Contact Center Intelligence

The contact center intelligence model has been developed specially for all types of services related to Contact Centers including outbound and inbound calls. The mission is to optimize resources and processes offering the best service to contact centers and a better experience to the customers using Predictive Analytics and Big Data technology.

What value does it provide?

In one word "Efficiency".

  • Increased conversion rate.
  • Improvement in the success rate of calls.
  • Automated prediction of the required resources.
  • Automated scheduling based on agreements.
  • Real-time conversation analysis to improve the quality of customer service.


1. Increase in Conversion

Predictive analysis enables contact centers to identify relevant customer segments. From different factors such as demographic data, call history, time of day, or the voice frequency (Hz), to name a few, it is possible to calculate the probability of conversion of a potential customer.

The architecture is designed to provide information in real time to the agents, helping them improve the chances of conversion. Customer's emotional feelings or their reluctance to buy a product, make a payment or accept a subscription are indicated to the agent allowing them to adjust their speech for better results. Similar preparation of customized argumentary is performed for each possible scenario.



2. Increase in Contact Rate

Contact Center agents spend excessive time trying to get in touch with customers. The lack of responses decreases the contact rate and reduces the number of calls per hour. This predictive model is very effective in ensuring the right client to contact and the right time to contact them. That not only improves the success of the call but also the quality of care.

One of the key factors in achieving this goal is the application of Machine Learning processes and predicting the response of the clients. Based on the predicted results and the accuracy of prediction, the calls are scheduled to maximize contact rates.

3. Avoid IVR

IVR is often frustrating for certain users or customers due to the enforced wait time before being able to speak with a real person. However, it is still considered necessary and used to know the intent of a call and redirect the user to the appropriate agent or department in order to get the resolution in quick time.

Our predictive engine is capable of predicting the intent from a client call and, reduce the jumps of an IVR improving quality in customer service.

avoid IVR-1


4. Sentiment Analysis and Speech Analysis

Customer conversations are processed and analysed using machine learning models and assessed for content and emotions. The keywords and phrases used during the call are analyzed to understand what the person needs, what they are feeling or what they talk about. Expressions, tone, frequency, volume and intensity are analyzed together to define the meaning of the words or phrases.

With these calculations the needs and interests of the person are predicted during a call, in real time. Providing these information to agents, it is possible to customize the solutions offered to customers, in addition to the benefits of offering a great costumer experience.

5. Quality Control

Quality control is an extremely important task in the Contact Center. The calls are analyzed in detail using machine learning models identifying the tone of the conversation, the professional behaviour of the agent, interest generated among the clients, the fulfillment of the scripts by the agent, the level of customer satisfaction among other factors.

Technically the voices are separated in the processing with idea of measuring the characteristics of each of them. The analysis of the calls allows to evaluate the form of presenting products or services, the service offered to the client, the opportunities of cross-selling and up-selling or simply if the actions match with legal protocols defined by the company. It is undoubtedly, a tool to improve the productivity and performance of the agents and the Contact Center.



6. Phone Authentication

Like biometric features, the components of voice have a unique identity. Identification and authentication processes can be enabled with the use of vocal attributes, just like a fingerprint, without the need of using a PIN or an identity document.

7. Voice Mail

Customers often evaluate calls negatively due to long wait times. Voicemail technology brings an alternate approach to this problem allowing customers to leave a voice message and schedule a call back at an appropriate time with a suitable agent. The voice message is authenticated using vocal parameters, analyzed for content and emotions, identified problems are then redirected to the relevant department or agent to resolve.

Among many others, these are a few examples of applications with Telephone services to improve productivity, optimize processes and generate greater customer satisfaction.




Call data optimization is fundamentally based on the information available with the clients. Some data points are captured during the call to add a layer of precision ensuring higher chances of success in the attempt of contacting customers.

However sometimes clients do not have sufficient data for a detailed analysis. Predictiva has developed a proprietary technology to capture sociodempgraphic details of users through their voice. These details add a layer of information and help in forming accurate segments of customers for various purposes. A list of phone numbers will no longer be the only available information to optimize calls. The phone numbers will be backed up with a lot more information.


These technologies and applications do not alter any existing solutions. They work alongside all available solutions and make them stronger.


The integration is simple and done via API. The projects are implemented in short time frames.