A support archive contains thousands of messages, but searching for “export” will not necessarily reveal why exports cause trouble. Some customers ask where the feature is, others report a broken file, and some mention an export only to explain a different problem. Content analysis makes those distinctions explicit enough to examine systematically.
Content analysis studies communication by organising material into categories and examining its characteristics or meaning. It can be quantitative, qualitative or combine elements of both. The source might be text, images, audio or another form of communication; the method is broader than counting words.
Define what you are analysing
In a fictional support study, decide whether the unit is a message, a conversation or a customer. Ten messages in one unresolved conversation should not silently become ten separate customers with the same problem. Specify the period and inclusion rules, and consider whether the archive excludes people who gave up without contacting support.
Columbia University’s overview of content analysis provides a methodological reference. For practical work, the essential starting point is a clear question and a defined body of material, so that the analysis is not continually reshaped around whichever examples attract attention.
Make the categories usable
Develop categories from the research question, existing knowledge or an exploratory reading of the material. Define what belongs in each category, what does not and whether one unit can receive several codes. Include examples of difficult boundaries, such as a customer who cannot find an export control because their role lacks permission.
Pilot the coding before applying it to the whole dataset. Where the study depends on consistent classification, independent coding of a shared sample can reveal ambiguous rules. Use an appropriate agreement assessment and inspect disagreements, rather than treating a single score as evidence that every category works equally well.
Some analyses focus on explicit content, such as whether a feature is mentioned; others interpret implicit meanings. Those choices involve different demands on judgement. Describe the approach rather than suggesting that every category is simply an objective fact waiting to be counted.
Keep the interpretation attached to the material
If one conversation can receive several codes, category percentages may add to more than 100%. State the denominator and coding rule. Counts from a support archive describe that archive or its sampled units; they do not automatically estimate the experience of all customers.
Keep representative extracts and exceptions available for review, with appropriate protection for personal information. A category labelled “permissions” becomes useful when the report explains which permissions, in what circumstances and with what consequence.
Sentiment analysis can add a classification of expressed evaluation, while thematic analysis may explore patterns of meaning in greater depth. Choose the approach that answers the question. A rising count is a reason to investigate its context, not a substitute for understanding it.