10. HOW IT WORKS ?
This app allows you to filter out the spam sms’s
reaching your inbox and reduce the clutter.
11. COMPANY OVERVIEW
Core competencies
• One-Stop destination for finding recipes by ingredients
• Reduced time and efforts
• Wide range of options for selecting recipes, depending on cooking time,
reviews and ratings.
12. STRATEGIC ASSETS
• In-app purchases, in keeping with the
freemium model
• In-app advertisements, that’ll generate
revenue
• Communication Partners
13. MARKET OVER-VIEW
Spam detection and control apps
are the next step in the marketing
business, offering users the ability to
control the spam sms’s being sent by
focus groups targeting their
audiences.
14. Opportunities
• Untapped Market
• Lesser Competitors
• Increasing smart
phone penetration
• Explosion of
information
Threats
• Relatively new concept tough to set industry standards
• No benchmarking of parameters
• Requires awareness campaigns for both the community
and users
16. Strategy
• Our Initial strategy is to target
key customers who form the
backbone of the community
• Once we get enough data
from this online community, we
introduce the paid version of
the app thus attracting more
and more customers
17. Value proposition
Customer value: Improving the productivity of the users by
restricting their interface with the spam sms’s
Collaborator Value: Forming the online community who
will gather the data required to maintain the spam filtering
algorithm
Company Value: Positive work environment, great culture,
growth potential, opportunity to pursue personal interests
19. PRODUCT
Free Version:
The free version of the app will have the following
features:
• Real-time spam detection of sms’s marketing banking,
FMCG, investment related products
• User’s ability to push those spam sms’s into either trash or
some temporary location to give a final go/no-go push
to be moved to trash
20. • Premium version of the app will have:
• Spam related Analytics
• Extending the functionality to e-mails
• Unlimited cloud Storage option, for both spam and non-
spam related sms’s
• In case of threats/hoaxes, an option to inform the local law-
enforcement authorities, with just a tap of a button
22. IMPLEMENTATION
Work with the online community to gather the data required
to develop a machine-learning based prediction filtering
model which can categorize an sms as spam or not-spam.
23. DISCALIMER
Created by Krishna Chaitanya Pullakandam,
IIT Madras, during Marketing internship under
Prof. Sameer Mathur, IIM Lucknow