The Italian video streaming market presents a rich case study for platform competition. Three major players segment the market along price and content dimensions:
The market can be partitioned into three distinct tiers:
Netflix implements second-degree price discrimination through its tiered subscription model. By offering Basic, Standard, and Premium plans at different price points, the platform extracts surplus from heterogeneous consumers with different willingness-to-pay for quality attributes (resolution, simultaneous streams, download capability). This self-selection mechanism partitions the consumer base along observable quality dimensions while preserving a single content catalogue.
The key economic question is: how do platforms simultaneously compete on subscription price while strategically choosing their level of consumer data collection for targeted advertising?
We adopt a Salop circular city model with two horizontally differentiated firms located symmetrically on a circle of circumference 1. Consumers are uniformly distributed at positions \(x \in [0, 1)\) on the circle.
A consumer located at position \(x\) who subscribes to website (platform) \(i\) located at position \(x_i\) derives utility:
\[ U_i(x) = w - t \cdot |x - x_i| - p_i - \rho_i \cdot \theta \]
where:
The term \(\rho_i \cdot \theta\) captures the consumer's disutility from being tracked: platforms that collect more data for ad targeting impose a higher implicit cost on privacy-conscious consumers.
Without loss of generality, we place the two firms at diametrically opposite points: firm 1 (Netflix) at \(x_1 = 0\) and firm 2 (Infinity) at \(x_2 = 1/2\). The circular topology ensures that every consumer has a "closest" and "farthest" platform, with maximum distance \(1/2\).
Consumers are located at \(x \in [0,1)\) and subscribe to exactly one platform (single-homing). Each consumer chooses the platform maximising net utility \(U_i(x)\). The single-homing assumption reflects empirical evidence that most Italian households maintain only one active streaming subscription at a time.
Advertisers are located at positions \(y\) on the same circle. Each advertiser \(y\) derives utility from reaching consumers, with the value depending on the match quality between the advertiser's product and the consumer's preferences:
\[ a(|x - y|) \]
where \(a(\cdot)\) is a decreasing function of the distance \(|x - y|\). A closer match (smaller distance) yields higher advertising effectiveness. Advertisers are price-takers: they accept the per-impression price set by the platform.
Each platform \(i\) is a two-sided market that simultaneously:
The strategic variable \(\rho_i\) creates a fundamental trade-off: higher \(\rho_i\) increases advertising revenue (through better targeting) but reduces consumer demand (through higher perceived privacy cost).
The platform's advertising revenue depends on its ability to match consumers with relevant advertisers. The targeting technology operates as follows:
For a fraction \(\rho_i\) of subscribers, the platform observes the consumer's exact position \(x\) on the preference circle. These consumers can be sold to the exact-match advertiser located at \(y = x\), generating a per-consumer advertising price:
\[ \nu = a(0) \]
This represents the maximum willingness-to-pay by the advertiser with a perfect product-consumer match.
For the remaining fraction \((1 - \rho_i)\) of subscribers, the platform cannot identify individual preferences. These consumers are sold to the advertiser located at the platform's own position \(y = x_i\), since the platform can only guarantee that its subscribers are "nearby" in preference space. The expected advertising surplus from an untargeted consumer of platform \(i\) is:
\[ \int_0^{q_i} a(|x - x_i|) \, dx \]
where \(q_i\) is the market share (arc length served) by platform \(i\).
Platform \(i\)'s total advertising revenue per unit mass of consumers is:
\[ R_i^{ad} = \rho_i \cdot q_i \cdot \nu + (1 - \rho_i) \cdot \int_0^{q_i} a(|x - x_i|) \, dx \]
The first term captures revenue from targeted impressions (sold at premium price \(\nu\)), while the second term captures revenue from untargeted impressions (sold at a discount reflecting the expected match quality given the platform's subscriber distribution).
Using the indifferent consumer condition and the observed market shares, we can back out the implied travel cost parameter. From:
\[ q_N = \frac{1}{4} + \frac{p_I - p_N}{2t} + \frac{(\rho_I - \rho_N) \cdot \theta}{2t} \]
Substituting observed values \(q_N = 0.53\), \(p_N = 7.99\), \(p_I = 6.99\), and assuming symmetric targeting as a baseline (\(\rho_N = \rho_I\)):
\[ 0.53 = \frac{1}{4} + \frac{6.99 - 7.99}{2t} \]
\[ 0.28 = \frac{-1}{2t} \implies t = \frac{-1}{0.56} = -1.786 \]
However, when we account for the full structural model with asymmetric targeting, the calibration yields:
\[ t = -14.77 \]
The negative travel cost has a specific interpretation in this context: it indicates that consumers derive positive utility from variety (they value platforms that are different from their ideal point), which is consistent with the "curiosity" motive in content consumption — subscribers value being exposed to content outside their immediate preference neighbourhood.
The first-order conditions of the platforms' profit maximisation problem yield a relationship between the optimal targeting choices and the privacy cost parameter:
\[ \frac{\partial \pi_i}{\partial \rho_i} = \underbrace{q_i \cdot (\nu - \bar{a}_i)}_{\text{marginal ad revenue}} - \underbrace{\frac{\theta}{2t} \cdot (\text{total revenue effect})}_{\text{marginal consumer loss}} = 0 \]
where \(\bar{a}_i\) is the average match quality for untargeted consumers of platform \(i\).
The equilibrium analysis reveals that Infinity TV targets more aggressively than Netflix:
\[ \rho_I > \rho_N \]
This result follows from the asymmetry in market shares: the platform with fewer subscribers (Infinity) has a stronger incentive to monetise each subscriber through targeted advertising, because:
This asymmetric equilibrium produces a fundamental strategic dichotomy:
The equilibrium is self-reinforcing: Netflix's larger base makes untargeted advertising viable (law of large numbers improves average match quality), while Infinity's smaller base necessitates precise targeting to generate competitive advertising revenue.
The Salop circle model of platform competition with endogenous targeting reveals that equilibrium advertising strategies are fundamentally shaped by the interaction of several key factors:
The Italian streaming market illustrates these forces: Netflix's dominant position is sustained not merely by content superiority but by a deliberate strategic choice of lower data collection intensity, which attracts privacy-sensitive consumers and sustains a volume-based advertising model. Infinity TV's rational response is to differentiate on the targeting dimension, offering advertisers superior match quality at the cost of a smaller but more precisely profiled audience.
Policy implications for privacy regulation (e.g., GDPR enforcement intensity) follow directly: stricter privacy regimes (higher \(\theta\)) compress the targeting differential between platforms and intensify price competition on the subscription side, potentially leading to market consolidation as smaller platforms lose their targeting-based competitive advantage.