When launching an app, teams usually integrate a similar stack of mobile analytics tools: Amplitude, Firebase, MixPanel, AppsFlyer, Adjust, among others. These tools are used to track product and business metrics such as: the number of installations, churn, engagement, and conversions.
The question that emerges from this data is: how can an app influence these metrics?
One of the most popular and effective ways is to use personalized, user-relevant push notifications. Most app teams realize this fact, however, in reality, it’s difficult to implement.
In order to send relevant push notifications at the right time, you need:
A major challenge for the market is that mobile analytics products can collect detailed, granular analytics, and offer advanced segmentation to send the most relevant, targeted push notifications, yet push notifications platforms can't use most of this information.
And because of this, push platforms are unable to transmit the same number of events as what mobile analytics systems can process. This means that a push tool needs to pass only the most relevant information about user behavior, otherwise it will no longer be economically viable due to high costs.
The system of push platforms is not designed for analytics, because its core goal is sending notifications. As a result, clients have no capability to look at data analysis from different angles. This requires exporting information to an analytics system, which is a lengthy, expensive, and sometimes impossible process.
For example: even the lowest tier Amplitude plan includes a desired and popular functionality - funnel visualization. This allows you to track how unique users navigate through different screens of the app after they clicked on push notifications, and what conversions it had at each step. Push platforms can provide information on segments and metrics, but they don't allow you to track how sending a notification affected a user's journey through the funnel.
In essence, the most basic plan of a mobile analytics system offers more analytical capabilities than a whole module in a push platform. At the same time, industry users have become used to the core capabilities of analytical tools, and expect similar functionality from push notifications platforms. The lack of familiar visualization, dashboards, and metrics presents new challenges for the decision-making manager: he needs to export data and transfer it to the analytics system, hire an analyst, or look for other alternatives. The specialist has to learn how to work with two systems at once to understand both of their functionalities and limitations. This increases the workload on the team and seriously delays the decision-making process.
An SDK always adds weight to an application and affects its speed. Because of this, when marketers send a request to the development team to integrate additional SDKs, the IT specialists must first research the product. Even if marketers are familiar with the tool, analyzing it in the context of a specific project takes time, which development teams usually don't have. This generally results in the implementation of the new SDK-based solution being placed into the queue of prioritization of the product team, which creates a delay in the process.
Additionally, the SDK also affects the crash rate and non-response rate (ANR) of the app, which in turn impacts the position of the app in the Google Store. The app's ranking in the Store can drop by several dozen points just because a new SDK has been installed. This is why many gaming apps try to avoid implementing push platforms.
Finally, apps usually start developing a push marketing strategy at some point after the initial app's release. The SDK is implemented in a new app version, and the push module starts working only for the users who have installed the latest update. As a result, the part of the audience that needs activation the most is left out simply because they are less likely to download the update.
When applications "outgrow" industry standard analytics systems, they generally want to have a more customized solution for their needs, get away from the extra costs that are characteristic of popular products on the market, and pay more attention to their data security. In pursuing this, they build their own data warehouses, event collectors and other tools, i.e. they create their own data management system, including the management of push campaigns.
To do this, the company involves a separate team of data engineers and other specialists. In parallel, the team develops a system for sending pushes that can integrate with the analytics system and retrieve all the necessary data and segments from it.
It is important to add and understand that in recent years it has become easier to develop in-house analytical systems. There are more and more services like Snowball on the market, that provide development components, and allow you to build a fully functional solution on Amazon infrastructure within six months, using a team of 5-6 people. So, creating your own products are gradually becoming more accessible, even to small and medium enterprise companies.
At nGrow.ai we use a different method - suitable for companies with any analytics system and push notifications technology stack. We integrate with all popular analytics platforms and build our own push notifications management system, incorporating machine learning on top. Over the past year and a half, we have learned how to connect to the most popular market tools, and have closed the gap between analytics, push notifications and reporting.
Our solution does not involve custom development, and can be integrated within just one week. Our approach allows you to take advantage of advanced mobile analytics systems and ensures connectivity between all components.
Google and Apple’s operating systems have dictated that mobile applications have to be as contextual as possible with respect to user behavior, and send only relevant messages at the appropriate time. The old approach to user engagement with its lack of data and "clumsy" rules no longer works, and doesn't guarantee good ROI because development and support are expensive.
nGrow provides no-code functionality for push notifications to work on top of data from mobile analytics systems, meaning you can easily achieve an ROI of thousands of percent, in the shortest time possible.