---
title: Top-of-Screen Promotions
url: https://www.emergentmind.com/topics/top-of-screen-promotions
type: topic
---

# Top-of-Screen Promotions

Top-of-screen promotions are a display strategy in digital platforms where prime above-the-fold banner space—typically the home screen’s most visible region—is allocated for promoting selected content or advertisements. This slot occupies a unique position for influencing user engagement and has significant operational and econometric complexities in both deployment and evaluation.

## 1. Platform Context and Design of Top-of-Screen Promotions

Prominent video streaming platforms such as ABEMA utilize a home screen “top-of-screen” banner area that alternates between promotional banners for target content and standard advertisements. ABEMA, a major Japanese streaming service with approximately 30 million weekly active users, exemplifies this structure. While the core function is content promotion, the slot may also be used for third-party advertising, with treatment assignment (promotion vs. standard ad) at the individual user level.

The target content types commonly include:
- Long-form, self-contained series (e.g., 46-minute comedy episodes)
- Short-form highlights (≈5 minutes, such as sports clips)
- Serialized narrative shows (≈40 minutes per episode, multiple episodes per series)

Randomized controlled trials (RCTs) have assessed promotional efficacy by randomizing exposure to the top-banner (D=1 for promotion, D=0 for standard ad), with approximately 10% of users allocated to treatment. The primary measured outcome is each user’s cumulative viewing time of the promoted series over the exposure window, often four weeks [2601.11185].

## 2. Data Structure, Outcome Measures, and Viewing Patterns

User viewing time distributions in this context exhibit nonstandard features: a dominant point mass at zero (users who do not engage with the content at all), spikes corresponding to multiples of episode lengths (reflecting episode-completion drop-off), and generally heavy right tails for engaged viewers. Mean viewing time in the control group demonstrates wide variation by content type:
- 46 min comedy: 0.244 min
- 5 min sports: 0.0353 min
- Long-form reality A: 0.657 min
- Long-form reality B: 1.397 min

Let $Y_i$ denote the total viewing time (minutes) of the target series for user $i$. To further examine engagement quality, interval-based metrics such as the Probability Treatment Effect (PTE)—which estimates the shift in the probability that $Y_i$ falls within specified time intervals (e.g., one full episode)—are used to capture more granular, distributional effects than mean shifts alone [2601.11185].

## 3. Statistical Frameworks for Impact Estimation

The evaluation of top-of-screen promotions leverages potential outcomes methodology, focusing on both marginal effect averages and full distributional changes due to the mixed discrete-continuous nature of viewing behavior.

Key statistical targets include:
- Marginal distributions under treatment/control: $F_1(y) = \Pr(Y(1) \leq y)$, $F_0(y) = \Pr(Y(0) \leq y)$
- Distributional Treatment Effect (DTE): $\Delta^{DTE}(y) = F_1(y) - F_0(y)$
- Probability Treatment Effect (PTE): $\Delta^{PTE}(y, h) = [F_1(y + h) - F_1(y)] - [F_0(y + h) - F_0(y)]$
- Optional: Quantile Treatment Effect (QTE): $QTE(\tau) = F_1^{-1}(\tau) - F_0^{-1}(\tau)$

Estimation proceeds via empirical CDFs, augmented by doubly robust regression adjustment. Gradient boosting with 3-fold cross-fitting is implemented for estimating conditional CDFs, and 500 bootstrap replications are applied to construct confidence bands. This framework uncovers heterogeneous effects and supports interval-based inference rather than relying solely on changes in sample means [2601.11185].

