Showing posts with label predictive analytics. Show all posts
Showing posts with label predictive analytics. Show all posts

23 July 2025

Four I's

In the vast and ever-expanding digital realm, understanding user behavior is paramount for any entity aiming to thrive. Beyond simple metrics like page views, a deeper comprehension of "Interest," "Intent," "Influence," and "Impact" provides a crucial framework for strategic decision-making across advertising, social media, business, and content creation. These four pillars offer a holistic view of digital engagement, guiding efforts towards meaningful outcomes.

Interest signifies a user's initial engagement and curiosity. It's the spark that draws attention to content, products, or services. For advertising and publishing, interest is measured by metrics like click-through rates (CTR), time spent on page, scroll depth, and content consumption rates. On social media and for news outlets, it manifests as likes, shares, comments, and video views. Understanding what piques audience interest allows creators to tailor content, refine headlines, and optimize visuals, ensuring their message resonates with the target demographic. Contextual signals such as demographic data, past browsing history, and content categories consumed are vital in identifying and nurturing this initial spark.

Intent moves beyond passive interest to reveal a user's underlying goal or desired action. This is where casual browsing transforms into purposeful interaction. In advertising and business, intent is evident in specific search queries ("buy running shoes," "best CRM software"), visits to product pages, adding items to a cart, or initiating contact forms. For influencer marketing, intent is seen when followers click on affiliate links or use promotional codes. Measuring intent involves tracking conversion rates, bounce rates from critical pages, form submissions, and direct purchases. Analyzing user journeys and segmenting audiences based on their declared or inferred intent allows for highly targeted messaging and optimized conversion funnels. Device type and location can also be strong indicators of immediate intent.

Influence speaks to the capacity of an entity or individual to shape opinions, drive discussions, and inspire actions within a network. This is the engine of virality and the core of PR and influencer marketing. Influence is not merely about reach; it's about the quality of engagement and the ripple effect. Metrics include sentiment analysis of brand mentions, share of voice in online conversations, referral traffic from social shares or influencer posts, and the rate at which content is re-shared or cited. For media and news, influence is reflected in how widely their stories are picked up, discussed, and acted upon by the public. Understanding an entity's influence helps in identifying key opinion leaders and amplifying messages through trusted channels.

Finally, Impact represents the ultimate, measurable outcome of all digital efforts, directly aligning with business objectives. This is the return on investment (ROI) that validates strategies. For advertising and business, impact translates to revenue generated, qualified leads acquired, customer lifetime value, or market share growth. In PR, it's about brand lift, reputation enhancement, or crisis mitigation. For publishing and news, impact might be measured by subscriptions, audience loyalty, or the tangible effect their reporting has on public discourse or policy. For virality, impact is the exponential spread and sustained relevance of content. Measuring impact requires connecting digital activities to real-world business results, often through attribution models and comprehensive analytics that go beyond vanity metrics.

These four "I's" are not isolated but interconnected, forming a continuous feedback loop. Interest can lead to intent, which, when fulfilled, can cultivate influence, ultimately driving significant impact. By meticulously measuring and analyzing these dimensions, organizations can move beyond guesswork, crafting data-driven strategies that resonate with audiences, convert prospects, and achieve tangible success in the dynamic digital landscape.

1 June 2023

Credit Reference Agencies

Credit scoring has been a very old area with very little change over the times. With the advancements in AI, credit and risk scoring needs to change significantly. Especially, as majority of the models in industry tend to be a well kept secret from the customer with drawbacks of significant biases. There are also no form of explanations provided to help the person improve their credit scores in a constructive feedback. At times, these credit reference agencies also hold incorrect data on people which impacts credit scoring reports. Verification and validation workflow checks are poorly structured. And, many times the same errors reappear in the aggregation process. Many such credit scoring models also do not include assessments for bias, fairness, and harms into the equation. They are inherently used as subjective criteria. The many models that are out there make it even more of a mess. Most credit scoring agencies are not really good at what they do. A small error could take years to correct on the credit file. Sometimes they mix up credit activity by linking people incorrectly. Sometimes it may be the case that someone else is using the profile to apply to lenders. Are such approaches really a fair process of determining someone's creditworthiness? What if the person has a history of paying on time but due to the downturn they fall into a temporary blip? They are essentially a flawed measure. Many wealthy people also at times fair poorly on credit scores. Not only correct data is important but the right type of data to show the full picture is too. Even looking at such things as cashflow is a bad idea as invariably leaving huge sums of reserves in current accounts will get eaten away through inflation. Even savings accounts don't generally carry very good returns compared to the impact of inflation nor are they a subject of credit scoring criteria.  Basing everything on a credit score is not only unfair but not an objective way of assessing creditworthiness.

