Next Wave: The perfect price discrimination

Picture the absolute wildest dream of a profit-maximising corporation. It is to charge every single customer exactly the maximum amount of money they have in their pocket at the moment they want to buy a product. Economists call this first-degree or perfect price discrimination. For most of human history, this was structurally impossible because a shopkeeper cannot easily look into your soul, guess your net worth, and magically change the price tag on a litre of milk before you reach the till. But a technology platform intermediating millions of transactions through a smartphone does not need to look into anyone’s soul. It just needs to look at your data.

A recent investigation by Consumer Reports in the United States found that Uber and Lyft routinely charge different customers wildly different prices for the same ride, requested at the same time, from the same location. The median gap between the highest and lowest prices was about 50%. The platforms vehemently deny engaging in what regulators are now calling surveillance pricing, which is pricing based on individual demographic or behavioural data, and instead claim it is just a reflection of real-time marketplace conditions. The functional result is the same, however, because the software sets a price based on what it thinks it can extract from you, specifically, at that exact second.

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The code goes to Kenya and Nigeria

This dynamic is fascinating in highly regulated Western markets, but it is entirely changing the foundational economics of emerging markets in Africa. Here, digital platforms are rapidly formalising historically informal economies, and they are using vast data networks to do it.

Take ride-hailing as a prime example. In markets like Kenya and Nigeria, international giants such as Uber and Bolt have deployed dynamic pricing software that determines both the passenger’s fare and the driver’s cut. The software is ostensibly an impartial market-clearing mechanism that raises prices when demand is high to coax more drivers onto the road and lowers them when it is quiet. But this system optimises for platform liquidity and revenue rather than macroeconomic stability. In Nigeria, petrol prices recently spiked by nearly 50% due to global supply disruptions and the removal of local subsidies. A human taxi driver would immediately raise their base fare to cover the fuel, but software only cares about the demand curve.

The automated pricing left Nigerian drivers with shrinking margins and effectively no control over their own unit economics, leading the Amalgamated Union of App-Based Transporters of Nigeria to launch strikes. The workers are realising that they are not independent contractors negotiating a market. They are variables in an optimisation function. The system is so rigid that competitors like inDrive have managed to secure 150 million downloads globally simply by treating price negotiation as a product feature rather than an automated output, letting drivers and riders haggle directly.

A bespoke megabyte just for you

The telecommunications sector is where this automated price discrimination becomes philosophically weird. In Kenya, Safaricom has invested heavily in predictive data analytics to pivot towards customer obsession. Using a platform co-developed with Huawei called Idea-to-Cash, Safaricom dynamically matches its subscribers with highly personalised data and voice offers. The system analyses a customer’s usage behaviour, spending patterns, and real-time context to generate bespoke “Just for You” packages.

This has been a massive corporate success for the telecom giant. Safaricom doubled its conversion rates on targeted packages, reduced time-to-market for new offerings by 90%, and drastically cut customer complaints about hourly data bundles. But consider that a megabyte of data is a pure commodity. It costs the network the same amount to deliver it to Customer A as to Customer B.

Safaricom is essentially isolating consumers into individualised micro-economies by employing data analytics to dynamically price telecom services. If the system knows you have a high willingness to pay based on past consumption, you will get a different deal than a highly price-sensitive student. The product remains standard, but the pricing becomes a behavioural trap. When everyone gets a personalised discount, the baseline price is essentially meaningless. It means the software has figured out exactly how much perceived discount is required to trigger a purchase from your specific psychological profile.

Your battery is low, so your interest rate is high

Nowhere is this digital alchemy more apparent than in African digital lending. Over the last decade, platforms like M-Shwari, Tala and Branch pioneered a model of providing uncollateralised loans to unbanked populations by replacing traditional credit bureaus with alternative mobile phone data.

The original thesis was brilliant in its simplicity. If you do not have a formal credit history, a program can ingest thousands of data points from your phone, including your mobile money transaction histories, the diversity of your contact list, how you text, and even your mobility patterns based on cell towers, to build a predictive credit scoring model. The benefits are undeniable: this tech has driven massive financial inclusion, expanding formal financial access in Kenya. A Harvard Business School study found that marginal borrowers randomly approved by Tala experienced significant improvements in financial well-being, especially when they used the capital for small enterprises.

But the cost of this automated access is astronomical, and the software is ruthlessly efficient at pricing risk.

Because administrative costs are practically zero, digital lenders can profitably lend tiny amounts to millions of people. However, because default rates are high, the system offsets this by charging what is effectively a poverty premium. The software learns exactly what exorbitant interest rate a desperate borrower will tolerate to secure short-term liquidity. This creates a doom loop where borrowers take out loans from one app just to pay off the massive interest of another. By 2025, borrowers had defaulted on 83.1% of digital loans of below KES 1,000 ($8), indicating the difficulty of recovering small loans from low-income households. The Central Bank of Kenya (CBK) eventually had to step in with the Business Laws Amendment Act to regulate the sector, mandate transparency, and stop lenders from simply extracting all the wealth from the bottom of the pyramid.

Recommending things you did not know you could not afford

It is not just ride-hailing, lending, and telecoms participating in this extraction. Big tech platforms across the continent are quietly deploying predictive software to shape consumer reality. Jumia, the pan-African e-commerce giant, relies heavily on personalised recommendation systems. The software analyses browsing behaviour, purchase history, and user preferences to tailor product suggestions in markets like Lagos, Nigeria.

In a Western market, a program suggesting a pair of shoes is just targeted marketing. In an emerging market characterised by severe socio-economic disparities, varying levels of digital literacy and trust deficits, a program dictating what products a user sees is effectively constructing a walled garden. The software learns what you can afford and simply hides the rest of the economy from you, and converts structural inequality into a perfectly optimised and frictionless digital experience.

The endgame of perfect extraction

Technology circles tend to view all this software deployment as an unalloyed good. To be fair, the benefits are very real. Standing in the rain in Nairobi, getting matched with a driver via a price surge is arguably better than being stranded. Needing inventory for your fruit stall, a 150% annual percentage rate loan approved by a computer in three seconds is better than dealing with local loan sharks. The efficiency gains are spectacular.

The problem, however, is the endgame. Predictive models deployed by profit-driven platforms are not designed to distribute social goods but to extract economic surplus. Surveillance pricing and automated product placement thrive in environments characterised by market failures and information asymmetries. In a perfectly competitive market, a telecom provider trying to charge you more for data because you are a power user would just prompt you to switch providers. But African digital markets are rarely perfectly competitive. They are dominated by tech oligopolies like Safaricom in Kenyan telecoms, Uber and Bolt in mobility, and massive banks in digital credit.

When these dominant firms deploy their proprietary software, the program ceases to be a tool for market discovery and becomes a tool for maximum extraction. Consumers have zero visibility into market benchmarks, while the platforms have perfect visibility into a buyer’s wallet and desperation. Eventually, the concept of a fair and standard price vanishes entirely. You are no longer participating in a broader economy but trapped in a bespoke and algorithmically generated simulation designed specifically to empty your pockets as efficiently as mathematically possible. The wildest part is that the software will convince you that you are getting a discount.

Kenn Abuya

Kenn Abuya is a senior reporter at TechCabal. He leads the Startups Desk.

Thank you for reading this far. Feel free to email kenn[at]bigcabal.com, with your thoughts about this edition of NextWave. Or just click reply to share your thoughts and feedback.

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