Owing to a large number of devices, number of apps on each smartphone, and high popularity of mobile devices, a lot of change is happening in the mobile world. Most businesses are now moving towards an all-around mobile advertising strategy to attract more and more mobile users. Before visiting a website on the laptop, everyone would check it from their phone. It is easier and simply convenient. Lacking behind in mobile strategy only means losing a lot of customers.
However, the huge amount of information collected every day through mobile devices is impenetrable without the use of advanced technologies such as machine learning. Without analyzing and evaluating this data, it is not possible to come up with great mobile advertising strategies.
In this article, we will evaluate how machine learning can enhance your mobile advertising efforts.
1. Real-Time Bidding
In a real-time bidding environment, the DSPs or the demand side platforms are required to figure out what is going to be the optimal bid amount relevant to every impression. Most of these exchanges only have 100 milliseconds response latency system – maximum. This means that the impression should be generated with data in a very short time.
Further to know the bid amount, the algorithm being used should be able to assess the resulting probability of an impression turning out to be a good performance. This is evaluated through performance metrics such as install rate, conversion rate, click-through rate, and lifetime value. Overall, this assessment is accomplished with the data from the data management platform (DMP) acquired from the impression or the first party data achieved from the advertiser.
When ML algorithms use historical data with this, future performance can be predicted. For example, it is possible to analyze whether a banner from a specific website has what chances of conversion. However, it is difficult to know what historical data to use exactly. But, algorithms are better at evaluating which impression attribute can lead to enhanced performance.
2. Lookalike Targeting
Understand this with an example:
Today, we have the power to show an ad relevant to iOS mobile users living in Delhi NCR. However, here, how would you decide which type of people are your target? How would you figure out the target cluster to a specific campaign?
This can be achieved with machine learning algorithms. Machine learning can help you understand the best cluster for a specific ad.
For instance, ML can help you figure out which males of the age 40 or more are more likely to finish an ad related to therapeutic insoles. Going forward, the algorithm will automatically start putting individuals in the group along with analyzing their specific response type. This will assist you in lookalike targeting.
3. Use Data Management
Talking about real-time bidding environment, we receive ample device and user data. The device data collected by the publisher can be less or more extensive. But, this data is enough for a buyer to come to an informed decision. For example, not every publisher can give you data related to demographics but this doesn’t change the fact that demographics are necessary for buyers. This is where DMPs helps in connecting the supply-side to the demand-side.
The ML algorithms extract user information from third-party sets of data to enhance buying decisions.
Machine learning plays an important role in advertising. This technology can be utilized to overcome data analysis, decision making, and targeting issues. Once all that is streamlined, it becomes possible for businesses to optimize mobile advertisements.