Unleashing sales potential: Machine Learning in telemarketing

Published by

Unleashing sales potential: Machine Learning in telemarketing

Sebastian Vasquez
Sep 8, 2019

The Machine Learning revolution has taken the tech industry by storm, especially Deep Learning (a method based on Artificial Neural Networks), it has opened new possibilities that let computers interpret and analyze data like never before. Deep learning works the same way the brain learns new things, through examples. Like when a sales representative has script to follow in a call with a client, he/she has to learn the vocabulary, questions and possible answers to give for a successful call. We train the model with large amounts of labeled data that goes through many layers of neural networks. This process takes a lot of computing power which makes it a good candidate for using server clustering or cloud computing because we can harness the power from multiple processors that we otherwise couldn’t have at hand easily.

Industries are looking into deep learning to get the edge they need to evolve because it can perform tasks better than humans in certain cases. Deep Learning combined with Natural Language Processing (NLP), a field that studies the interactions between humans and computers through natural language, can allow us to do pretty amazing stuff like analyzing the sentiment in written words and speech, mine relevant information from interviews and lectures and even imitate humans and pass the Turing Test, a test of a machine’s ability to exhibit intelligent/behaviour in a human level, by keeping a conversation with someone.

It is because of all these new possibilities that mixing Deep Learning & NLP with a traditional sales force provides an extremely powerful tool that will give us insightful information from potential leads and create strategies to ace sales goals. Traditionally, companies could measure the performance in sells, number of calls and success rate but it is very difficult to ensure a standardized pitch and get the real sentiment from the potential clients. The idea is to give the telemarketer insightful information when she/he is giving a pitch to someone over the phone and make the right decision, also the information collected in the call allows to segment our clients and potential leads, give us a higher success rate and overall increases performance in the operations by creating new strategies.

Imagine this scenario: Martha is trying to sell a new insurance package to John over the phone. Martha doesn’t have a lot of experience but she is very good delivering her script, by using Deep Learning the system can understand the idea of what is a “good sales pitch” and can give Martha real-time feedback in her job and improve at it. Now, John sounds a bit hesitant at first, but he changes his tone when Marta tells him about the family coverage. The system perceives this, and place John as a client keen on products that relate to his family and suggest Martha to offer products related to John’s interests in real time, at the same time Martha will grow as a sales representative and she will have more confidence in her future calls. As the system is used more and more we can redefine and improve our model making it better and optimize the experience in all the calls.

Conversations with machines are not farfetch, we can see them in Facebook’s chatbot that can be set to answer simple questions, we can interact with non-playable characters in the latest video games with human like interactions and even machine learning can diagnose some disease with extreme accuracy. In the telemarketing field these ideas are already implemented in products such as https://i2x.ai/ and https://cloud.google.com/solutions/contact-center/ which show the great potential and viability in the technology. These previous examples are pioneering the way we interact with people and computers over the phone with excellent results. In the future, we’ll see this kind of assistance permeates multiple levels in our society and makes interactions better among people.

It is worth mentioning that this is a symbiotic relationship, we need Martha as well as the system to optimize our sales, and it might not be a good idea to let a machine do all the work and impersonate a real human, because of ethical reasons that go beyond the scope of this article, for example accountability or tricking clients they are talking to a real person. We should never remove the human factor from our interactions because a machine will never replace a human being, no matter how well they perform.

In conclusion, machine learning is a powerful tool and if it’s implemented in activities such as telemarketing we can improve pitches and staff, we can enhance personnel into well-trained professionals and provide better service to our clients that will go beyond their expectations. We have a bright future where machines and humans can work together in unison, optimize communication and make our economy grow. Thus, we should embrace a world that sooner or later will work as a singularity where machines and humans can connect between them seamlessly, seeking for a more united, optimal and dynamic economical and sociological system.

Written by Sebastian Vasquez

Related articles

Women engineers making their mark in Tech

Their contributions drive the establishment of inclusive organizations, foster conscientious environments and support business success.

5 eco-conscious changes driven by technology

Do you like the idea of satisfying your needs without jeopardizing the resources and opportunities of future generations?

Healthtech to outshine market expectations next year

Even if there is a hiring slowdown across the entire tech sector as a whole, the best and brightest will likely flow into healthtech firms.

NASA style reinvention: Major changes in the pharma industry

A successful vaccine for a new pathogen and over 10 billion doses distributed shifts how people see the pharma industry.

CPA firm issues SOC 2 – Type 2 report Source Meridian

Our SOC 2 report has shown that we have appropriate controls to mitigate risks associated with our services.

“The doctor will see you now”: How AI is disrupting health tech

We collaborate with global health tech firms, boosting their AI and ML projects. The AI industry is set to surge by over 1100% from 2020 to 2028.

The DevOps Ice cream: 6 flavors that you should taste!

When we start to talk about DevOps people usually say that the term as it is is too broad or complex to have any practical implication.

Unleashing sales potential: Machine Learning in telemarketing

The Machine Learning revolution has taken the tech industry by storm, especially Deep Learning (based on Artificial Networks).

Artificial intelligence: Enriching people’s way of life

Who would have thought that technology would become such a fundamental part of our everyday lives?

Key motivations for embrace Data Science

By utilizing semantic analysis, patterns, statistics, mathematics, and machine learning, data science identifies patterns within extensive data.

Unpacking UI and UX: Exploring language and applications

To help you better understand UI and UX development, we’ll define these terms and explain their differences and purpose.

Women engineers making their mark in Tech

Their contributions drive the establishment of inclusive organizations, foster conscientious environments and support business success.

5 eco-conscious changes driven by technology

Do you like the idea of satisfying your needs without jeopardizing the resources and opportunities of future generations?

Healthtech to outshine market expectations next year

Even if there is a hiring slowdown across the entire tech sector as a whole, the best and brightest will likely flow into healthtech firms.

NASA style reinvention: Major changes in the pharma industry

A successful vaccine for a new pathogen and over 10 billion doses distributed shifts how people see the pharma industry.

CPA firm issues SOC 2 – Type 2 report Source Meridian

Our SOC 2 report has shown that we have appropriate controls to mitigate risks associated with our services.

“The doctor will see you now”: How AI is disrupting health tech

We collaborate with global health tech firms, boosting their AI and ML projects. The AI industry is set to surge by over 1100% from 2020 to 2028.

The DevOps Ice cream: 6 flavors that you should taste!

When we start to talk about DevOps people usually say that the term as it is is too broad or complex to have any practical implication.

Unleashing sales potential: Machine Learning in telemarketing

The Machine Learning revolution has taken the tech industry by storm, especially Deep Learning (based on Artificial Networks).

Artificial intelligence: Enriching people’s way of life

Who would have thought that technology would become such a fundamental part of our everyday lives?

Key motivations for embrace Data Science

By utilizing semantic analysis, patterns, statistics, mathematics, and machine learning, data science identifies patterns within extensive data.

Unpacking UI and UX: Exploring language and applications

To help you better understand UI and UX development, we’ll define these terms and explain their differences and purpose.

We’d love to hear
from you!

At Source Meridian, we are always looking for talented individuals who
share our passion for innovation and technology.

Categorised in:

This post was written by Santiago

Comments are closed here.