10 February 2022

Organizational Web of Data

There are a lot of terms flying around in the data world. Many organizations struggle to find ways of effectively tackling their insurmountable data growth where they want to be able to constructively derive insights and meet their digital transformation needs. Terms like data hubs, data mesh, data fabric, and data lakes have been thrown around in many organizational data architectures. However, many don't utilize the concept of linked data in most of their data architecture frameworks. A data fabric could be used to cover for the semantic knowledge graph that has a data centric view of the organization. And, a decentralized data hub could be extended from this to cover for business functions that form a connected linked data in the conceptual world of organizational web of data. Each business function can then serve, protect access, and make available to share queryable linked data internally within the organization. A further transposition could be made in the context of a focused data mesh that acts as a data as a product strategy for defining a more focused view for analytics and business intelligence requirements. Over time organizations form into internal abstractions of a linked data world across their business functions and have a very connected view of all things with a flexible control for governance and provenance.

1 October 2019

Marketing Mix

  • Packaging
  • Partnership
  • Passion
  • Penetration
  • People
  • Perception
  • Personality
  • Persuasion
  • Phrases
  • Physical
  • Place
  • Placement
  • Planning
  • Popularity
  • Population
  • Positioning
  • Positiveness
  • Power
  • Pragmatism
  • Preference
  • Price
  • Privacy
  • Process
  • Product
  • Productivity
  • Professionalism
  • Profit
  • Promotion
  • Prospect
  • Publicity
  • Purchase
  • Push-Pull
  • Picture
  • Part
  • Pilot
  • Persona
  • Peers
  • Pass-Along-Value
  • Party
  • Pandemic
  • Pandemonium
  • Pain
  • Placebo
  • Planting
  • Playfulness
  • Pleasure
  • Plot
  • Politics
  • Praise
  • Prediction
  • Premeditation
  • Press
  • Pressure
  • Preview
  • Principle
  • Prominence
  • Promise
  • Proof
  • Properties
  • Prosperous
  • Protection
  • Purple Cow
  • Purpose
  • Production

