Predictive Analytics Summary

How much of your future are you comfortable having predicted—and who should have the ability to foresee it?

1. Predictive Analytics Offers Precision in Decision-Making

Predictive analytics (PA) helps organizations reduce risks and make well-calculated decisions by using algorithms to predict individual behavior. Unlike traditional marketing approaches, PA focuses less on mass appeal and more on predicting how specific people might react to specific situations. For instance, a marketing team could use PA to determine which ads resonate best with users of a particular demographic group.

PA generates predictions by analyzing a multitude of variables such as age, email patterns, or even search habits. The outcome—a predictive score—offers insights into the probability of certain actions. This score doesn’t guarantee an outcome but provides probable insights to guide decisions. Through machine learning and backtesting, such models refine their accuracy over time.

For example, companies can predict future stock trends by testing the model with old data. Similarly, online retailers might predict which products a consumer is most likely to buy during their next visit. By targeting individual preferences, businesses lower the likelihood of wasted investments.

Examples

• Financial institutions using PA to identify high-risk investments.
• Retailers analyzing consumer shopping data to create personalized recommendations.
• Online advertisers targeting users based on past engagement data.

2. Predictive Analytics Raises Ethical Concerns

The rise of PA prompts questions about privacy, fairness, and responsibility for its societal impact. For example, corporations like Target have used PA to identify personal details, such as which shoppers might be pregnant. Though this may enable relevant marketing, it risks leaking personal information inadvertently or intruding too heavily into private lives.

PA is also being applied in crime prevention, with some success in predicting potential areas or times for burglaries. While useful, applying predictive tools to individuals can lead to biased results, particularly when assumptions about one person are made based on group data.

One major issue is prejudice embedded in PA models. For instance, parole assessments using PA might assign higher recidivism scores based on a convict’s neighborhood data. This could unfairly penalize people from high-crime areas, perpetuating cycles of inequality and bias.

Examples

• Law enforcement using PA to patrol “hot spots” based on past crime data.
• Courts using predictive systems to assess parole candidates’ likelihood of reoffending.
• Public outrage concerning Target’s use of PA to predict and market maternity products.

3. Data Quantity and Balance Are Critical for Prediction

While data abundance nurtures PA’s potential, the quality and balance of data are equally important. Human activities—like online shopping, social media interactions, and even blogging—generate vast amounts of data daily. This surplus allows PA to make sophisticated guesses, but imbalance in datasets can lead to significant errors.

When datasets aren’t diverse or large enough, machines might misidentify patterns that seem relevant but are actually coincidental. A study once found people were less likely to buy faulty cars if they were painted orange, a correlation later debunked with more data.

Balanced datasets ensure accuracy, especially as industries adopt increasingly elaborate models. Adding varied data types—such as demographic characteristics and behavioral patterns—mitigates the chances of creating false patterns from noise.

Examples

• Businesses using phone and transaction histories to evaluate creditworthiness.
• Researchers unraveling faulty car data correlations by expanding datasets.
• Social networks analyzing millions of posts for meaningful advertising patterns.

4. Machine Learning Detects Hidden Risks

Machine learning equips PA to uncover risks that would typically go unnoticed. It identifies subtle losses—or “microrisks”—that may initially seem insignificant but compound to create larger issues over time. By continuously updating based on new data, the model adapts its predictions to long-term effects.

Chase Bank provided a prime example by using PA to analyze early loan repayments. What appeared as minor prepayment losses were identified as potential threats to long-term profitability. PA enabled them to respond proactively before losses escalated.

However, overlearning by machines can create its own problems. A professor once demonstrated data correlating Bangladesh’s butter production to stock market trends—a spurious connection. Allowing room for errors ensures models differentiate true patterns from meaningless links.

Examples

• Banks monitoring subtle repayment trends to protect earnings.
• Companies applying PA to spot unnoticed product defects early on.
• Machines detecting patterns in transaction spikes potentially linked to fraud.

5. Ensemble Models Amplify Predictive Power

Ensemble models, which aggregate insights from multiple predictive systems, enhance PA’s effectiveness. By combining different models, weaknesses in any single tool are outweighed by collective strengths. This framework fosters better performance over time.

In 2008, Netflix launched a crowdsourcing competition to improve its recommendation algorithm by 10 percent. The solution came from merging two competing teams’ models, showcasing the additive power of ensemble modeling. Today, models combined this way achieve performance boosts of up to 30 percent.

Ensemble modeling has turned PA into a collaborative field. Applications range from reducing dropped calls in telecommunications to improving fraud detection systems across industries like finance and government.

Examples

• Netflix enhancing user recommendations through ensemble methods.
• Telecommunications firms reducing dropped calls with ensemble analytics.
• The IRS using ensemble predictions to detect tax fraud.

6. Breaking Through Human Language Barriers

Understanding language is one of PA’s most challenging yet transformative projects. Human communication harbors subtleties, such as tone and sarcasm, that machines struggle to interpret. But advances in natural language processing are unlocking new possibilities.

IBM’s Watson represents a milestone. By combing decades of trivia data, it competed against Jeopardy! champions, blending ensemble models to interpret and respond better than humans or existing tools. Beyond winning Jeopardy!, technologies refined through Watson are forging paths in medical diagnoses and financial forecasting.

Today, PA-powered tools like Siri reflect Watson’s influence. While far from flawless, the technology has found its way into everyday devices, improving how people interact with machines.

Examples

• IBM’s Watson dominating Jeopardy! through language-based predictions.
• Siri interpreting smartphone user commands.
• Predictive models tackling customer service through chatbots.

7. The Uplift Model Quantifies Persuasion

PA is becoming increasingly sophisticated in understanding persuasion—predicting not only reactions but also whether someone can be nudged toward a certain choice. This technique is embodied in the uplift model, which evaluates which customers will respond positively to specific interventions.

Telenor, for example, uses uplift modeling to minimize negative effects on loyal customers when targeting at-risk customers. The model isolates responses, thereby protecting untargeted groups from accidental alienation.

From banks to telecommunication companies, uplift modeling maximizes efficiency while avoiding harm, driving up marketing effectiveness by as much as 36 percent.

Examples

• Telenor minimizing unintended consequences of customer outreach efforts.
• US Bank identifying customers most receptive to loan offers.
• Fidelity applying uplift models to target likely investors accurately.

Takeaways

1. Explore predictive modeling tools to optimize decisions and risk evaluations in your business or organization.
2. Ensure datasets for PA initiatives are diverse, balanced, and expansive to reduce false correlations.
3. Use ensemble models for complex problem-solving, blending collective strengths for better outcomes.