## 4. Empirical Findings: Heterogeneity and Engagement Mechanics

Top-of-screen promotions on ABEMA drive statistically significant but modest lifts in mean viewing time for most content types, with pronounced heterogeneity based on content structure:

| Case | Control Mean (min) | ATE (adj., min) | ATE/Control | Significance |
|------|-------------------|-----------------|-------------|--------------|
| 1: 46 min comedy    | 0.244 | 0.0140 | +5.7% | * (p<0.05) |
| 2: 5 min sports     | 0.0353 | 0.0028 | +8.0% | ** (p<0.01) |
| 3: Reality A        | 0.657 | 0.0590 | +9.0% | ** (p<0.01) |
| 4: Reality B        | 1.397 | 0.0485 | +3.5% | ns          |

Cases 1–3 demonstrate statistically significant lifts (6–9%), with the largest relative and sustained gains for short-form and serialized, strongly linked content. In contrast, longer-form narratives with weak initial hooks yield only trial viewing with no sustained engagement. Distributional and interval-based analysis confirms that top-of-screen promotion transitions users from non-viewing to trial viewing, but only strongly sequential or compact formats show increased probabilities of multi-episode or full-episode completion. Effects on long-form, self-contained content are largely restricted to initial sampling [2601.11185].

## 5. Optimization of Top Banner Allocations under Constraints

Operationally, the delivery and selection of content or advertisements in the top-of-screen slot can be formulated as a continuous or discrete allocation problem. Frameworks from online advertising optimization applicable here (e.g., Caruso & Giuffrida [1011.5364]) model the system over $K$ epochs and $N$ candidate creatives with forecasted supply $S_t$ and per-creative profit rates $\pi_i$. The static objective is

$$
\max_{\{x_{i,t}\} \ge 0} \sum_{t=1}^K \sum_{i=1}^N \hat\pi_{i,t} x_{i,t}
$$

subject to primary constraints:
- Supply cap: $\sum_i x_{i,t} \leq S_t$
- Individual campaign budgets: $\sum_{t=1}^K \sum_{i:j(i)=j} \pi_i x_{i,t} \leq D_j$
- Minimum delivery and no-overflow constraints for visibility or fairness

Rolling-horizon approaches re-optimize the allocation as returns, traffic, or campaign mix shift, converting $x_{i,t}$ (impressions) to real-time display probabilities for each candidate in the top-of-screen slot. In a dynamic environment, reward uncertainty mandates exploration: multi-armed bandit-based algorithms (UCB, Thompson sampling) guarantee sublinear regret $O(\sqrt{T\log T})$ relative to the best possible static strategy, and can be adapted to budget and visibility constraints [1011.5364].

## 6. Managerial Insights and Implications

Evaluation on the ABEMA platform yields several operationally actionable principles:
- Top-of-screen promotions reliably increase trial viewing probabilities for all content types.
- Highest business value is derived when promotions induce repeated, sustained engagement, particularly with short-form and sequentially linked content.
- For long-form narrative, positive incremental engagement occurs only when early episodes act as strong engagement hooks; otherwise, only trial viewing is observed.
- Design of promotional strategy should prioritize short/strongly sequential content for maximal incremental effect and leverage in-content follow-on mechanisms for long-form serials.
- Simple average treatment effect estimates can obscure important heterogeneity, which is critical for targeting and sequencing campaigns [2601.11185].

## 7. Implementation Guidelines and Adaptive System Design

Effective deployment of top-of-screen promotions integrates the following processes:
- Comprehensive event logging (impressions, clicks, registrations) and feature engineering for demand and reward modeling
- Short-horizon supply and engagement forecasting with periodic retraining
- Regular re-optimization of banner allocation via static or rolling-horizon linear programming, translating allocations into fractional display probabilities
- Mechanisms for learning (mandatory minimum delivery for new creatives) and adaptive exploration (multi-armed bandits for established choices)
- Continuous monitoring for model drift (e.g., abrupt changes in demand or conversion rates) and responsive re-solving of the allocation problem
- Real-time evaluation of cumulative performance and empirical regret versus clairvoyant optima [1011.5364]

Combined, these elements operationalize top-of-screen promotions as a tightly integrated system that adapts to user behavior and supply fluctuations while approaching revenue or engagement-maximizing allocations under practical constraints.

Source: https://www.emergentmind.com/topics/top-of-screen-promotions