Johannesburg, 25 Sep 2019
Werner Tenten, Genii Analytics Business Development Director, provided an overview of the journey and design criteria that have played a role in the development of the Genii Analytics Quantum Predict product over the past two years.
The Quantum Predict product concept was initiated by the question: how can we help to reduce calls but improve the customer experience within call centres?
Quantum Predict is an artificial intelligence enabled product that is designed to predict future interactions from call data. Genii Analytics has focused on the prediction of a “next interaction” and, in particular, future interactions of churn and repeats.
The Quantum prediction product is designed to deliver a business return by enabling proactive communication with clients, improving the customer experience (CX) and identifying self-service opportunities that assist to drive down operational costs.
In order to comply with clients' data security requirements, Quantum Predict is designed to work either in the cloud or as an on-premises solution.
The product ingests the interaction data and relevant metadata, which is prepared for modelling. The product is designed to include automated calibration and competitive model selection; the results are presented in the form of visualisation and executable reports in the form of CSV files. This information will include who should be contacted, about what and by when.
The success of a project is dependent on the execution of a defined set of project phases. The design of a Predict solution starts with the identification of a clearly identified business problem and a defined ROI. A data discovery process will identify the data that is available and in sufficient volume. Once the data has been prepared for processing, the models are supplied the data, outputs are evaluated, accuracy assessed, and models adjusted. The models are designed to compete and auto select in order to ensure continuous improvement.
Genii Analytics artificial intelligence products are designed to support contact centres to improve the customer experience of clients by enabling proactive communication. A case study of a Genii Analytics prediction solution for a media business with more than 10 million subscribers provided positive insights.
The 40% of the media company’s call centre volume was due to technical support calls. These calls made up more than 70% of the contact centre’s cost-to-serve expense. The reduction of calls provided a return on investment for the project exceeding 300%.
The learnings from this case study highlighted the need to confirm the business problem and the metric that will be used to evaluate success. In this case, the objective was call reduction and the metric was the value to the business of the savings. This clarity determined that the project had been a success.
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Genii Ai
Genii Ai started four years ago to R&D, innovation and analytic platforms to improve customer service, sales and retentions for B2C companies. During this process, Genii developed the first Ai Prediction models to provide future customer behavioural prediction models that could integrate to digital RPA platforms and chatbot platforms. Genii leverage NLP, Text Analytics and machine learning on platforms such as Google, AWS and Azure for the prediction models.
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