3 Phases of Programmatic App Marketing
Wondering what programmatic app marketing is all about and what it can do for your business? Read on to discover more about the three phases of programmatic app marketing and what each one offers.
1 Exploration
During the exploration phase, you're trying to figure out what kind of ads and content will work best for your programmatic app marketing campaign. This is a time when performance tends to be less stable, as the system is discovering which variety of behavioral and contextual data points works best.
Because this is such a new advertising approach, there's always room for improvement. So, you'll likely see fluctuations in performance throughout the campaign – but ultimately, it will lead to more successful outcomes in the end.
Keep in mind that it's essential to keep track of your results throughout the exploration phase so that you can assemble adjustments as needed. This way, you can ensure that your ads are reaching precisely the people who need them most and making the most impact possible.
2 Optimization
Optimization is the procedure of discovering the best combination of variables (known as " KPIs") to drive the best performance for the advertiser's business goals. Machine learning is used to develop a model that can automatically optimize these combinations for the advertiser.
The first phase of optimization is known as "training." In this phase, the machine learning model is trained on a set of data that represents past campaigns. This data includes information about how each ad performed, as well as any history of targeting and bidding strategies used.
The second phase is "testing." During this phase, the model is used to predict how an upcoming campaign will perform given specific parameters (such as budget and target audience). If it signifies that a particular ad will be successful, then it will be run by that prediction. If not, then adaptation may need to be made before launch.
3 Scale
In the scale phase, you will be trying to achieve quality at scale by collecting as many behavioral and contextual data points as possible. You will also want to drive incremental growth for your app so that it becomes more popular among users.
The scaling phase is all about achieving quality at scale. You need to collect as many behavioral and contextual data points as possible to determine what users are doing and how they're reacting. This will allow you to create ads that are more likely to be effective, drive incremental growth for your app, and increase the number of users who use it.
How long does it take to find helpful performance and scale in programmatic?
More data analyzed means a shorter Exploration phase. Larger upfront budgets speed data collecting and model building. Smaller costs can work, but it will take longer to see favorable results.
App vertical is also significant. Programmatic campaigns require a set number of registered events, which vary for each app. It's easier to optimize for a high-frequency event than a low-frequency event.
Does the time bring to ramp up conflict between User Acquisition and App Retargeting Campaigns?
The answer could be yes or no. The campaign settings are the same, but the style of the campaign affects data collection time.
App Retargeting uses more in-app behavioral data. Lower-funnel events (like purchases) take longer to optimize than top-funnel activities (like installs). The funnel events you want to optimize for will determine how many impressions and how long it takes.
What other elements can affect performance and machine learning in programmatic mobile campaigns?
- Ad placements: the inventory in which each ad will be shown
- Bidding: the cost associated with each ad's 'bid' or chance to be shown
- Creatives: a wide range of innovative themes in several styles and modifications in the advertisements.
Conclusion
Programmatic app campaigns can take a few weeks to ramp up, but the results are worth the wait. If you're looking to increase downloads and visibility for your app, programmatic advertising is the way to go.