A BI & Data Scientist on your
team is the new normal

But for most teams getting easy access to growth data and then making sense out of it has been challenging. Tenjin's DataVault gives you direct access. Now your team can play with that data, without bothering the gatekeepers. You're free to slice and dice the data in any way you like.

Data Exporter - Create your growth reports with a few clicks

Growth managers tasked to create weekly and daily reports in Excel depend on Tenjin's free Data Export tool.
Data Exporter
Free on the Starter Plan
Data Exporter

A saved report in the data exporter which fetches multiple metrics from Facebook & Applovin for the Word Search app on iOS

Previously, to get funding, all it took was to launch an app and push it into the App Store to get initial traction. This is no longer the case. Now, teams have to show weeks of improving retention cohorts until the app shows enough retention for investors to fund app teams further.

Typically, this includes teams buying thousands of installs for a couple of weeks. To compile the necessary reports, growth managers then pull campaign data from different sources and also pull attribution and analytics data. Once pulled, these data sets have to be combined and summarized in Excel. This often amounts to a couple of hours of work every week.

By using Tenjin’s free Data Exporter tool, growth managers can pull the data needed within minutes. The free Data Exporter tool is one of the most popular feature. Growth managers tasked to create weekly and daily reports in Excel heavily rely on it.

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Before I started using Tenjin for our weekly management meetings I had to pull the Campaign data from different sources, including both our Attribution provider and our Analytics provider. This meant having to manually summarize data in an Excel spreadsheet, which can be super time-consuming. I would spend half a day creating a decent dashboard with all the KPIs needed. With the management dashboard based on data from Tenjin, I can easily get the full picture I need with only a few clicks.

Marina Guz, Former Head of Growth at Familonet

Behavioral Analysis - Frequent insights to improve the economics of your apps

UA teams these days are tasked to do frequent behavioral analyses of growth data to improve the economics of apps.
Behavioral Analysis
Developer & Unlimited Plan
Data Exporter

An illustrated query result in DataVault that shows the number of sessions within 24h, split by free and paying users. A possible outcome of this would be to experiment with
* Lookalikes of new paying users with at least 10 sessions within 24h
* dedicated notifications for users with at least 10 sessions within 24h

Because of the market pressure, developers spend more time not only looking into improving the user experience of their apps, but also improving the economics.

One of the key tactics in improving the economics of an app is Behavioral Analysis of growth data. Behavioral Analysis utilizes the massive volumes of raw user event data including interactions, purchasing decisions and marketing responsiveness.
Most UA teams don’t get direct access to growth data needed to run Behavioral Analyses. Tenjin’s DataVault stores all the growth data needed to analyze the behavior of users - including their purchasing decisions and marketing responsiveness.

Advanced Management Reports - When you regularly need to dig deeper into the effectiveness of your growth efforts

Growth teams have to create weekly and monthly reports about the effectiveness of their work - they want to do it with minimum effort.
Advanced Management Reports
Developer & Unlimited Plan
Management Report

A query in DataVault and the result that shows IAP and ad revenue that comes from paid UA campaigns - split by platform and month

Suppose you need to generate a report about how the revenues from UA are developing over time. Will you cohort it and if so, by weeks or months? Will you split it by platform? Will you split it by revenue type (ad revenue vs IAP revenue)? Will you count all the (organic and paid) revenue or will you try to differentiate between organic UA and paid UA?

On Tenjin, a large portion of these reports can be done via the Data Exporter. For everything else there’s DataVault. A differentiation between organic UA and paid UA revenue, for example, can be done with a query in DataVault.

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Without Tenjin, my biggest hurdle as a data scientist is to get direct access to clean user-level data from all the mobile campaigns. For example, in a typical user segmentation project without Tenjin I spend 80% of my time trying to complete and clean data sets. On a three weeks project this means more than two weeks of data cleaning and two, three days of actual modelling. With DataVault I get immediate access to all the user-level growth data I need for my queries and models - whenever I want and without being a hassle to the engineering team.

Elisabeth Reitmayr, Co-founder and Data Analyst at Patya Analytics

LTV Models - You're trying to figure out how much you can bid in your campaigns and make a profit

Having an intelligent UA team is one of the keys to success - smart LTV models allow them to bid more aggressively with confidence.
LTV Prediction Models
Developer & Unlimited Plan
LTV model calculation

An example of a relatively simple LTV model calculation based on Tenjin - different LTVs in the various countries allow for more aggressive bidding in AU & CA in particular

Mobile marketing success can be done in various ways. Some people bid based on costs, others base success on costs and custom events, while others look at revenue or ROI.

Some even try to calculate customer lifetime value: the revenue a single user will generate from the time they download the app until they abandon the app altogether.

Calculating your future profits with high confidence happens in different degrees of sophistication too. User behavior can give you an early indication whether a campaign is likely to reach target ROI. Depending on the app, UA managers most frequently predict LTV based on 3, 7, 14, or 30 days of data.

Typically, you start with a very basic calculation using all your historical growth data. You can drill the historical calculation down to a country/campaign/platform level to monitor different segments. Then, you can break down your LTV into cohorts to understand the influence of product changes, season and competition with similar apps. The next level is to predict LTV using an advanced model (statistical and machine learning approaches).

The most painful step in modeling LTV is to get the data. Tenjin’s DataVault provides you with all the data you need to model customer lifetime value.

Segmentation & User Scoring - The basis for creating custom user journeys

Insights into your most valuable user segments and scoring of your users allow for targeted offers
Segmentation & User Scoring
Developer & Unlimited Plan
Segmentation

Tenjin gives you all the growth data needed to easily segment your user base and create different user journeys

App developers face various situations that push them to segment their user base and customize the experience within their apps.

For one, their apps have users that churn. Identifying these users allow them to target these churn candidates and improve retention. Additionally, apps have different groups of users that monetize differently. Defining these user segments well can improve the balance between monetization and user experience. Non-paying users, for example, can be shown specific types and numbers of ads.

There are different levels of sophistication with regards to how developers tackle this. Often they create a one-off segmentation project where they look into an app’s data. Sometimes they work with third party companies that offer on-going segmentation. An accomplished way to approach this is to do close-to-real-time scoring where the developer shares user-level data with their partner on an ongoing basis and gets this user level data back with scores.

Tenjin’s DataVault gives immediate access to all the user-level growth data needed to follow any of these tactics.

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Without Tenjin, my biggest hurdle as a data scientist is to get direct access to clean user-level data from all the mobile campaigns. For example, in a typical user segmentation project without Tenjin I spend 80% of my time trying to complete and clean data sets. On a three weeks project this means more than two weeks of data cleaning and two, three days of actual modelling. With DataVault I get immediate access to all the user-level growth data I need for my queries and models - whenever I want and without being a hassle to the engineering team.

Elisabeth Reitmayr, Co-founder and Data Analyst at Patya Analytics