Download pdf - Vietnam Mobile Day 2013: Business Intelligence Driven Marketing

Business Intelligence Driven Marketing

©Copyright 2013-2014. Spicy Cinnamon Pte. Ltd


Introduction

Alex Sung

• Technical Lead at eBay

• Sr. Product Manager at GREE International

• CTO at Spicy Cinnamon


Focus Today

Contents

• Identifying Key Performance Indicators (KPI)

• Designing an Analytics Platform

• What’s next & other notes


KPI


KPI

Topics

• Purpose

• Overview

• Important KPI’s

• Segmentation


KPI - Purpose

KPI’s evaluate the health of your product and your

business. 3 categories:

• Acquisition

• Engagement

• Monetization

Goal: final cost per user is less than final life time

value of user


KPI - Overview

Acquisition Engagement Monetization

• Cost (per user)

• Cost-per-impression (CPI)

• Cost-per-click (CPC)

• Cost-per-install (CPI)

• # Impressions

• # Clicks

• Click-through-rate (CTR)

• # Downloads

• # Installs

• # Activations

• Conversion (CVR)

• K-factor (Virality Rate)

• DAU

• MAU

• Stickiness (DAU/MAU)

• Retention Rate (RR)

• Session Length

• Life Time (LT)

• Product-specific

• Spending DAU

• Spend Amount

• ARPU

• ARPPU

• ARPDAU

• Life-time Value (LTV)


KPI - Overview (Today)

Acquisition Engagement Monetization

• Cost (per user)

• Cost-per-impression (CPI)

• Cost-per-click (CPC)

• Cost-per-install (CPI)

• # Impressions

• # Clicks

• Click-through-rate (CTR)

• # Downloads

• # Installs

• # Activations

• Conversion (CVR)

• K-factor (Virality Rate)

• DAU

• MAU

• Stickiness (DAU/MAU)

• Retention Rate (RR)

• Session Length

• Life Time (LT)

• Product-specific

• Spending DAU

• Spend Amount

• ARPU

• ARPPU

• ARPDAU

• Life-time Value (LTV)


KPI - K-factor (Virality)

Value:

• A measure of how virally effective or social the app is

• More viral = less cost to hit # Installs KPI

• Longer app life time

Definition:

• K = # invites & conversion rate (1 = status quo)

Suggestions:

• Social network integration (FB, Twitter connect)

• Competitive or cooperative systems (raids, trading)

• Friend codes

• Invitation incentives (HC, speed-up’s)


KPI - Retention Rate (RR)

Value:

• Indication of how well the app keeps users returning

• More returns = longer user life = more monetization

• Highly targetable cohorts

Definition:

• Day0 = First time activated users on specific date

• DayX RR = # users from Day0 return / # Day0 users

• Day0 is constantly rolling (every day is a new Day0)

• Day1-14 RR most indicative of user behavior

• Followed by Day30, 60, 90

• View DayX RR as trending or averaged over data points

• Averaging Day30 RR requires 2 months


KPI - Retention Rate (RR) continue

Suggestions:

• Good product and useful service

• Virality – friends are on it

• New content (whether by developers or active community)

• Incentivized returns (HC)

• Special events (discounts, raids, limited time)

• Push notification reminders


KPI - Life-Time Value (LTV)

Value:

• Ultimately, how much revenue each average user provides

Definition:

• Simple, ARPDAU * LT (estimate LT based on RR)

• Most popular, usually assume 30-Day LTV

• Strict, create cohort for first time users for each day for LT

# of days. Track & sum up IAP for each user. Average

across cohorts.

• Difficult to define: are users that provide community

content or are virally active part of value?


KPI - Life-Time Value (LTV) continue

Suggestions:

• Improvement to RR & worth paying service / product

• Discount events to IAP

• Strong visual indicators (juicy feedback)

• Provide enough access points to IAP

• Provide enough options for IAP

• There are high-rollers, provide options for them

• Allow pay-to-speed-up

• Special expression based VG

• Do not allow pay-to-win; risk of damaging community


KPI - Segmentation

Time Acquisition Monetization

• First-time users

• New vs Existing (30-days)

• Cumulative vs Daily

• Hardware

• Model

• Manufacturer

• OS Version

• Marketplace

• Ad Network

• Campaign

• Creative

• Incentivized vs non

• Invitation

• Organic

• Geo-location

• Language

• Spenders vs non-spenders

• One-time spenders

• SC vs HC vs real-cash

• Age

• Gender

• Ethnicity

• Occupation

Platform Location Demographic


Analytics Platform


Analytics Platform

Topics

• Data Collection & Integration

• Data Engine & Big Data Processing

• Visualizations & Services


Data Collection & Integration

Ad NetworksClients

App Engine

Data Collector

Analytics

Logger

Raw Data

Ad Network

SDK

API

Integration

Client SDK

• Client-side SDK’s simpler to integrate

• Some Ad Networks provide analytics

• SDK’s must be scheduled in release

plan

Server API

• API provides scheduling flexibility

• Requires client to send uniqueness

and segmentation data

Data Collector

• Store uniqueness, segmentation, &

event data

• Collect application data via web log

scraping

• Collect Ad Network raw data for

acquisition to engagement mapping

• Sends collected analytics data to Data

Engine for processing

Rules EngineJob Status

Tracker

Scheduling Framework

Validation Framework

Notification Framework


Data Engine & Big Data Processing

ETL

Pig / Hive

Intermediate

Events(HDFS)Segmentation

Buckets(HDFS)

Big Data Processing

(Hadoop Cluster)

Summary

Containers

Aggregated Storage

Data Collector

Workflow Engine

Analytics

Data

Hadoop

• Intermediate data storage as

files

• HDFS custom data mining

• Scheduled ETL to process

summarized data

• Workflow Engine to manage

Hadoop jobs

Aggregated Storage

• Summary data stored in RDBMS

• Connected to Visualization tools

Real-Time

• Not everything, identify critical

KPI and segmentations

• Revenue

• # Sessions

• Enterprise solutions:

Vertica, Teradata


Visualizations & Services

Dashboards

• Pictures are worth a thousand words

• Tell a story

• Reporting is only valuable if it is

actionable (avoid vanity reporting)

• Standardized KPI reporting for

business health

Services

• Funnel analysis & A/B testing for

product

• Alerts & notifications for engineering

• Sudden drop in # sessions means

app engine may be down

• Visual data mining

Tools & Tech

• 3rd party: Tableau, Microstrategy

• HTML5 for web and mobile support


What’s Next


What’s Next

Ad Networks

• Obtain internal raw data

• Vendor reporting cannot always be trusted

• Cohort to find channels with high-quality users

• Strategize between burst vs consistency

marketing campaigns

• Normalize channel data


What’s Next continue

Engagement

• Acquiring users is expensive

• Focus on Live Ops for re-engagement

• Provide fresh content

• Provide community and discount events

• If you support multiple apps / games

• Cross-Promote (X-Promo) at end of user life

• Be careful to not self-cannibalize


Thank You

AiTi-Aptech & Vietnam Mobile Day 2013

We are HIRING!

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