Companies have accumulated a significant amount of their users’ data. After directing investments into advertising campaigns, they have gained a better understanding of their customers and their needs. However, many of them have not yet learned how to effectively monetize this data.

Traditional media advertising has become less effective due to information saturation and banner blindness. Therefore, companies are actively working on personalization and targeted advertising to increase conversion rates and campaign effectiveness. As a result, businesses are investing more in user acquisition.

Numerous companies are emerging with the goal of increasing the efficiency of their investments in advertising and user acquisition:

• Advertising agencies with expertise in configuring advertising campaigns on platforms like Google, TikTok, and Snapchat or
• Companies offer predictive analytics to determine the most effective creatives (e.g., INCYMO.AI and smartUA) and reduce production costs, ultimately improving efficiency and saving resources.

For companies with high stakes and narrow user segments, analytics and historical data can help identify which users bring in more profits and how to acquire them more effectively. Thus, they can fine-tune their advertising campaigns and improve user acquisition metrics.

In the context of rising costs on auction platforms like Google and Facebook, companies are facing increased click costs and competition. Therefore, it’s important to understand how quickly user acquisition investments can be recouped. Analytical solutions like Lemon AI can help companies determine the payback period and make real-time decisions about scaling or adjusting advertising budgets.

Let’s take a look at how this works in practice: there are two main scenarios in the market.

In the first scenario, you have a large number of purchases, and most of your users are high-value or make frequent purchases. 

In this case, you don’t want to pay the same rate for every user because some of them may bring in additional purchases in the future. Instead, you want to optimize your spending considering the potential profitability of each user. 

That’s why it’s crucial to:

1. Segmenting users and 
2. Predicting how much each user will bring you in the future. 
3. Based on this information, you are willing to pay different amounts to acquire users depending on their potential value. 

For example, you’re ready to pay $10 to acquire a user from segment A, which, according to your calculations, will bring you profits ranging from $10 to $50. This solves the first problem.

In the second scenario, you have very few target users, and you need to find users who are similar to your target audience but are not making purchases yet. 

In this case, you don’t narrow down your audience, as in the first example; you expand it. 

You’re looking for users who are very similar to the 1% of users who are currently making purchases and working with them. Even if you increase the conversion rate to just 10%, it’s already a significant improvement compared to 1%. This way, you optimize your user acquisition source and increase their quantity.

It’s important to note that the effectiveness of solving these problems always depends on mathematics and data processing methods. There are numerous data collection methods, but not all companies have learned how to analyze and monetize them correctly. 

Understanding which methods and approaches work best for a specific industry can give companies an advantage and help them achieve better results.

Performance marketing & media

The first step is to define the goal of your advertising campaign. 

For example, if you want to introduce a new product, whether it’s a new game or something else, your initial objective would be to create brand awareness so that people become aware of your product. 

To achieve this, you can utilize various media channels like DV360, YouTube, GDN, where you optimize expenses for the most efficient audience acquisition.

The second step involves User Acquisition and Performance. Here, two essential questions come into play.

The first question is how to find the optimal marketing mix using various channels? 

For instance, how to allocate your advertising budget across different channels such as Google, TikTok, and others. You need to understand how to create the best combination of these channels to achieve your goals. Your marketing mix (the percentage of advertising budgets invested in different channels) might include 50% on Google, 30% on Meta, 20% on TikTok, and so on. It’s crucial to determine how to effectively use each channel to meet specific objectives.

Each channel has its own optimization mechanisms, and it’s important to identify which of them are best suited for your company. Some optimization engines work better in specific channels based on their audience and unique integrations. For example, gaming companies value integrations with games and formats not available in standard advertising networks. Therefore, choosing the optimal marketing mix is crucial for an effective campaign.

Within each channel, you conduct A/B tests to find the most effective creative solutions – banners, videos, and targeting settings. Suitable assets will help you address your objectives most efficiently.

The second question pertains to cross-channel strategies. This involves determining where to direct your audience based on their behavior. For example, if you understand that some users start the checkout process in a mobile app while commuting to work and then complete it on the website, you can adapt your advertising to optimize the process for such users.

This also involves personalized advertising at different times of the day and utilizing technologies like INCYMO.AI to predict the effectiveness of different banners and ad settings.

In the end, your task is to find the optimal combination of channels, optimize each channel, and create a cross-channel strategy based on an understanding of your audience’s behavior.

The correlation between predictive user acquisition and the conventional bidding practices in Google and Meta

Typically, you gather a sufficient amount of historical data, usually not less than 5000 unique users. Then, you convert this data into a numerical format because predictive models work with numbers, not text. The process looks like this:

1. Data Preparation: Initially, the data you plan to use for model training must be transformed into a numerical format.

2. Model Training: Historical data is employed to train the model, describing how much money users brought in the past. The model is trained to predict how much money new users can bring based on their characteristics.

3. Model Evaluation: The model is evaluated based on its ability to predict revenue, taking into account known user and expense data. The model learns from this data.

4. Model Deployment: After training, the model can be deployed on real-time servers. This allows you to use the model to predict user revenue for those currently interacting with your app.

5. Real Data Collection: In the process of the model’s operation, real-time data about users is collected. This data includes information about what actions users take in the app.

Using a service like Lemon AI automates these processes. In other words, the user only needs to select what they want to predict. The rest – model training, feature engineering, data parsing, and conversion into numbers – happens automatically.

Next, you can optimize advertising campaigns on various platforms. For example, you can target ads to users who, according to the model’s predictions, are more likely to perform specific actions, such as installing your app or making purchases.

At this stage, it’s essential to optimize and automate the campaign. You can monitor how advertising campaigns perform and adjust them based on real results and model predictions.

Automating this process makes ad buying more efficient. By directing your advertising efforts based on automated campaigns and detailed analytics, you can improve KPIs by 30-40% compared to traditional advertising methods.