Making sense of the Lockdown Climate Through Data Driven Insights


The year 2020 was characterised by a lot of uncertainty which was largely brought about by the Coronavirus pandemic. The unpredictability that followed the March 27th lockdown largely loomed over the country for most of the year. In order to survive the unknown conditions, rapid changes in social behavior amongst the general population was required for the adaption to the different stages of lockdown in the country. These changes made it difficult for companies to adjust and keep up with the variability in market trends.

Opti-Num, which has been tasked by a local telecommunications provider to manage one of their most valued price plans, sought to use advanced data analytics to create value and search for structure in the chaos of the business landscape during the lockdown. The Opti-Num advanced business analytics team understands that necessity is the mother of invention, and the restrictions brought about by the lockdown would help develop possibilities using creative new ideas presented from the insights given by the new social behavioral changes in the subscriber base.


One of the core consultancy principles of Opti-Num as a company is to walk with our clients through every step of the process. Working beside our clients allows us to not only carry out data analysis from a retrospective point of view, but to also deliver key insights in real time. This enables the efficient implementation of more agile work techniques that achieve results in a prompt manner. The early days of the lockdown were the most challenging as the changes in subscriber behavior was so erratic and there was not enough reliable data to make informed decisions. Opti-Num worked closely with the local telecommunications provider to monitor the subscriber behavior throughout the process to get the most value for the price plan without losing its subscriber base.

The data driven decision making approach used was broken down into the 4 stages summarised below

  1. Investigate and Identify the problem: This involves analyzing the data to detect any form anomalies or new trends in the subscriber behavior based on well-defined and measurable KPIs. Carrying out deep dives into the data based on the KPIs provides insights into the actual causes of the problem. The causes of these problems can either be clearly defined or from speculative factors, which sometimes can add an extra layer of complexity to the analysis.
  2. Formulate a solution: Based on the insights obtained from the data analysis we are able to formulate possible solutions which are then passed over to the implementation phase.
  3. Implement the solution: Solutions are then adjusted to fit the portfolio of the price plan in a way that makes sense to both the subscribers and the telecommunications provider. This approach helps extract maximum value from product without losing the engagement of subscriber base.
  4. Monitor the progress: This stage involves the collection and analysis of the data to measure the success of the implemented solution. The data collected in this stage is also added to the existing data and is used to help reinforce future data driven decision making. This data is also used in the development of the predictive models.

As the lockdown progressed, we were able to develop more accurate models in the creation of pricing strategies from the data available using MATLAB. The models created enabled our team at Opti-Num and the telecoms provider to not only adjust to the subscriber behavior in a timely fashion, but also to help predict the behavioral changes and plan for them in advance.


Using a data driven approach has helped us at Opti-Num provide clear solutions to the telecommunications provider in the uncertain marketplace brought about by the corona pandemic. Creating predictive models for our pricing strategies has also helped provide a good experience for the subscribers which has led to better engagement with the price plan. Making use of the data driven decisions backed by the models created in MATLAB has brought about a level of confidence in the development of pricing strategies that best suit the subscribers, while at the same time adding great value back to the telecommunications provider.

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