Communication in Sales
Case Study by Intentex
Live chat is an important part of any online business. It’s a fast and efficient way to engage with one’s customers, provide reassurance, and increase the conversion rate. The purpose of this research is to identify the presence of linguistic factors affecting the efficiency of live chat communication between agents and potential clients. Based on sentiment and tone measurements we are able to provide a comprehensive and extensible method for the data-driven analysis of text chat logs that can support both qualitative and quantitative investigations of computer-mediated communication of the exchange between customers and company.
The chat transcripts were provided by 4Team Corporation. 4Team Corporation is a Microsoft Partner and Independent Software Vendor for small and large enterprises. They have over 17 years of experience, specializing in developing software for Microsoft Windows and Add-ins for Microsoft Outlook.
In this study, we examined chat transcripts for the time period between Feb 08, 2016 and Feb 07 2017. The transcripts were divided into two groups: chats which resulted in the sales of products and services, and chats that did not result in any sales.
Transcripts were analyzed using IntenCheck text analysis engine which can perform text analysis across 26 different psycholinguistic categories split into two groups: Tone (Emotions and Attitude) and Communicative (Communication Style, Insincerity, Timeline, Motivation, Perceptual Positions).
The transcripts that did not result in any sales were set as the baseline for our analyses. baseline against which the analysis was performed was created from the chat transcripts which did not result in any sales.
All values are presented as a percentile deviation - Pdev. By taking the percentage of words Pno_sales, classified to the category of No_sales and percentage of words Psales belonging to the category of the Sales. The percentile deviation was calculated by using the following formula:
Pdev = (100 (Psales-Pno_sales)) /Pno_sales
The analysis was broken down further by analyzing only the text sent by the sales agents and excluding the clients’ replies.
Analysis of the two types of chat transcripts (dialogues with the customers vs sales agents chat lines) showed very little or no difference in the statistically significant category of results, meaning that sales personnel were leading in the conversations.
As the texts are mainly of technical nature, we did not expect any strong resemblance of Emotions. However, it is preferred that emotions such as Anger, Sadness, Disgust, Fear and Surprise, would be within the language norm or even lower and that the scores for the Joy category were higher.
In the analysis of Attitude (Semantic Differential) category, we observed that chats which were Strong in nature - led to more sales. This indicates that during the conversation with the client, successful sales agents used mostly Strong characteristics of the text which helps to create a feeling of confidence and reassurance. The analysis also showed that Negative Attitude category should be absent or be at a minimum (have a negative correlation). From this, it can be concluded that attitude within a short time of the sales chats must be Strong and Positive.
Communication Style analysis didn’t show any correlations indicating a preference of a particular channel usage when conducting sales. Because the conversations are about software functionality, computer terminology, etc., we would assume that the Rational communication channel would dominate. However, the study showed that in the dialogs there were no predominant channels. All information channels of perception have been more or less equally used, which is a good approach for effective communication when the preference of an addressee is unknown. According to the study, it can be assumed that the Auditory channel should be used to a minimum (negative correlation).
On the other hand, Timeline turned out to be a very important category. The analysis showed that successful sellers established rapport by understanding the problems experienced in the Past and translating them to the advantages (benefits) in the Future.
The analysis demonstrated that successful salespeople strongly expressed Motivation to Toward. People who are motivated towards what they want find it easy to stay focused on their targets. They think in terms of goals to be achieved and are often motivated to have, get, achieve, attain, etc.
Furthermore, The 2nd Perceptual Position - “You” was used the most, whereas the 1st Position - “I”, “me” was used very rarely - a negative correlation.
Based on this analysis, we have obtained important linguistic characteristics used by successful 4Team Corporation sales agents. By understanding these characteristics, we can conclude that certain categories should be used more and some to a lesser extent. To achieve more sales, while chatting to customers the dialog should be Strong, Positive, and contain the minimal use of Audial words. It should also be more about the Past with an intention to go forward Toward a solution leading to the Future, and with a minimal use of 1st Perceptual Position-I (I, me). Analysis of chat logs can provide an understanding of effective engagement with customers at the most critical moments in their journey – sales, service, and support.
By using IntenCheck text analysis engine it is now possible to obtain linguistic characteristics of the chat transcripts and understand how to improve the effectiveness of sales agents. This knowledge can be also applied when training and educating sales personnel, providing precise instructions on how to improve performance in communication with clients and become more successful when selling products and services.
By deploying IntenCheck API within CRM solutions and by combining it with analytics, one can monitor customer and employee engagements across multiple communication channels such as emails, chats, voice transcripts, surveys, reviews, feedbacks, testimonials, etc. and interpret according to the business objectives. They can then make appropriate strategic plans to enhance customer experience, loyalty, and eventually lead to higher revenue.
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