In the stock era, with an increasingly competitive market environment, merchants aiming for growth must overcome two challenges: first, enhancing user engagement amidst heightened ad immunity; second, minimizing intermediate costs and improving efficiency as consumers tighten their purse strings.

The former pertains to stimulating users' interests in purchasing, downloading, and inquiring, while the latter concerns the cost-effectiveness of customer acquisition efforts.

These challenges intersect at advertising campaigns.

Especially for consumer brands focused on purchases, compared to products like home furnishings or finance, which involve high decision costs and longer purchase cycles, most consumer goods have shorter user decision paths. The quality of advertising campaigns significantly impacts merchants' growth.

Two variables determine the success of advertising campaigns: people (optimizers) and tools.

Since the advent of large models, the technological foundation of advertising campaigns has continuously evolved, and many platforms have upgraded their advertising systems. For instance, Tencent Advertising upgraded its system to version 3.0 this year, hereafter referred to as the new advertising system (3.0).

As tools evolve, optimizers must also upgrade their advertising methodologies, crafting innovative and targeted strategies to reach the right audience at the right time and through the right channels, thereby achieving marketing objectives.

How to use the new tools? How to upgrade advertising methodologies? We invited Chen Chen, the Operation Director of Shenzhen Qianxi Big Data Co., Ltd., and a seasoned optimizer in the e-commerce consumer goods industry, to share his experiences with the new advertising system (3.0).

1. AI handles AI tasks, optimizers handle optimization tasks; creativity matters more than planning

Traditionally, advertising campaigns were considered mysterious, prompting optimizers to rely on volume to gamble on probabilities. This led to vast amounts of redundant and ineffective data, burdening platforms and reducing ad targeting accuracy and efficiency.

The new advertising system (3.0) aims to enhance stability and effectiveness through technological upgrades and logical optimizations.

Chen Chen, known as the 'Number One Cao Cao of Chegongmiao,' explains that the upgrade has significantly transformed advertising strategies and workflows.

The new system streamlines hierarchy, automates processes, and frees up optimizers' time. It separates AI tasks from optimization tasks.

Chen Chen notes that optimizers previously spent much time on manual tasks like entering target audience attributes (location, age, etc.) and seed audience packs for system optimization. This process was tedious and irreplaceable.

Advertising logic has also evolved.

Chen Chen explains that advertising used to involve people finding people, but now it's about products finding people. Instead of optimizers labeling audience segments based on experience, the system now learns product attributes to target potential customers.

Chen Chen cites an example with a toothpaste ad. After entering product attributes, the system learns about the toothpaste's sensitivity-reducing feature and targets users interested in this benefit, enhancing ad targeting accuracy.

The key to a smart and efficient large model lies in abundant, precise, and targeted data. To better understand products and businesses, the new advertising system (3.0) encourages optimizers to articulate their marketing goals.

(Illustration of the new advertising system)

Chen Chen emphasizes that, besides detailed product information like toothpaste, enriching store or account details can also enhance the system's human-product matching capabilities.

Changes in advertising processes and logic have shifted optimizers' focus.

Chen Chen believes that automation reduces the entry barrier for optimizers. However, outstanding ads rely on creativity. Optimizers' core competency lies in mastering platform mechanisms and being highly sensitive to innovative ideas.

Under Tencent Advertising's ad limit policy, optimizers must be more cautious in ad setup, focusing more on creativity. A great creative idea is more valuable than numerous homogeneous plans.

AIGC enhances creative content production. Chen Chen mentions that Tencent Advertising's Miaosi platform facilitates mass production of high-quality materials.

Han Wenjing, a seasoned feed optimizer with a background in comic companies, appreciates Miaosi's accurate text understanding, attributed to its large model and advertiser/industry performance data, resulting in higher click-through rates for generated images.

Han Wenjing notes that traditional ad formats, such as text scrolls or mixed clips showcasing exciting chapters, may have reached their limits, with low click-through rates of 1% to 3%.

Miaosi can generate scene-based images related to novel stories based on given keywords. These vivid, immersive images effectively capture users' attention, improving user engagement and ad performance, with click-through rates exceeding 5%.

Cui Shijie, the AIGC commercialization leader of an advertising service provider, emphasizes that AIGC must address material production efficiency, copyright, and review issues.

Miaosi addresses these concerns by pre-reviewing materials to avoid copyright violations and rejected elements, shortening review times with higher approval rates.

Jian Feng, CEO of LinkOne Digital, a full-link brand ecosystem service provider on WeChat, emphasizes the significance of this for time-sensitive advertisers.

2. Component-based creativity enhances efficiency; optimization actions must align with marketing goals

Besides conceptual changes, Chen Chen shares practical experiences and pitfalls to avoid.

First, compared to custom creativity, component-based creativity enhances ad testing efficiency, helping optimizers quickly identify optimal combinations.

For example, with 4 videos, 3 captions, and 2 links, there are 24 custom combinations. Component-based creativity instantly identifies the best mix, enabling optimizers to identify the most effective video and caption through data analysis.

In an advertising campaign for a blind box brand, Chen Chen used a 1:1:3 ratio (one plan, one ad, and three fixed creative combinations) under the old system for A/B/C testing.

After migrating to the new advertising system (3.0), Chen Chen adjusted the video-caption combinations based on extensive testing, enhancing efficiency nearly twofold compared to custom creativity.

Second, successful component-based creatives can be reused. Creatives are consumable and require continuous production. Optimizers can analyze and dissect high-performing materials to identify strengths (e.g., effective voiceovers or captivating opening seconds). Applying these strengths to new creatives accelerates output quality and effectiveness.

Third, optimization actions must align with marketing goals, which typically include user growth, brand promotion, lead generation, and product sales.

Chen Chen notes that most consumer goods clients prioritize product sales (conversion rate over click-through rate). For these brands, deep data is more crucial than superficial metrics.

In one case, a blind box brand performed well in click-through rates under the old system, indicating sound content production logic and quality. However, after migrating to the new system (3.0), performance fluctuated, with a noticeable decline in click-through rates after a few days.

Given the challenges in optimizing click-through rates for live ads, Chen Chen's team focused on conversion rates, considering factors like landing pages, discounts, and bid prices.

(Illustration of the new advertising system)

Chen Chen shares tips for enhancing ad competitiveness:

Ensure consistency between outer ads and inner product pages for a seamless user experience.

Boldly test bids early while controlling ad tests, adjusting after securing wins.

Utilize the three-day cost guarantee period to learn and accumulate samples, monitoring various data dimensions for subsequent optimization analysis.

After adjustments, the blind box brand's conversion rate remained stable over a month, with an overall ROI increase of around 5%. Chen Chen concluded that the new system's different optimization logic caused fluctuations in metrics like click-through rates.

While product sales remain the primary marketing goal, white-label blind boxes pose unique challenges due to limited brand recognition. Enhancing superficial metrics like click-through rates and 3-second completion rates is crucial for gaining exposure and volume.

In the new advertising system (3.0), advertisers must thoroughly articulate their marketing goals, carriers, and content to enable the system to offer precise strategies and improve campaign effectiveness.

Combining effective tools with creative ideas significantly enhances consumer goods brands' growth potential.