Insights into consumer behavior and preferences can be surfaced not only from social media conversations but may also be found in an organization’s internal, private data. I’m certain many organizations use a community framework or email workflow process that allows customers to ask questions and get advice from in-house experts. The information exchanged on these platform may contain valuable intelligence on common issues, ideas for new products and emerging trends.
CI recently worked with a client that had constructed a similar knowledge base, which contained not only exchanges between customers and in-house engineers but also the online conversations within the engineering team. The goal of the project was two-fold: analyze the consumer – engineering exchanges for product enhancement and issues for a particular product and dissect the email exchanges within the engineering team for new product ideas and innovation. CI applied its statistical language modeling technology to the unstructured consumer data to surface common issues, emerging problems and recommend solutions to customer support requests. Using the same latent semantic analysis methodology on the collective email of the engineering team surfaced potential new solutions and product ideas.
Of course, analyzing and categorizing data is only the first step in the process, ensuring the right organization has access to the surfaced insights is also a critical step. If you are just beginning the process of identifying data resources that may contain important business insight you might also consider how your organization intends to use the resulting information, share findings across the organization and measure the impact of ongoing analysis.
Interested in learning more, read our text analytics case study.
