The transformation digital technology brings to all business functions
E-commerce types: B2B, B2C, C2C, D2C — and their business models
Big data: the 3Vs and how businesses use data to gain competitive advantage
Artificial intelligence and automation — where they create and destroy value
Social media and digital marketing — new metrics and channels
Risks of digital transformation: cybersecurity, data ethics, disruption
Digital transformation appears throughout Paper 2 and Paper 3 as a strategic force reshaping competitive advantage
Expect questions on how digital disrupts existing industries and incumbents
Business to Business. Suppliers selling to retailers. High volume, long contracts.
Business to Consumer. Amazon, Tesco online, ASOS. Largest sector by transactions.
Consumer to Consumer. eBay, Vinted, Facebook Marketplace. Platform provides the market.
Direct to Consumer. Brand bypasses retailers. Higher margins, owns customer data.
Consumer to Business. Freelancers, influencers offering services. Growing model.
Government to Citizen. Tax returns, benefits, NHS appointments online.
24/7 global trading — no opening hours or geographic limits
Lower cost base — no physical retail space, fewer staff at point of sale
Rich customer data — every click, browse, and purchase is trackable
Personalisation at scale — show each customer a different version of your store
Faster international expansion — a website is accessible globally from day one
Intense price competition — consumers can compare prices instantly across rivals
Logistics and returns complexity — fulfilment and reverse logistics are costly
Trust and security — data breaches destroy customer confidence rapidly
Platform dependency risk — relying on Amazon/Google/Meta gives them power over you
Digital exclusion — older demographics may not engage with digital-only channels
Vast quantities of data — petabytes generated daily (social media, IoT, transactions)
Generated and processed at high speed — real-time or near real-time analysis
Many formats: structured (databases), unstructured (tweets, images, video, sensor data)
Some add a 4th V: Veracity — how trustworthy and accurate is the data?
Customer segmentation and personalisation (Netflix recommendations, Spotify Wrapped)
Dynamic pricing — Uber surge pricing; hotel rates by demand pattern
Predictive maintenance — sensors predict machine failure before it happens
Fraud detection — banks flag unusual transaction patterns in milliseconds
Supply chain optimisation — demand forecasting reduces overstock and stockouts
Handle routine queries 24/7 at fraction of human cost
ML models spot anomalies banks' rule-based systems miss
Amazon's Kiva robots pick, sort, and move goods faster than humans
Predict which users are most likely to convert and show ads only to them
NHS AI tools analyse scans faster and as accurately as senior radiologists
Draft reports, emails, product descriptions — human editing still required
Routine, repetitive tasks (data entry, basic customer service) most at risk of automation
Creates demand for new roles: data scientists, AI trainers, cybersecurity specialists
Net effect on jobs is debated — historically, technology creates more jobs than it destroys
Two-way conversation — customers talk back; brands must engage, not just broadcast
Viral potential — a single post can reach millions at near-zero cost
Micro-targeting — platforms allow targeting by age, location, interests, behaviour
Influencer marketing — consumers trust peer recommendations over brand advertising
Real-time feedback — sentiment analysis reveals what customers think immediately
Instagram/TikTok: visual, lifestyle, B2C — best for brand building and impulse purchases
LinkedIn: professional, B2B — lead generation, recruitment, thought leadership
YouTube: long-form content — tutorials, reviews, brand storytelling
X/Twitter: real-time conversation — customer service, news, crisis management
% of website visitors who complete a desired action (purchase, sign-up)
How much each click on a paid ad costs — efficiency of paid media
Revenue generated per £1 of advertising spend
Total marketing cost ÷ new customers acquired
Total revenue expected from one customer over their relationship with the brand
% of visitors who leave after one page — signals poor UX or irrelevant traffic
AQA insight: CLV > CAC is the fundamental profitability condition for any customer acquisition strategy
Clayton Christensen (1997): disruptive innovation starts at the low end of a market and eventually displaces incumbents
Digital disruptors enter via lower cost, better UX, or new business models — not incremental improvement
Netflix disrupted Blockbuster — subscription streaming vs physical rental
Airbnb disrupted hotels — asset-light platform vs owned properties
Uber disrupted taxis — dynamic pricing, no dispatch system, global scale
Monzo/Starling disrupting high street banks — no branches, instant notifications, open banking
Acquire the disruptor (Facebook bought Instagram, WhatsApp)
Build competing capability internally (expensive, slow, risks cannibalising existing revenue)
Partner with digital start-ups — access capabilities without full acquisition cost
Pivot business model before disruption arrives — requires vision and risk appetite
Data breaches: theft of customer data → regulatory fines (GDPR: up to 4% global turnover) + reputational damage
Ransomware: hackers encrypt systems and demand payment — NHS attack (2017) cost £92m
Phishing: employees tricked into revealing credentials — human error is the #1 vulnerability
Cost of a breach: average £3.4m per incident in the UK (IBM, 2023)
Consent and transparency: GDPR requires clear consent for data collection
Algorithmic bias: AI trained on biased data reproduces and amplifies discrimination
Data monetisation: selling customer data to third parties — ethically contested, legally restricted
Surveillance capitalism: business models that trade free services for data (Meta, Google)
Digital technology creates enormous value AND new risks — the exam wants both sides + a judgement
A clothing brand sells directly through its own website rather than through department stores. This allows it to capture higher margins and access customer data directly. This is BEST described as:
A bank uses machine learning to analyse millions of transactions per second and flag unusual patterns in real time. Which characteristic of Big Data does this PRIMARILY relate to?
Evaluate whether digital technology inevitably leads to job losses across the economy.