The Good Tech Companies - How to Power up your Digital Marketing with Deep Learning Predictions

Episode Date: May 1, 2024

This story was originally published on HackerNoon at: https://hackernoon.com/how-to-power-up-your-digital-marketing-with-deep-learning-predictions. In this article, we explore the impact of AI and deep learning predictions on digital marketing, providing some specific hints on how to make campaigns shine. Check more stories related to media at: https://hackernoon.com/c/media. You can also check exclusive content about #digital-marketing, #deep-learning, #machine-learning, #predictions, #predictive-modeling, #advertising, #ai, #good-company, and more. This story was written by: @lemonai. Learn more about this writer by checking @lemonai's about page, and for more stories, please visit hackernoon.com.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. How to power up your digital marketing with deep learning predictions, by Lemon AI. Greater than today, 83% of organizations worldwide cite AI as a top priority, with the AI market projected to surge 20-fold by 2030. Amidst intensifying competition, it makes sense that businesses do not dare disregard AI technologies anymore. So it's quite disappointing to many that, despite certain advancements in digital marketing, ad campaigns continue to fall short of optimal efficiency, with ad investments yielding subpar returns, campaigns failing to meet benchmarks and KPIs, and ROIs proving challenging to gauge.

Disrupting traditional advertising with the power of AI. Over the years, companies have accumulated a significant amount of raw data, a true goldmine of marketing insights that is often underutilized and undervalued. After investing in ad campaigns, businesses gained a better understanding of their customers and their needs. However, many of them have not yet learned how to effectively monetize that data. To increase profits, companies started paying more attention to higher margin indicators. This has led to the layoff of redundant employees and the automation of work processes. Multinationals and tycoons like Tesla are investing significant resources in robotics and automation to minimize errors in production and reduce labor costs, which are rising due to inflation. 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 efficiency. As a result, businesses are investing more in user acquisition, but their returns need to be secured. For companies with high stakes and narrow user segments, analytics and historical user activity data can help identify which users bring in more profits and how to acquire them more efficiently. This way, they can fine-tune their advertising campaigns and improve performance marketing metrics. In the context of rising costs on auction platforms like Google and Meta, companies are facing increased click costs and competition. Therefore, it is important to understand how quickly user acquisition investments can bear a coup.

Analytic solutions like Lemon AI can help companies determine the payback period and make informed decisions on scaling or adjusting their ad budgets. How this actually works? Let's take a look at two scenarios that are taking place in the market. 1. You have a large number of purchases from a very wide target audience, with some users bringing you more revenue and staying with you longer than others. Backslash dot. Still, you pay more or less the same average price for all users from your wide audience, even though their lifetime value and retention rate can vary significantly. This, of course, makes your campaigns less efficient than they could be. So, it's reasonable to want to optimize your spending, considering the

potential profitability of each user. That's why it's crucial to segment your audience based on how much each segment will bring you in the future. Based on this information, you can pay different amounts for different segment depending on their predictive value. For example, it could be $5 for users from segment A that will bring you $15 to $20, $7. $5 for users from segment B that will bring you $25 to $30, and $10 for users from segment C with over $30 of potential lifetime value. Backslash dot. 2. Imagine you have very few target users, and you need to find users who are similar to your current paying audience but have not made a purchase yet. Backslash dot. In this case, you would want to expand your audience. The challenge here is that with very

few events happening, it's difficult to promptly identify the users you need. What we can do here is leverage our predictions built for an audience as similar as possible. As a result, your user acquisition source gets much more knowledge about target users to be attracted and can be easily optimized based in this knowledge. If historically you've had, let's say, only 1% of users who make purchases, increasing your conversion rate to just 5% is already a significant improvement that has a great impact on your revenues. 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. Making your ad campaigns shine. The first step here is defining the goal of your campaign. For example, if you want to introduce a new product, whether it's a new game or a fitness app, your initial objective would be to create brand awareness so that people start spreading the word. To get there, you can use various media channels like Display & Video 360, DV360, or Display Network, GDN, by Google, where you search to optimize expenses for the most efficient audience acquisition. Next, it comes down to user acquisition, UA, and performance, and here we have two essential questions. First, how do we find the optimal marketing mix using various channels? For

