Unleashing sales potential: Machine Learning in telemarketing

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Unleashing sales potential: Machine Learning in telemarketing

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Sebastian Vasquez
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Sep 8, 2019
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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.

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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.

Sebastian-Vasquez
Written by Sebastian Vasquez

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