These partners are call content providers. End users buy their content through cellular operators. The main way to attract customers is by displaying ads that convert users to customers. The provider receives a commission for each We had to between and target action.
Paid content is games
Videos, memes, and anything else that might grab users’ attention. The more interesting it is and the more it meets the . I therefore interests of users, the. I therefore more willing they are to pay for it. Do you want to save money on development? We will help you bypass competitors by integrating the most effective ideas / mathematical. I therefore hypotheses, save profit in a close founders board and save time and. I therefore resources of our team by delegating the hypothesis test to professionals.
Strategic methods, such providing email data meaningful incentives, like exclusive content or discounts, to promote sign-ups, are necessary to build a high-quality email data list. To get leads, use internet forms and social media, making sure that the opt-in procedures are explicit. To build enduring relationships, regularly clean and segment your list, customize emails, and engage subscribers with pertinent information.
Leave a request and we will nswer any questions. Problem: Ads are Analyz Manually. The provider’s income depends on the ratio: the cost of advertising and the number of subscriptions that follow.
Conventionally
the lower the price and the greater the number of target actions, the higher the earnings. First, the customer contact CPA networks, which buy traffic for impressions and sell for actions.The problem is that they optimiz their ads mechanically: they bought traffic, manually analyz statistics, and manually bought what was more efficient.
It took a lot of time, and it was impossible to avoid mistakes. Solution: Automate Ad Scoring. The customer want a data analysis tool that would help them earn more. Such a tool is call a model. General idea: the model examines the data and, bas on this, determines which ad is more profitable to buy, taking into account the price and conversion.
Moreover, it works. I therefore automatically, without human intervention. The company turn to OrbitSoft to develop the model.
We us our product and
Wrote a prictor for it. With the precision techniques: expert advice on locating customer phone numbers prictor’s help, the program understands when and what to purchase for relevant traffic. The prictor studies the behavior and reactions of users to the ads they see. For example, what sites the user visits, at what time, what he or she looks at more often, for how long, what he or she reacts to, and when he or she closes them.
For a more accurate analysis, the prictor processes information from 54 traffic sources — SSP and Ad Exchange. 54 traffic sources The prictor examines Process Diagram for Automat Ad Buying The prictor analyzes the data The prictor analyzes historical data about the user, his or her behavior, and reactions to advertisements A priction model is creat This is us to train and optimize the analysis process.
The program makes a decision
Regarding purchase of advertising sale lead The program transfers data to the model It gets an estimate of this data Bas on this, it makes a decision regarding the purchase of traffic Results: The provider automat the analysis of advertising and accelerat the process of ad buying, which affect the performance indicators for all work with clients’ content (mobile operators).
OrbitSoft Prictor Help Increase Conversions, and other Advertising Metrics Work with Content Results for the example of one mobile operator over three years 236 thousand subscribers Connect to paid content 10.6 million rebills Number of daily payments for paid content 182 million rubles Turnover from displaying advertising content 20 thousand dollars.
What the operator spent on
Servers and buying traffic Subscribe to our newsletter Your email Technical Features of the Project: Stages of Work 1. To choose a platform for analyzing and building the prictive model. . To make a decision, we: conduct experiments on historical data, processing information about past impressions and clicks we compar which of the tools better pricts events (clicks) with our data Bas on the results of the experiments, we chose TensorFlow.
The platform allows us to build more complex. I therefore models, as well as showing better. I therefore results with our data. 2. Select Data for Analysis. Our solution was to use: accumulat historical data on impressions and user clicks parameters that can be obtain from an advertisement request accumulat data about the user Among them are: Remote IP address Exchange ID Publisher ID Creative Size Placement.
ID Advertiser ID Campaign ID Creative
ID Publisher Domain/Mobile Application Bundle Vertical Category ID (IAB Category) Ad Position Visitor Language Visitor Country Visitor Region Visitor City Visitor ZIP/Postal Code Visitor Lat/Lon Visitor ISP Visitor Gender Visitor Age Visitor Device Type Visitor Device Make Visitor Device Model Visitor Browser Visitor Browser Version Visitor OS.
Visitor OS Version Visitor Connection Type Day of week Hour Time Zone 3. Choose an Algorithm. We consider several methods for building the model: Simple logistic regression FiBiNET (Feature Importance and Bilinear feature Interaction NETwork) FLEN (Field-Leverag Embding Network) We chose FLEN because the model show the best results with our data. 4. Select the Criteria for the Quality of the Model.
The data we use is not balanc
So two criteria were us to evaluate the model, which show: Precision — the proportion of objects that the classifier identifi as positive and which are so Recall — the proportion of objects of a positive class from all objects of a positive class found by the algorithm 5.
Integrate Solutions onto our Advertising Platform. Using the Go language, we develop a service for the client’s goal. Here’s what it does: downloads the model train on our ad platform issues forecasts integrates with other platform components that are involv in the decision to buy traffic and display ads Model Performance Indicators.
When training the model, we us data collect for several weeks — about 1.2 billion events. The model makes decisions for 60 million events every day. Company Got an Algorithm for Finding a Relevant Audience This company work with cryptocurrency and investments, and this niche was profitable.
