This document proposes a predictive battery saver app called Optimizer that uses deep learning techniques to provide personalized recommendations for improving battery life. It analyzes user behavior and app usage data to predict battery drain and offers tailored suggestions without running constantly in the background. Both free and premium versions are suggested, with the premium version costing $0.99 annually. Revenue would come from the premium subscriptions and sharing anonymous user data with collaborators. The goal is to help the 1.6 billion smartphone users worldwide extend battery life through a scientific, non-intrusive approach.

1. OPTIMIZER
Predictive Battery Saver App

2. Personalized
recommendations for
improving battery life
using Deep-learning
techniques!

3. Personalized recommendations for improving
battery life using Deep-learning technique!
EXECUTIVE
SUMMARY

4. • Battery drain major headache with
smartphones
• Reason aplenty
• Apps draining power
• Improper usage. Ex: Brightness
• Currently existing apps themselves
drain battery
• No scientific solution
• Generic suggestion
• “Dim your screen”
• “Shut down all your apps”

5. • Proposed app uses Deep learning
techniques
• No background running
• Less space occupation
• Predictive scientific modelling
• Modelling done in remote server
with huge data known as big data

6. • Smartphones are inherent with degrading battery due to the
chemical nature.
• Improper usage and hogs (HIGH Power Consuming Apps) make
it worse
• Proper diagnosis and suggestion needs to be given to users
• Battery saver app itself must not EAT the battery!!

7. TARGET
MARKET

8. • All smartphones are basically target customers
• Any paid user of battery saver apps can be accounted for
premium version of this app
• Over 1.6 Billion smartphones to be shipped by 2016
• 330+Million using single battery app alone
• Huge potential!!

9. Free and premium competitors

10. Points of parity
• Battery optimizing Goal
• Uses markets to install like Google Play
• Can be installed on any smartphone device

11. Points of difference
• Uses scientific approach
involving deep learning
techniques
• Does not run on
background
• Predictive analysis made
through remote servers
• Algorithmic approach

13. Free version
• GPS needed to know about demographics details
• Demography based other app usage information-
Revenue Source
• Revenue generation through info given to other
apps regarding
- Usage
- Popularity in a region
- App status- whether a Battery hog/ good
status

14. Premium version revenue model
Expected User targets
• Targeting 10% smartphone users to use the
app (Approx. 16M)
• Among them converting 1% to premium
(160000 users).
• Doing the math for a year
Users
App
cost
Market charges-
Google play
Hardware
and other
costs (40%)
Total
revenue
160000 $0.99 $25 $63360 $95040

15. COLLOBORATORS
• Major apps like fb, linkedin
• Analytics players
• Ad agencies
• Markets such as Google Play

16. Product
Introduce
premium version
of the app
Price
Premium version
costs $0.99 for a
year of usage
Incentives
Referral programs
to upgrade to
premium
Distribution
channels
Google Play,
App store
TACTICS

17. I
M
P
L
E
M
E
N
T
A
T
I
O
N
Optimizer
Collaborat
ors
Distribution
channels
(Play)
Users
Major apps
like fb, linkedin
Proposed
App

19. ThankYou!

20. DISCLAIMER
Created by Deeban Babu, IIT Madras, during a
marketing Internship by Prof. Sameer Mathur,
IIM Lucknow.