Does your text analytics implementation or VoC program seem like a joke? If so, maybe you're falling for one of these blunders:
- Not listening appropriately: Not all data is created equally. Having a listening post - survey questionnaire, social media account, etc. - doesn't guarantee that it's being used correctly. Identifying the right metric to use for your text analytics project is key for success.
- Not using the right software: We use Clarabridge to help turn unstructured text data into a manageable, structured format. We then pair Clarabridge's offerings with our own technology, allowing for customized data integration, manipulation, and understanding.
- Not integrating the results: Text analytics can be confusing and, sometimes, make us want to run back to the comfort of quantitative data. Over 80% of data is unstructured/qualitative, though, so we can't just ignore it. Not using the qualitative data paired with quantitative results robs us of a full, robust image of the customer.
What other blunders might derail a text analytics program? Leave a comment to share your thoughts or experiences!