Many companies use chat to provide consumers a real time, direct link to a company. While on the surface, each chat session is initiated to solve an individual’s specific problem or question, this is just the tip of the iceberg of the value in chat. Chat is essentially a forum where consumers are discussing your products and services. Analyzing these interactions holistically with Signals can provide a window into collective issues and problems that are impacting your business. This unstructured data source not only indicates customer sentiment, but can also yield insights to common themes and emerging issues that impact customer experience. Sentiment tools often cover up the root issues expressed in chat sessions, requiring further data analysis to identify the key words and phrases expressed by customers.
As companies begin to recognize the value in analyzing the textual content contained in chat, they are choosing to do so in two distinct ways:
- Analyzing “customer only” conversation. This analysis methodology focuses on the voice of the customer to uncover themes, topics and emerging issues discussed by the client base.
- Analyzing “agent only conversation”. This analysis method focuses on monitoring agent behavior to ensure compliance with protocols and to identify areas for individual improvement.
By breaking analysis into these two distinct ways, companies can uncover opportunities to better business practices both internally and externally.
The best place to start your text analysis of chat data is to identify the central themes through buzzword combinations and categories. Buzzwords are paired word combinations identified by their frequency. Tag cloud formats provide simple data visualizations for identifying the most prominently mentioned terms within your content. With tag clouds, it’s easy to determine the product types or names (e.g. Money Market or Debit Card) being frequently discussed. Categories (types) of conversation are automatically identified by the platform based on a variety of factors including frequency, strength, and quality of word association in the chat data, sentiment, and others. These are displayed in order of statistical relevance. Visualization of both categories and buzzwords will then quickly shed light on the most important areas to address.
The next step in the chat data analysis is to look for trends in each category, and in the most frequent buzzwords. It is important to know if the volume and sentiment for each topic is gaining or losing strength month over month. For example, a financial institution may see a steady increase in tax related issues and products in the spring, peaking in April. Trending the text analytics can also identify chat questions and comments on product changes. Perhaps a new version of a mobile app launched, and it generated an increase in chat questions. Businesses may be implementing process improvement plans, and are looking for confirmation from customers that the efforts are driving desired results. In either case, the chat data trends will show changes in outcomes.
Chat text analytics can also reveal issues that regularly occur with customers. Examples include commonly asked questions that are not addressed on the website’s FAQ page. There could be issues with particular browser versions, or operating systems, causing a different user experience. A standard business transaction process could be a source of chat support. By data mining the chat sessions, it is possible to identify these persistent issues and take a deeper look into the variables such as geographic distribution. Examine the individual chat sessions to confirm conclusions. It is also important to look for ‘social influencers’ in the chat data, and make sure their issues are 100% resolved.
Now that you have the explanatory analytics, explore possible explanations for why certain business outcomes are occurring, and the best ways to improve the outcomes. Areas to consider often include:
- Additional functions/capabilities in the product
- Updating FAQs on the website
- Improving the user interface
- Improving error messaging, and error handling
- Correcting employee interactions with your client base
- Implementing a go-forward data analytics plan to uncover issues in near real time
Analyzing the unstructured data set of chat sessions can directly lead to improved business performance. Not only will the text analytics identify specific problem areas, but it can confirm improvements as well. Of course, with Signals, these courses of action are all supported from a data driven foundation.
Our Signals platform can easily analyze your chat data. Signals provides data visualization of the unstructured text, providing the key buzzwords and categories and showing trends over time. Request a demo of our platform and see it for yourself. Contact us by emailing email@example.com or visit our newly-designed website!