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Mar 29, 2021

With powerful Artificial Intelligence (AI) models we have watched big companies like Google perform a remarkable robot call and completely deceive the receiver.

Despite these extraordinary innovations, it will not be technically easy and ethically conscious to robotize every single call-center operation (at least in a short time).

As a result, call-centers are far from dead:

  • They are the preferable way to communicate for many customers
  • They are one of the fastest ways to get problems solved
  • And they are a powerful sell-out operation for products and services.

On the opposite side, customers are more demanding! Long waiting times, poor service level, or low technical expertise are no longer acceptable.
In times where being good is not enough, how can AI improve the new generation of call center’s operations?

1. Improve operation sizing and resource allocation

Human resources are the major cost of a call-center. Consequently, being able to correctly plan and size HR is critical. In the case of an inbound operation, this projection is even more relevant due to demand uncertainty. But how to minimize idle time without jeopardizing time-to answer? The key is to have the most accurate demand forecast possible and plan accordingly.

For outbound call-centers, although more variables are controlled by the operation, to be able to contact the client at the best time fosters the client’s probability to answer and reduces the restlessness of receiving a marketing call.

As a fact, AI offers multiple tools to properly dimensioning and allocate resources:

  • predictive models can improve demand forecasting (incoming calls) or indicate the best time to contact each client
  • simulation helps to test different operations design and get more comfortable on decision making
  • optimization can suggest the best resource sizing/schedules that fulfill demand peaks and business constraints.

2. Reduce call bouncing and increase first call resolution

Client experience is a critical issue and calling to the support line may result in a situation where:

  • the call bounces between multiple operators and the client needs to explain the issue from the beginning several times
  • or the problem is not properly solved, and the client is forced to make a second call.

To avoid this lose-lose situation, readiness and operator knowledge are the keys. Being able to correctly steer the client to the most suited first operator is a job for an AI model that anticipates (in real-time) the call’s probable cause and decides the best operator for each call, also accounting for the client’s profile.

The benefits range from operational costs – fewer minutes per call and fewer re-calls – to customer satisfaction – more first call resolution and less bounce.

In a second layer, AI can also be a powerful tool to redesign the outdated first-in first-served queue line. Estimating the call importance allows creating a dynamic queue line that could easily improve churn rate and overall satisfaction.

3. Anticipate customers’ needs and proactively tackle them

Call-center calls are fairly correlated with technical problems or service issues – more issues/problems lead to more calls.

As a result, it is not pleasing and even less efficient to answer a group of calls that point to the same unsolved problem.

Being able to identify common patterns and act preventively will decrease the number of calls and, often, improve client quality perception.

Quick and targeted action is possible with the help of RPA supported by AI models that identify the probability of each customer being affected by the problem. As a result, preemptively notifying the client before receiving a call will result in a win-win situation. As a spillover, these results will also help the solution team to quickly identify and fix the problem.

4. Grasp client satisfaction - sentimental analysis over client calls

As important as avoiding the problem itself, the key to the client experience is how you handle it! Some problems can cause unpleased situations for customers. Proactively contacting customers in healing stages (after issues are experienced) will promote a notion of personalization and support.

By re-analyzing every call with AI models, it is possible to perform a sentimental analysis and weigh the satisfaction of each client.

Acting accordingly by establishing a follow-up call or giving a reward will promote satisfaction and client engagement. It is fundamental to avoid detractors that will damage brand perception and future sustainability.

5. Personalizing the user experience and automated responses

In today’s challenging environment, personalization and differentiation play the main roles in marketing operations. Proposing the right product to the customer can be the key element for the success of a sale.

Proficiently matching marketing offers to clients resembles the perfect job for an AI model – establish the connection between products/services and clients by leveraging historical sales, client’s profiles, and current market trends, defining the next best offer for each individual client.

When combined with a sound business strategy and a robust decision system, these AI models can be easily tuned to maximize ROI and customer buying probability. The experience could be even more augmented if it is done by robot agents that clarify simple questions/doubts or present simple marketing offers.


In a nutshell, AI can leverage every call center operation to high-performance standards without big system redesigns. As of today, customers are more demanding, markets are fearless, and competition is no longer local. Being on the edge requires a data-driven operation that leverages data in every single decision.

Dashboards and reports represent the industry’s standard and are great tools to evaluate historical events. However, they are not appropriate to forecast future events (predictive models) or generate insights for future decisions (prescriptive models) – Only AI is up for such a challenge!

By: Bruno Batista

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Marketing & Customer Analytics
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