In mature markets, predictive analytics has been proven to transform business data into actionable insights for enterprises to build their future growth plans. An advanced facet of business intelligence and predictive analytics takes corporate decision-making to the next level. But can developing countries also springboard off it and apply the technology-enabled assumptions for new areas of development?
Predictive analytics uses historical data to predict future events. Typically, historical data is used to build a mathematical model that captures important trends. That predictive model is then used on current data to predict what will happen next or to suggest actions to take for optimal outcomes.
Interestingly, this segment has come into its own in recent years due to advances in supporting technology, particularly in the areas of Big Data and machine learning across industries. One of the primary drivers of this trend has been digital transformation initiatives.
With the assistance of Oxford Economics, SAP recently concluded a study on digital transformation that surveyed more than 3,000 senior executives from around the world, including emerging economies like Pakistan. 84% of those surveyed believed that digital transformation was key to their company’s future survival. Companies that had substantially enacted their digital transformation process experienced an average rise of 23% in revenue, while 80% of these early adopters expected increased profitability and 85% increase in market share.
As the emphasis on digitalization takes root across services and sectors in emerging economies like Pakistan’s, analytics is becoming more relevant and more usable. By building forecast models that are economy-based and multidimensional, developing countries like Pakistan can understand country-level indicators including risks, metrics for investment, inflation, and opportunities to build a plan for growth.
Customer retention strongly linked to quality of customer data analysis
The Pakistan telecom landscape has emerged as one of the most dynamic business sectors in the country where cell phones have become a household utility. This present scenario is a battlefront for a large number of private telecommunication companies, where the government’s decision to deregulate the telecom sector has paved the way for a number of applicants to provide cellular service.
This situation is now being altered due to the maturity of the telecom markets and the resulting cut-throat competition, which has caused the churn rate to be examined with a focused and systematic approach. Effective churn management allows an operator to stay ahead of competitors, increase profitability and improve investor confidence.
Typically, the workflow for a predictive analytics application imports data from varied sources, such as web archives, databases, and spreadsheets. By aggregating different data sources together, an accurate predictive model based on customer patterns can be developed.
The telco industry can recruit more subscribers, but more importantly, it can harness predictive analytics to retain them and offer specialized services based on user segmentation such as influencers, key users, power users, etc.
With a population of over 220 million people and a massive subscription customer base, Pakistan demonstrates the effectiveness of predictive analytics. Historical data in the telecom sector is used to measure customer behavior and understand how to retain customers instead of having a heavy churn rate.
Data analysis and predictive models create new revenue streams
Developing economies like Pakistan’s could benefit the most from analysis of available Big Data as they are likely to see the greatest gains. Not only are these countries starting from a low level of information-based decision-making, but they also tend to experience rapid development of their infrastructure and systems.
But the big question is: How do we get the most out of analysis and what is the accuracy of this analysis? This is a critical factor toward building predictive models that could potentially be applied to resolve economic and political issues.
The initial demand for an IT-specific skillset was created because the industry was becoming more data- than voice-driven. And with the digital era taking shape, businesses began to capture data in the form of user and consumer behavior, and this data was being stored in large data centers.
The infrastructure at the back end, as a result, began to change and the need to analyze and utilize Big Data started driving demand for data scientists with a specific skill set who can analyze and guide predictive models based on collected data. All domains are now looking at data scientists to get best results from their historical data and use the information to realize revenues and flow of revenues from existing to new business streams.
Industries drive momentum
Analytics has been instrumental in transforming many sectors. For instance, consider the analog music industry, which has been all but obliterated by digitalization. Without predictive analysis, older business models risk of losing out in the future.
Ride-sharing platforms such as Uber is another example of how disruptive organizations are leading the way with innovations and continuous improvements based on in-depth data crunching. And just as Google matches advertising demand and supply and varies price, predictive analytics can be a game-changer for developing nations.
The oil and gas sector is another example of what we can call an early mover into analytics. From data collection, analysis, statistics, modeling, and deployment, the predictive analytics process is being used in predictive maintenance, forecasting, energy trading, buying and selling, risk management, and optimization.
As Big Data analytics gets smarter with location-based services, it spells a prospective overhaul in the government sector. Mobile devices create a citywide sensing network, producing a rich layer of location data which is being used by governments to learn about issues, push notifications to relevant residents, and provide access to goods and services within a given area.
In our dealings with the government, banking, and telecom sectors, the conversation is increasingly about data analytics to build future-proof models for economic transformation.
Working smarter is the key to gaining value
The use of siloed analytics tools is now migrating to embedded analytics, an approach that simplifies business intelligence (BI) by embedding it directly into operational applications and business processes. Most database companies have the data managed by a BI platform placed directly within the application user interface to improve the context and usability of the data.
Chief digital officers are also increasingly helping companies drive growth by converting traditional “analog” businesses to digital ones, overseeing operations in the rapidly changing digital sectors including predictive data analytics.
This indicates that gaining value from analytics, especially predictive analytics lies in working smarter, rather than simply investing more in buying technology.
And although how much we can rely and invest on speculated trends pointed out by data scientists could be a matter of continued debate, the truth is that the scope for disruption and growth lies in balancing investments with speculation using data analysis predictively.
For more on the emerging economy of Pakistan, see Creating Momentum For Digital Transformation In Pakistan.