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Data Science and ML

Finding a Purple Swan with Predictive Analytics

Every business aims for growth, but the ultimate prize is finding a “purple swan” — that rare yet foreseeable event offering unparalleled rewards. Predictive analytics has emerged as a powerful tool to produce those results. Through the lens of predictive analytics, businesses can spot purple swans on the horizon, set their course, and sail toward a future rich with promise.

Originally, predictive analytics was a tool for risk mitigation, helping businesses shield themselves from unforeseen events. As far back as the 1600s, Lloyd’s of London used basic predictive analytics to underwrite insurance for ships at sea. These models relied on limited historical data, making their predictions relatively straightforward. The goal was financial security and, with fewer variables to consider, that goal was often achieved.

Predictive analytics sat on the fringes of business strategy for hundreds of years, until reliable data became more readily available. Decision-makers often settled for obvious insights, like knowing pumpkins would sell at Halloween, but they overlooked the potential to optimize pricing or inventory to maximize sales. Predictive analytics is now central to achieving profitability.

The film “Moneyball” illustrated how predictive analytics, through sabermetrics, could transform a struggling baseball team into a success story. This sparked interest across various industries, from retail to pharmaceuticals to fintech. If analytics could predict player performance on a budget, why couldn’t it guide businesses in adapting to market trends?

While it’s essential to prepare for unexpected events, businesses should also focus on spotting opportunities, the purple swans that promise significant rewards.

The insights are in the data

Businesses operate on troves of data — Veritas reports the average enterprise stores 10 petabytes, or 23.1 billion files. It’s not all being used effectively. Not even close. A Gartner survey revealed only 44% of data and analytics leaders say their team is effective in providing value to their organization.

Businesses struggle with translating this data into valuable forecasts that anticipate market shifts and provide a competitive advantage. When combined with artificial intelligence (AI), predictive analytics has catalyzed a revolution in making data more insightful. This fusion injects agility into decision-making, predicts potential successes, and unveils purple swans. 

AI’s trajectory is unprecedented and supercharges the next generation of predictive analytics. Broadly, the AI market is poised to grow at a compound annual growth rate of 36.8% until 2030, creating a gargantuan $1.35 trillion in projected revenue. Applied to predictive analytics, AI has the potential to be a huge business enabler. 

Imagine sales strategies that adjust in real-time, responding to predictive cues and conjuring scenarios that captivate customers. For example One of Altimetrik’s clients, a leading fashion retailer, stands as an embodiment of this skill. Leveraging live store sales data, its engineering team introduces fresh designs and predicts inventory shortfalls. This data-powered methodology leads to producing 50% of units mid-season, a notable contrast to competitors’ modest 20%. Today, this retail conglomerate proudly touts the lowest year-end inventory among its peers, paired with swift trend rollouts and robust sales turnovers.

Applying the findings

Through predictive analytics, businesses gain proactive strategies. A leading U.S. bank harnessed predictive analytics to uncover hidden patterns and capitalize on untapped cross-sell and upsell opportunities. As a result, the bank’s book size swelled by roughly 20%, yielding a remarkable 46% increase in cross-sell conversion and a staggering 83% surge in average revenue per call.

It’s not just about finding new revenue streams, though. Predictive analytics can overhaul entire business models. For instance, a financial technology company transformed from a rule-based risk management system to a predictive, intelligent network that negates risk proactively. This led to a monumental 30-time reduction in fraud transactions.

The pharmaceutical industry echoes the efficiency driven by predictive analytics. Employing simulation models, pharmaceutical companies expedite medicine delivery, removing a crucial bottleneck from the pharma value chain. Rapid pattern generation empowers smoother demand management and diminishes manufacturing risks.

Where predictive analytics is headed

Predictive analytics has evolved from a supplementary tool to a cornerstone for maintaining a competitive edge. When integrated with the rapidly advancing fields of AI and machine learning (ML), businesses are not merely adapting to change but actively shaping new frontiers. 

This synergistic alliance turns what could be paralyzing uncertainties into actionable opportunities. Leveraging data to become an opportunity-seeking trailblazer exemplifies the digital evolution we’re witnessing and helps identify new purple swans.

For instance, the synergy between predictive analytics and AI is steering us toward precision drugs, in which the suitability, dosage, and efficacy of a drug are tailor-made for a specific individual. The standard of care may eventually be drugs manufactured with predictive analytics, supported by AI/ML models based on an individual’s genomic build. 

As futuristic as precision medicine sounds, it could just be the beginning of a new wave of purple swans. Instead of rare events where businesses hit the jackpot, they could become far more common with the help of predictive analytics.

About the Author

Vijay Veerra is a Principal Consultant of Business Solutions and Research with Altimetrik. He has 15 plus years of experience across multiple domains and has been responsible for strategizing digital transformational initiatives and helping enterprises achieve their business objectives. He is passionate about technologies and business models that bring transformative benefits to an organization.

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