Knowing user habits, typical objections and motivations for making a purchase decision, problems during product use helps to improve the user experience, as well as the product itself and its advertising positioning.
Unfortunately, it often happens that problems are never assessed during south africa phone number library product use, as advertising traffic brings in a lot of non-targeted leads – people who are not interested in buying.
How can we identify non-targeted calls among hundreds and thousands of requests in a simple and clear way?
We'll tell you how Voice Tagging of Calls helped an online psychological counseling service improve its advertising campaigns and attract more high-quality leads.
Features of the MCN Telecom client
Our client, an online platform for choosing a psychologist and conducting psychological online consultations, has been using the Virtual PBX from MCN Telecom, integrated with the CRM system, for a little over a year now.
The marketing department was faced with the task of improving the quality of leads attracted to their new product due to low conversion to sales.
To evaluate the effectiveness of advertising campaigns, the client connected Call Tracking, which allowed calculating the number of leads generated by each ad.
Further segmentation of incoming calls was supposed to be done by manually adding tags - marks that relate the call to a certain category - but operators forgot to add them or made mistakes in their selection.
Solution
We proposed to connect Voice Tagging of calls to classify telephone dialogues with clients using voice commands at the end of the conversation.
To implement the plan, it was necessary:
Update the tag structure for non-target calls by assigning recognizable synonyms to each variant
Activate the Question-Answer function and make the appropriate changes to the call scripts in your Personal Account
Set up sending tags to CRM based on external characteristics: targeted/non-targeted call, repeat, etc.
Results
Thanks to Voice Tagging, the marketing department was able to analyze the most frequent non-targeted requests and, based on this information, refine the offer of the new product and the content of advertising messages. This allowed the share of non-targeted leads to be reduced from 28 to 7% in just 3 months.
Those leads that were classified as targeted but with low priority (implicit interest) were placed into the marketing funnel for further warming up in various ways, depending on the type of objections.
In addition, it became possible to set tags even without being at the computer, which allowed for timely classification of leads in almost 100% of cases, and a large number of synonyms assigned to each tag made recognition as accurate as possible.
