Featuring

Android SDK

With the capacity to process millions of data records in seconds, Datagran x Android SDK integration is the fastest and most effective way to track your APPs data.

5 World Class Machine Learning Features
Built Thinking About You

Churn Reduction

Don't let Cart abandonment reduce your revenue. With our proprietary algorithms, process behavioral and transactional data, clean and dedup it to  identify the events that lead to churn and use our Predictions to push the output via bot, email, Facebook or Google campaigns.

RFM Analysis

RFM (recency, frequency, monetary) analysis quantitatively measures customers by examining how recently a customer has purchased (recency), how often they purchase (frequency), and how much the customer spends (monetary) enabling you to do target them accurately.

Similar Taste

Groups customers who have similar taste. For example, if they bought several products in common. Increase profitability by targeting them with a tailor-made list of products they are most likely to buy.

Recommended Product

Recommend a specific product to a user group that has not yet purchased it based on precise algorithm prediction.

Budget Allocation

Don't struggle with trying to find the optimal balance allocating your Facebook & Google campaign budget. Analyze your objective, budget and costs and our algorithm will recommend the exact budget to allocate for each campaign. This is specially useful when testing multiple campaigns with many type of events.

For example: a campaign with a Purchase event and another campaign with a First Purchase back event.

RFM (recency, frequency, monetary) analysis quantitatively measures customers by examining how recently a customer has purchased (recency), how often they purchase (frequency), and how much the customer spends (monetary) enabling you to target them accurately.

The Similar Taste feature allows you to receive reports of user clusters who have similar taste. For example: a cluster of people bought a group of products in common.

Increase profitability by targeting them with a tailor-made list of products they are most likely to buy.

Our powerful Recommendations engine enhances your intuitive capabilities by recommending specific products to a user group that has not yet purchased them. This process is based on precise algorithm prediction.

Don't let Cart abandonment reduce your revenue. With our proprietary algorithms, we process behavioral and transactional data, clean and dedup it, to identify the events that lead to churn. Take advantage of our Predictions tool to push the output via bot, email and even Facebook or Google ads.

Don't struggle trying to find optimal balance allocating Facebook & Google ad budgets. Our engine recommends the exact budget to allocate, by analyzing its objective, historical budget and costs. This is specially useful when testing multiple campaigns with more than one type of event.
For example: a Purchase event and a First Purchase event campaign.

How to use Tracker Library for Android

1. Add the token to $HOME/.gradle/gradle.propertiesauthToken=
jp_gb5h29iiph0lho81r7j3e8m6vuauthToken=
jp_gb5h29iiph0lho81r7j3e8m6vu Then use authToken as the username in your build.gradle:allprojects {
repositories {
...
maven {
url "https://jitpack.io"
credentials { username authToken }
}
}
} Add the dependency
dependencies {
implementation 'com.github.datagraninc:DGAndroidPixelSDK:20191101-v01'
}
}

2. Add the following dependencies in your project (Module gradle)
implementation 'com.google.code.gson:gson:2.8.5'
implementation 'com.squareup.okhttp3:okhttp:3.4.2' implementation'com.google.android.gms:play-services- location:16.0.0'

3. Add the following permissions in the Manifest file<uses-permission android:name="android.permission.INTERNET"/> <uses-permission android:name="android.permission.
ACCESS_COARSE_LOCATION" /> <uses-permission android:name="android.permission.ACCESS_FINE_LO CATION" />

4. Initialize the Tracker object in the Application class of your project Tracker.init(context)

5. Initialize Tracker.singleton().setActivityContext(this) in the launcher activity class of your project. It must be initialized to use tracking functions.

6. You can use the “track” function to track different events in your app.

For View clickTracker.singleton(). track(view, eventId, context);
For Event dataTracker.singleton(). track(keyEvent, eventId, context);
For Menu itemTracker.singleton(). track(menuItem, eventId, context);
For Custom Events Tracker.singleton().trackCustom(category, label, action, context);
The event IDs are different for every handler EVENT_ID_CLICK = 1; (For click events) EVENT_ID_LONG_CLICK = 2; (For Long click events) EVENT_ID_ON_KEY = 3; (For key press events) EVENT_ID_FOCUS_CHANGED = 4; (For Focus changed event)

Android
Documentation

The current version of the Android SDK covers the following events:

EVENT NAME
TRIGGERS
onClick
An onscreen element is clicked
onLongClick
An onscreen element is long pressed
onFocusChange
Focus changes from EditText
onOptionsItemSelected
An options menu was selected
An options menu was selected
A key on the keyboard was pressed
custom
Custom events
Parameters to ServerFinal request parameters
VIEW EVENTS
TRIGGER
viewName
Name of resource (i.e. id provided to the controls/views or ASCII code of characters for key press)
viewEventName
Event Name (onClick, onLongClick, etc.)
deviceInfo
Device Information
Device Information (Common Data)
EVENT NAME
TRIGGERS
currentDate
Current date and time
deviceId
Device UDID
className
Main Class name from which the method was called
deviceSystemVersion
Device IOS version
deviceModel
Device Model (like Samsung, Google etc.)
latitude
Latitude based on the current location
longitude
Longitude based on the current location
Custom Event
EVENT NAME
TRIGGERS
currentDate
Current date and time
deviceId
Device UDID
className
Main Class name from which the method was called
deviceSystemVersion
Device OS version