instance, efficiently allocating your ad budget across different channels, such as Google, TikTok, and others, might be a serious challenge. It's crucial to determine 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, 10% on TikTok, and so on. Each channel has its own optimization mechanisms, and it's important to identify which of them is best suited for your company. Some optimization engines work better on 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. 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 AI powered tools 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. Predictive OOA and conventional bidding practices. Typically, you gather a sufficient amount of historical data, usually over 5,000 unique users. Then, your raw data is converted into a numerical format, as predictive models work with numbers instead of text. The process looks like this one. Data preparation. The data you plan to use for model training must be transformed into a numerical format. 2. Model training. Historical user activity data is used to train the model. The model is trained to predict how much money new users can bring based on patterns in their activity. 3. Model evaluation. The model is evaluated based on its ability to make predictions. Model Deployment. After training, the model can be deployed in real-time,

so you can predict the values of users currently interacting with your app. Realtime Data Collection. New user activity data is collected in real-time. Lemon AI fully automates these steps for you with its patented deep learning technology that boasts over 90% prediction accuracy. You only need to choose what you want to predict. This can either be a conventional performance marketing KPI, E, G, ROAS, LTV, retention, ARPU, and CAC, or any custom metric crucial to your business. Whether it's identifying users who spend 100 gems after completing 20 levels in your game or those who place a minimum of three orders worth $500 within the last 30 days on your e-commerce platform, our solution will help you identify the

most important metrics based on raw data analysis and create a custom event to boost your app or website performance. All the rest, model training, feature engineering, data parsing, and conversion into actionable insights happen automatically and do not require you to get deep into the tech. No-code data transfer via pull and push API takes only 30 minutes, with deep learning models trained within 48 hours. The fast track feature allows you to start generating first predictions within 15 seconds of a new user's app launch, even with scan limitations. Seamless integration with leading mobile management partners and analytics services further streamlines the process. In your ad manager, you can monitor in real-time how your optimized campaigns perform and adjust

them based on actual results and model predictions. LemonEye's intuitive interface eliminates the need for dedicated managers or coding skills, so campaign optimization becomes as simple as pressing a few buttons, sparing you the tech intricacies. Our end-to-end analytic solution helps automate matching data across different data storages, whether it be mobile measurement platforms, MMPs, CRMs, back-end storages, etc. This allows businesses to seamlessly get actionable insights from the whole range of raw data they possess. Automating all the above-mentioned steps makes ad buying way 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. It actually works. Lemon AI enables companies to harness advanced deep learning technology in alignment with their goals, whether that means enhancing KPIs while maintaining costs or vice versa, reducing costs without compromising KPIs. In just six months, we've optimized a total ad spend of $8.2 million for more than 60 clients from industries such as e-commerce, banking, gaming, delivery, hospitality, and travel. Here are just two brief examples. Case 1. LTV growth by 49% in e-commerce challenge. A top e-commerce platform in the MENA region, with 25 million installs and 650k plus monthly average users,

struggled with low LTV, AOV, and retention rates despite a wide product range. The mobile app was leveraging predictive user acquisition and analytics tools to little effect. The aim was to drive sustainable growth in business metrics by implementing a comprehensive digital marketing strategy and optimizing Google Ads and meta ads channels to attract high-value users, encourage repeat purchases, and develop predictive personal dynamic offers. How we got there in three steps. One, we analyzed data to forecast buying habits and churn likelihood, as well as optimize user acquisition and retention strategies. Two, we targeted users with the top 35% LTV

within 60 days and those making 3 plus purchases within 30 days post-installation. After 3 months, we reduced CAC by 17.9%, optimized banners, texts, and USPs. 3. We implemented personalized product recommendations based on purchase history to enhance the shopping experience, boosting AOV by 59% over 5 months. Backslash dot results for Android plus 35% retention plus 42%V, plus 32% LTV on day 60 CASE2. ROAS surged by 42% for a casual game challenge. The client, a casual game with over 5M installs and 700K monthly average users, sought to optimize their advertising strategy in order to maximize revenue across MENA, Europe, and APAC regions

while balancing user experience and engagement. The goal was to increase ROAS and retention within app purchases using data from AppsFlyer. How we got there? 1. In just 8 days, the Lemon AI model was fully trained and integrated, with no code required. 2. We made ML-based predictions for the top 10%, 20%, and 30% of players by revenue. 3. For players who reached level 10 feet and spent a total of 200 diamonds, we created a tailored event that served as a proxy metric and enhanced efficiency. Backslash dot results. Plus 17% overall efficiency CF. Client's internal benchmark for Android. Plus 42% ROAS. Plus 28% ad revenue for iOS. Plus 27% ROAS. Plus 16% ad revenue. Thank you for listening to this Hackernoon story, read by Artificial Intelligence.

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