I've recently been working with a client who uses on-line chat as a sales facilitation tool. Sales chats as a use case for text analysis fascinated me from the beginning because the sheer number of people who shop via the internet is so vast and the number of companies using sales chat is growing so rapidly. If Etuma360 could do a good job of analyzing sales chats, potentially a lot of companies might be interested in Etuma's solution.
The client knew that they were having thousands of chat dialogs every month, knew that some sales agents were better at selling via chat than others and that some agents had lots of chat dialogs as compared to other agents. But, without extensively studying all the chats, there was no way to know what the topics were that were being discussed, whether more successful agents chatted about different things than less successful agents, how customers felt about different issues being discusssed, whether repeat customers dialoged differently than new customers, etc.
After running a set of the client's data (several thousand chat dialogues) it was clear that Etuma was going to be able to help the client with both topic and sentiment readings on their chat data. And, because Etuma can take background variables like agent number, region, website, or other data, its analysis was going to be able to tell the client what topics and sentiments expressed were for successful vs less successful agents, as there were clear differences. Some other valuable insights came out of the analysis, including certain topics that started trending positively or negatively at at different points in time. As chats are going on costantly, picking up newly trending topics, whether positive or negative gives the client real-time insights into issues that might be driving business.
Lastly the topics Etuma identified were interesting in and of themselves. Being able to see sentiment rated chat topics by itself showed the client what its customers were interested in, what they felt positive, or negative, about and how those topics related to sales.
All in all a very interesting exercise, with the take away being that analyzing sales chats is a valuable thing to do. And, one that is important to do well when doing it..
Stewart Nash
www.linkedin.com/in/stewartnash
The client knew that they were having thousands of chat dialogs every month, knew that some sales agents were better at selling via chat than others and that some agents had lots of chat dialogs as compared to other agents. But, without extensively studying all the chats, there was no way to know what the topics were that were being discussed, whether more successful agents chatted about different things than less successful agents, how customers felt about different issues being discusssed, whether repeat customers dialoged differently than new customers, etc.
After running a set of the client's data (several thousand chat dialogues) it was clear that Etuma was going to be able to help the client with both topic and sentiment readings on their chat data. And, because Etuma can take background variables like agent number, region, website, or other data, its analysis was going to be able to tell the client what topics and sentiments expressed were for successful vs less successful agents, as there were clear differences. Some other valuable insights came out of the analysis, including certain topics that started trending positively or negatively at at different points in time. As chats are going on costantly, picking up newly trending topics, whether positive or negative gives the client real-time insights into issues that might be driving business.
Lastly the topics Etuma identified were interesting in and of themselves. Being able to see sentiment rated chat topics by itself showed the client what its customers were interested in, what they felt positive, or negative, about and how those topics related to sales.
All in all a very interesting exercise, with the take away being that analyzing sales chats is a valuable thing to do. And, one that is important to do well when doing it..
Stewart Nash
www.linkedin.com/in/stewartnash
