Application Of Artificial Intelligence At The Call Center
Artificial Intelligence technologies find very fertile fields of application in the field of marketing and customer relationship. In this article, we would like to introduce an example of how to apply the artificial intelligence call center that can be used to analyze telephone conversations in near real time to extract structured data.
Extract data from phone conversations in real time
The applications of artificial intelligence call center are still relatively unknown, because little widespread. There are, to date, very few solutions to exploit the AI in Call Center which does not mean that there are none.
But first, let’s quickly highlight the potential benefits of AI applied to customer service. Why and how to use AI in the Call Center? The major challenge is to extract from the telephone exchanges data (in the form of tags) which can then be exploited for marketing and relational purposes:
- For the enrichment of marketing automation scenarios.
- For retargeting and, more generally, the reinforcement of marketing targeting.
- For the analysis of customer satisfaction.
- For deepening customer knowledge, and a better understanding of customer expectations / needs.
- For the personalization of commercial solicitations.
To date, it is clear that very little data is extracted from oral conversations between clients and call center advisers. The counselors only transcribe a tiny part of the conversations. However, the phone is the channel No. 1 of the Customer Relations remotely (in front of the email) and, very often, the customer relationship just short. More than half of the interactions between customers and brands go through the phone, so through the Call Center. As a result, brands are depriving themselves of a colossal field of customer data. Artificial intelligence call center can help address this situation by helping companies extract and process as much data as possible from telephone conversations.
A tool consisting of 4 technological bricks
The tool combines four technological bricks:
- A signal analysis technology , for detecting speakers, IVR (interactive voice server) and speech segments, noise, music, etc.
- An acoustic model , allowing the vectorization of the signal and the transformation of the vectors into phonemes (TDNN LSTM neural networks). The acoustic model is calibrated to adjust the tool to the characteristics of the company’s telephony.
- A model of language , to recompose words from phonemes and to combine words into sentences. A language dictionary is produced when the tool is deployed.
- A technology of semantic analysis , allowing the standardization and the tagging of the data thanks to the artificial intelligence and the machine learning. Semantic analysis deals with sentence segments.
The deployment of tool in the company takes place in two stages :
- A pre-deployment stage of an average duration of 3 weeks. It is necessary to analyze approximately 6000 calls to calibrate the language dictionary. Depending on the size of the Call Center, it can take from a week to several months. Calibration of the acoustic model requires between 4 weeks and 6 weeks.
- A deployment step, which varies according to the volume of calls processed.
The pricing model used by is SaaS based, with on-call billing and a cost per call inversely proportional to the volume of calls handled. If company refuses to communicate publicly about its rates, they are able to get them. It represents a moderate cost per call. In many situations we face in our Call Center or CRM business, these costs will find economic viability, return on investment. It should be noted that tool is currently working on a model of performance billing.
For the company, three priority applications can be implemented: hot and cold customer voice analysis, triggering marketing trigger via your marketing automation solution, triggering retargeting by linking to the individual between the tags and Adwords. Company is an interesting example of application of Artificial Intelligence in the world of marketing and customer relationship. It illustrates the interest of the AI applied to the Call Center, this interest residing first and foremost in the ability to extract data still largely unexploited by customer services.