Your Playbook to Short Drama & Novel UA: Master the Two Test Goals

You launch a short drama & novel app globally. Ads are running everywhere—Facebook, TikTok, Google, yet the dashboard only shows a blur of clicks and downloads. Budgets drain fast, yet real readers don’t stay. Sounds familiar? This is a common pitfall of user acquisition (UA) testing.

The good news? After working with numerous short drama and novel clients, SolarEngine team has summarized a set of solutions for acquiring users and driving growth, which will be broken down in two parts: The first part focuses on the testing phase, teaching you how to use pre-heating to lock in effective signals and lay a solid foundation for subsequent promotion; The next part delves into the major promotion phase and the sustaining phase, analyzing the core strategies.

Today, let's start with the testing phase that determines whether the volume can be generated, and break down the key actions behind that data.

The Role Of The Testing Phase

So, how do you move from guesswork to a predictable, data-driven approach? It all begins with a well-structured testing phase.

Many developers rush straight into scaling campaigns, only to watch budgets evaporate. Yet, for short drama and novel products, the period of UA testing is not a simple process of placing advertisements and looking at data, but a meticulous layout that requires pre-heating and precise judgment. The testing phase acts as a small-scale pre-launch lab. Instead of chasing volume, it's where you validate your channels, creatives, and user quality. Think of it as building a reliable compass before sailing into the high seas of paid acquisition.

Two Core Testing Goals

Goal 1: Acquisition Efficiency

The first task is to verify whether the content can support large-scale promotion at a reasonable cost. Through paid advertising, you need to determine whether the plot setting of the novel and the theme style of the short drama can truly attract users to click and download.

Goal 2: Retention & Engagement Quality

The second goal is to test retention and engagement, which is the key to evaluating whether an app can retain users. Therefore, after users download, the second-day retention and 7-day retention, session length, and early in-app actions (like unlocking paid chapters or tipping authors) are the core indicators for measuring product stickiness. If the situation of "high download volume but second-day retention rate less than 30%" (i.e., "attracting traffic but poor retention rate") occurs, optimization should start from user relevance.

Tactics For The Two Testing Goals

Don't Rely On A Single Channel

In the actual UA testing, we found that most clients would use major media such as Facebook and TikTok, yet they failed to attract traffic due to fierce competition or insufficient user matching. Therefore, a single channel or algorithmic limitations can lead to "misjudgment".

In addition to major media platforms, you can try platforms like Mintegral and Applovin. These channels have more segmented user profiles and relatively lower UA costs, which can precisely reach vertical enthusiasts of short dramas and novels. Multiple types of placement methods can also be adopted. Through forms such as Web to App (for instance, embedding a "free trial reading" entry on the novel information webpage to directly guide downloads) and deeplink (for example, clicking on short drama ads directly leads to the corresponding episodes within the APP), the limitations of traditional advertising links can be bypassed, thereby enhancing the efficiency of download conversion.

Identify High-retention Users And Expand The Audience Base

As mentioned above, when retention is poor, optimization should start with user relevance. SolarEngine's User Analysis can identify users who both unlock paid chapters consecutively and tip authors, allowing you to target them with limited-time bundles, boosting retention by 20–30%. Meanwhile, with its Lookalike targeting feature, you can acquire high-retention users from the start, improving overall retention quality at the source.

SolarEngine’s Event Analytics feature can help optimize media models to acquire high-retention users. In the early stage, you can detect high-engagement user behaviors (such as registration, completing free chapter reading, or click-to-top-up events) to leverage the media's oCPE model for expanding high-retention users. You can also use filter conditions and rule-based event callbacks to further refine the model and reduce interference from non-core events.

By setting the rule of "only passing back payment events above ＄1.4" to the media, one novel app avoided data redundancy and helped the media focus on core data for more effective model optimization.

Among these operations, if your campaign struggles to scale due to the oCPX algorithm suppression, you can break through by using a rapid creative rotation strategy. For short drama products, try launching three types of clips simultaneously — "hook openings", "mid-story conflicts", and "twist endings". This approach helps the algorithm quickly identify high-CTR creatives and break down traffic barriers.

Practical Guidelines For Effective Tests

To make the testing strategy more actionable, it's essential to define execution details across core dimensions such as timeline, funnel, and budget, ensuring that every step contributes reliable data for the upcoming user acquisition cycle.

Timeframe: Scientifically Plan The Cycle

The typical testing period lasts 2–3 days. Once the data meets the set benchmarks, you can move into the peak launch phase. This duration allows the algorithm to fully learn from the data and optimize strategies, while also ensuring you don't miss the golden window of opportunity, keeping your UA pace both efficient and effective.

Channel Selection: Optimizing Primary And Secondary Paths

Use Web-to-App (w2a) as the primary channel and direct app install campaigns as the secondary. The w2a path seeds interest in advance through web content, using free trial chapters or highlight clips to lower the download barrier. Direct app campaigns reach users who already have app usage habits. Together, this combination covers multiple kinds of audiences and boosts downloads.

Budget Allocation: Separate New Products From Existing Products

Focus primarily on existing products, which can leverage historical data to optimize creatives, targeting, and other details. For new products, allocate at least ＄420 per campaign to cover a sufficient sample size, avoiding misleading results due to insufficient budget that could impact decision-making.

Optimize The Placement Model Regarding Core Actions

Focus on core user actions to refine the placement model. SolarEngine's Event Analytics can track events including registration, completion of free chapter viewing, click to top-up, payment completion, and subscription. By sending back these events, the algorithm can better identify high-quality user characteristics, optimize placement strategies, and improve user acquisition efficiency.

Smart Bidding: Balance Cost And Scale

Use maximum_delivery (automatic bidding) as the main strategy, and for VBO (Value-Based Optimization), also choose the highest value option. This allows the system to dynamically adjust bids, securing more traffic while maintaining conversion quality.

For ad sets that perform well but show rising costs while maintaining acceptable ROAS, you can duplicate them and switch to cost_cap (manual bidding). This approach sets a fixed cost ceiling, allowing you to scale within the profit margin, while extending the creative's lifecycle.

Final Thought

The pre-launch testing phase isn't just a box-ticking exercise; it's about focusing on two key goals: user acquisition and retention, in order to eliminate uncertainty. Scaling decisions shouldn't rely on "gut feeling" either, but on data that identifies repeatable success. With SolarEngine, short drama and novel apps can precisely discover the right mix of high-potential channels, tailored strategies, and quality users during the testing stage, turning ad campaigns from blind trial-and-error into targeted execution.

Next up, we'll dive into how to maximize scale during the peak launch phase and how to extend the lifecycle during the sustaining phase. We'll break down the strategic playbook for these two critical stages. Stay tuned to hit every growth inflection point throughout your entire UA cycle!

Ready to transform your UA testing from guesswork to a data-driven science? Contact us today to learn how SolarEngine can help you hit every growth inflection point.