19 August 2019

Applications of Data Science

  • Anomaly Detection
  • Assistive Services
  • Auto-Insurance Risk Prediction
  • Automated Closed Captioning
  • Automated Image Captioning
  • Automated Investing
  • Autonomous Ships
  • Brain Mapping
  • Caller Identification
  • Cancer Diagnosis/Treatment
  • Carbon Emissions Reduction
  • Classifying Handwriting
  • Computer Vision
  • Credit Scoring
  • Crime: Predicting Locations
  • Crime: Predicting Recidivism
  • Crime: Predicting Policing
  • Crime: Prevention
  • CRISPR Gene Editing
  • Crop-Yield Improvement
  • Customer Churn
  • Customer Experience
  • Customer Retention
  • Customer Satisfaction
  • Customer Service
  • Customer Service Agents
  • Customized Diets
  • Cybersecurity
  • Data Mining
  • Data Visualization
  • Detecting New Viruses
  • Diagnosing Breast Cancer
  • Diagnosing Heart Disease
  • Diagnostic Medicine
  • Disaster-Victim Identification
  • Drones
  • Dynamic Driving Routes
  • Dynamic Pricing
  • Electronic Health Records
  • Emotion Detection
  • Energy-Consumption Reduction
  • Facial Recognition
  • Fitness Tracking
  • Fraud Detection
  • Game Playing
  • Genomics and Healthcare
  • Geographic Information Systems
  • GPS Systems
  • Health Outcome Improvement
  • Hospital Readmission Reduction
  • Human Genome Sequencing
  • Identity-Theft Prevention
  • Immunotherapy
  • Insurance Pricing
  • Intelligent Assistants
  • Internet of Things and Medical Device Monitoring
  • Internet of Things and Weather Forecasting
  • Inventory Control
  • Language Translation
  • Location-Based Services
  • Loyalty Programs
  • Malware Detection
  • Mapping
  • Marketing
  • Marketing Analytics
  • Music Generation
  • Natural Language Translation
  • New Pharmaceuticals
  • Opioid Abuse Prevention
  • Personal Assistants
  • Personalized Medicine
  • Personalized Shopping
  • Phishing Elimination
  • Pollution Reduction
  • Precision Medicine
  • Predicting Cancer Survival
  • Predicting Disease Outbreaks
  • Predicting Health Outcomes
  • Predicting Student Enrollments
  • Predicting Weather-Sensitive Product Sales
  • Predictive Analytics
  • Preventative Medicine
  • Preventing Disease Outbreaks
  • Reading Sign Language
  • Real-Estate Valuation
  • Recommendation Systems
  • Reducing Overbooking
  • Ride Sharing
  • Risk Minimization
  • Robo Financial Advisors
  • Security Enhancements
  • Self-Driving Cars
  • Sentiment Analysis
  • Sharing Economy
  • Similarity Detection
  • Smart Cities
  • Smart Homes
  • Smart Meters
  • Smart Thermostats
  • Smart Traffic Control
  • Social Analytics
  • Social Graph Analytics
  • Spam Detection
  • Spatial Data Analysis
  • Sports Recruiting and Coaching
  • Stock Market Forecasting
  • Student Performance Assessment
  • Summarizing Text
  • Telemedicine
  • Terrorist Attack Prevention
  • Theft Prevention
  • Travel Recommendations
  • Trend Spotting
  • Visual Product Search
  • Voice Recognition
  • Voice Search
  • Weather Forecasting

29 March 2019

AI Ethics

There are a lot of people claiming to be AI ethics experts, but the field is only just emerging. In mainstream, the topic has only been around for a short while. So, how can someone be an expert in it as there is still lots of unanswered research questions in area?

  • How can one focus on ethics without also focusing on morals? Morals are the basis of ethics?
  • Does ethics in AI, intrinsically, have a universal equivalence? i.e something that is codified as ethical in west may not be sufficiently compatible for the east. What attributes in ethics form for-all-there-exists vs for-some-there-exists as an existential quantification?
  • How can one control the abuse and falsely manipulated justification of AI ethics? i.e someone trying to drive political/cultural change/influence in a society/organization using AI ethics?
  • How do you make sure that the people in control of ethics, who are by their own accounts calling themselves as ethical experts, are in fact ethical? AI is only as ethical as the human that programmed it? Can the codification of AI ethics be programmed to mutate as defined by the environment and changing norms of society? In so doing, allowing the AI agent to question the ethical and moral dilemmas for/against humans?
  • If one builds a moral reasoner in horn clauses, can such reasoning then genetically mutate for ethics, on a case by case basis, for conditioning of an AI agent? Can AI agents be influenced by other AI agents, like in a multiagent distributed system - argumentation via game theory, reinforcement policies, towards mediation and consensus?
  • Can ethics and morals be defined in a semantically equivalent language?
  • If one defines horn clauses for moral reasoning and a set of ethical rules, can such moral/ethical conundrums be further defined using markov decision processes, in form of neural network, for any and all states as a good enough coverage for a global search space that can be further reasoned over with transfer learning?
  • How do you resolve human bias in a so called AI ethics expert?
  • Who defines what is ethical and moral for AI? Is there an agreed gold standard of measure?

In general, a moral person wants to do the right thing with a moral impulse that drives the best intentions. Morals define our principles. While ethics tend to be more practical towards a set of codified rules that define our actions and behaviors. Although, the two concepts are similar, they are not interchangeable nor aligned in every case. Ethics are not always moral. While a moral action can also be unethical.

AI Ethics Lab