements helps to communicate the company’s real “felt need to act” through the one critical measure of business success - the customer experience. Each of the methods noted below represent relevant techniques for gathering data.
Data affinitization is the next step. Take the collected insight data and start organizing them into "like" groupings to see themes and patterns. This simple exercise helps manage what sometimes can seem overwhelming - looking at the varying, separate pieces of data and trying to see the true message. It is this convergence of information down to a level of specificity that allows teams to cluster data into zones of interest. These clusters form the basis of simple problem statements that clearly define the problems or opportunities impacting the customer experience.
Problem statements define two things - object and deviation. Object is the “thing” experiencing a problem. It can be a person, process, technology, data, department, business model or other business entity that is negatively impacting the experience customers are having with a product or service. Deviation is the specific issue that is happening that creates the problem itself.
A clear problem statement is the first step in isolating the root-cause of an issue. They help focus on the right issues, problems or opportunities to be solved. An example of this happened with a large retailer who increasingly experienced a high level of product returns. Upon initial quantitative data analysis (NPS score, financial reports, etc.) it was discovered that product return costs were increasing, customers complained about long in-store customer service waiting lines, and vendor / supplier relations were strained.
By conducting a thorough qualitative review (observational data collection techniques) it was evident that customer service representatives (the object) were accepting too many fraudulent product returns (the deviation) from customers with nefarious intent. The processing of fraudulent returns created tension for all customers in the customer service area who witnessed contentious situations of those trying to return a product falsely. Likewise fraudulently returned products going back to suppliers increased their costs, thus irritating key partnerships.
The resulting problem statement formed from the gathered, affinitized data - “Customer Service representatives accept fraudulent returns” - was created by applying both quantitative and qualitative methods to gather insights. Remember the organization’s initial “felt need to act” was to address supplier cost, increase customer complaints about wait times, and general irritation in the customer service area. The problem statement pinpointed the issue, that when fixed, would addressed all these concerns.
Problem statements should not try to define a solution, but to state the simple object and deviation. From problem statements, co-design solution teams now have a targeted “zone of interest” to begin crafting hypotheses for eventual customer experience solutioning.
Conclusion
Insight is the initial series of actions that helps advance a business concern into a clear problem statements that can be further analyzed in the frame and focus steps of Opportunity framing. Insight steps:
Gather, collect data (quantitative, qualitative)
Affinitize data into themes, categories
Draft problem statements (object / deviation format)
Shift’s next article will explain how to transform the Problem Statements to “Frame” problems into visual formats that allow for detailed analysis and clarification on what to solve.
Interested in learning more? Reach out to Shift to schedule a conversation, we’re always happy to chat.