Sample Request

APP Version
EVENT NAME
TRIGGERS
Label
Internally set to the version status (i.e. App Installed, App Updated)
Version
Application current version
deviceInfo
Defice information

Note:
a) Whenever any viewEvents occur then View Events and Device Information from final parameters will be sent to server
b) Whenever any custom events occur then Device Information and Custom Event from final parameters will be sent to server
c) Whenever any app update or first time install is done then Device Information and App Version from final parameters will be sent to server.Important Note: you must compile the app with framework without obfuscation, you should add exception in pro guard. The obfuscation could break the framework.

Sample Request

Request for App Install & Custom Event

{
"arrTrackData": [
{
"deviceInfo": {
"className": "MainApplication", "currentDate": "12-06-2019 17:02:28",
"deviceId": "871cd4c96e366e1b", "deviceModel": "Android SDK built for x86", "deviceName": "Google", "deviceSystemVersion": "9",
"latitude": "0.0",
"longitude": "0.0"
},
"versionInfo": { "label": "App Install",
"version": "Installed Version 1"
}
},
{
"customEvent": {
"action": "Launch Event", "category": "Activity", "label": "Activity Created"
},
"deviceInfo": {
"className": "MainActivity", "currentDate": "12-06-2019 17:02:28",
"deviceId": "871cd4c96e366e1b", "deviceModel": "Android SDK built for x86", "deviceName": "Google", "deviceSystemVersion": "9",
"latitude": "0.0",
"longitude": "0.0"
}
}
]
}Request for View Clicked{
"arrTrackData": [
{
"deviceInfo": {
"className": "MainActivity", "currentDate": "12-06-2019 17:03:33",
"deviceId": "871cd4c96e366e1b", "deviceModel": "Android SDK built for x86", "deviceName": "Google", "deviceSystemVersion": "9",
"latitude": "23.02",
"longitude": "72.58"
},
"event": {
"viewEventName": "onClick", "viewName": "btnAdd"
}
}
]
}


You Got Challenges,
We Got Solutions

01. Reduce Churn

For example: find out why are your customers buying a specific product or determine why only females in a specific area are converting.

  • Base decisions on our churn reduction algorithms.
  • Send targeted products to cluesters of customers thanks to our Recommended
  • Products and Similar Taste features.
  • Retain your best customers by pinpointing them through your RFM model.
  • Find friction points with your behavioral data report.

Datagran provides many ways to reduce churn.

02.Reduce CAC

For example: target specific products to a cluster of clients by using our Recommended Product Feature, Similar taste within the Predictions tool, and lastly, set specific filters. Then, use our Launch & Optimize tool for Facebook and Google campaigns to reach your audience, based on the results.

Our clients are reducing CAC by 50%. Copy their strategy by mixing some of our features.

03.Smart Budget
Allocation

Find optimal balance by allocating budget across Facebook ads and Google Ads. Our system analysis objective, budget and cost across campaigns thanks to our Machine Learning foundation.

04.Target The Right Customer

Shift from a channel-centric to a customer-centric view to attract the right customer. Predictive analytics lays the groundwork for the entire sales process. The results from each sale or campaign is stored in the predictive platform, and our machine learning algorithm begins its process. See what worked, what didn’t, and what needs to change based on campaign results, sentiment analysis and more.

05.Managed
Services

Building predictive pipelines can be complex. That is why, our team can assist you with building the right ecosystem for your company.

01. Reduce Churn

For example: find out why your customers are buying a specific product or determine why only females in a specific area are converting.

  • Base decisions on our churn reduction algorithms.
  • Send targeted products to clusters of customers thanks to our Recommended Product feature.
  • Use the Products and Similar Taste features to send relevant content.
  • Retain your best customers by pinpointing them through the RFM model.
  • Find friction points with your behavioral data report.

Secure your customers by lowering churn rates with our Predictions tool.

02.Reduce CAC

For example: target specific products to a user cluster by using our Recommended Product, and Similar Taste features within the Predictions tool while also setting specific filters. Then, use our Launch & Optimize tool for Facebook and Google ads to reach your audience based on the results.

Our clients are reducing CAC by 50%. Copy their strategy by mixing some of our features.

03.Smart Budget
Allocation

Find optimal balance by allocating budget across Facebook Ads and Google Ads. Our system analyses objective, budget and cost across campaigns thanks to our Machine Learning foundation.

04.Target The Right Customer

Shift from a channel-centric to a customer-centric approach to attract the right customer. Predictive analytics lays the groundwork for the entire sales process. The results from each sale or campaign is stored in our predictive platform, and our machine learning algorithm begins its process. See what worked, what didn’t, and what needs to change based on campaign results, sentiment analysis and more.

05.Managed
Services

Building predictive pipelines can be complex. That is why, our team assists you with building the right data ecosystem for your company. Let's Talk!

START NOW FOR FREE
+ 1 856 -369 - DATA
156 2nd St, San Francisco, CA 94105
support@datagran.io

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