My wife had a hearty laugh on my account when I showed up with a fancy-looking cough syrup a couple of evenings ago. They have sold you the most expensive kind, she said, in amusement.
Suffering a slight dry cough at the end of a bout of flu, I popped into a chemist on my way home. May I have cough syrup please, I asked the young man behind the computer on a counter.
What kind of cough is it, he inquired? Dry, I said. How long? A couple of days, I reported. When is it worst, morning or evening, he quizzed, concern in his voice? Mmmm, morning I said, hesitation evident. He gave me a propolis-based syrup.
My wife’s mirth got us talking about price discrimination, a strategy where businesses charge different prices to different customers for the same or similar product, based on their willingness to pay rather than actual cost.
To maximise profit, businesses target different market segments with tailored pricing. Legal in most scenarios, the practice exploits data-driven consumer segmentation and can exacerbate inequality. Common examples include prices of airline and train tickets, pharmaceutical products, and movie tickets.
For a business to successfully practice price discrimination, three conditions must be met. First, the seller must be a price maker (such as a monopoly or oligopoly) with the ability to set prices rather than simply taking them from the market.
Second, a firm must be able to identify and separate different groups of consumers based on their price sensitivity (elasticity of demand).
Third, the seller must prevent customers who buy at a lower price from reselling the product to those who would otherwise pay more.
To sound learned, we economists categorise this practice into three levels: perfect, second-degree and third-degree. In perfect price discrimination, you charge the maximum price a customer willing to pay. Volume discounts are a good example of the second, while third-degree uses identifiable traits such as age, location or status to determine price.
Clearly, the young man at the chemist had politely, and perfectly sized me up, and acted accordingly.
The practice increases overall market efficiency by allowing people who would otherwise be priced out by a single uniform price, to access a product. For producers, it maximises revenue and helps recover high fixed costs. But, it can also raise fairness concerns or lead to consumer distrust.
Voter segmentation
Similar practices go on in political messaging in Kenya. Evidence shows that messaging is evolving from traditional broad ethnic mobilisation into segmented, data-driven strategies. Politicians are now using digital footprints to categorize voters by age, geography, and socio-economic priorities.
Demographic segmentation is focusing on Gen Z and millennials. They are expected to be the largest (56 percent by some estimates) voting bloc in 2027. As evidenced by the 2024 Gen Z uprising against the finance bill, messaging for this group prioritises issue-based politics, such as unemployment and corruption, over traditional ethnic loyalties.
It is great for issues to trump ethnicity, but old habits die hard. Politicians are still talking about geographic and ethnic zoning, splitting the country into “strongholds” where they agree not to sponsor competing candidates within coalitions to maintain their base.
Recent trends, particularly in Mt Kenya, show leaders reverting to ethnic appeals to consolidate support ahead of 2027. This risks community isolation, ethnic animosity, and violence.
Campaigns are segmenting audiences by language preference. Apparently, content performs up to 40 percent better when localised into Kiswahili or specific Kenyan languages, particularly for rural audiences reached via WhatsApp.
Voters are also being segmented based on their primary social platform usage: X and Instagram are for urban, tech-savvy millennials and Gen Z. Messaging here focuses on policy infographics. TikTok has become the primary tool for viral, creative messaging. Complex issues are converted to memes and short-form videos optimised for virality.
WhatsApp and Facebook are dominant for rural populations and older voters. Messaging here relies on voice notes in local languages, testimonials, and video endorsements from respected local voices.
There is evidence of data-driven micro-targeting. By using data to identify audiences at an individual level, personalised messages are targeted based on specific ideological commitments or political leanings. The posts popping up in your timeline are not random.
To guard against abuse the National Cohesion and Integration Commission and the Communications Authority are, at least in theory, keeping a watchful eye, using AI tools to monitor and track harmful content.
@NdirituMuriithi is an economist and Partner at Ecocapp Capital, an advisory firm. He is also the chairman of KRA and former Governor of Laikipia County. Email: [email protected]
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