Know if your customers think positively or negatively about your products and services, and put yourself in control of delivering an optimal customer experience
“A picture is worth a thousand words” — True in 1920s advertising, even truer when businesses need to connect with their clients in the 2020s. And if understanding the wants, needs and pain points of customers or business partners was ever important, then it certainly is now in this volatile economy. Data sources for this sort of information are readily available, e.g., product reviews on e-commerce websites, requests received via a company’s webpage, or plain simple correspondence with clients. In fact, data tends to be so abundant that it is often complicated to pin point key topics, relevant context, and client attitudes.
Inspirient’s fully automated text analysis can help companies in both understanding the exact topics and context of client enquiries (the semantics) as well as the attitudes that clients have towards these topics (their sentiment). For example, the key phrases (n-grams) above were automatically extracted from product reviews on Amazon’s online market place. They are also shown in representative textual contexts to ease understanding. Below, two key analyses are run based on these extracted phrases: A semantic network analysis examines in which context clients enquire about key topics. And a sentiment analysis prioritizes pain points that clients may have when dealing with a company.
These analyses are equally applicable to client enquiries received via a webpage or through regular correspondence. They can help companies in optimizing their marketing, sales, and after-sales activities, and – as was the case in a recent client project – even help to identify new market opportunities.
Inspirient's Automated Analytics automates the entire data analytics process end-to-end: From the assignment of input data, pattern and outlier detection, automated visualization of patterns, weak points and opportunities to automatic generation of textual explanations and recognition of the underlying relationships and rules. Most other analytics solutions rarely include these textual explanations and observations regarding the underlying data relations, which are both critical to provide a deeper level of analysis and more actionable conclusions.