Customer feedbacks are mostly in the form of unstructured data such as comments in surveys, review sites, and other places where a company’s products and services are discussed. However, without standardizing feedback, it would be a challenge to identify trends, incidence levels, and sentiment from feedbacks originating from disparate sources. A common categorization framework can help standardize coding of unstructured data.
Common categorization frameworks standardize unstructured feedback and make it analyzable at scale. Another advantage offered by a common categorization framework is data reduction, which in turn minimizes the complexity in identifying patterns from say, thousands of comments.
The framework is in addition channel agnostic as evidence is rolled up by issues rather than by functional areas. Yet, it is possible to refer to the affected channel or channels. A common categorization framework allows for multiple levels of specificity. It allows the backcasting of issues that might have happed earlier and projecting them to the past by allowing additions to the taxonomy.
The first step in developing a taxonomy is prepping the data. This involves standardizing terms. The next step is developing an initial taxonomy. The best way to set up the initial taxonomy is with data. By working on a cross-section of a company’s voice of the customer, verbatim using text analytics will suggest a data-driven coding structure.
The subsequent step is to apply the code to your database. The coding structures are typically hierarchical in nature with most coding structures having at least three levels, with some having eight or more. These are usually very technical coding structures such as quality studies with highly technical products. Once the data is coded, analysis can be done. It is also advisable to revisit the taxonomy from time-to-time because building a common categorization framework is only the first step. The taxonomy has to be updated and maintained to deliver optimum results.