1. HUG Bucharest
Radisson, 29 January 2015
© Avira Operations GmbH & Co KG, 2015. All rights reserved. For internal use only.

2. Tonight’s Talks
1. Big Data Tools & Mobile Apps
– Cornel Balaban, Avira
2. Windows Secure Hadoop Clusters
– Remus Rusanu, Microsoft
3. Couchbase 3.0, new Java Client Library
– David Maier, Couchbase
4. Privacy vs Relevancy in Consumer Applications
– Jason Radisson, Avira
© Avira Operations GmbH & Co KG, 2015. All rights reserved. For internal use only.

3. Background: Privacy Paradox at Avira
1. At Avira we have more than 30M daily active users of our security products
2. In order to keep our users safe, we have to deploy real-time detection and
other data-intensive AI applications
3. Avira was a trailblazer in terms of business model innovation with freemium.
4. Now increasingly the security industry is moving to a freemium model.
There’s no turning back the clock
5. So our industry is moving to advertisement business models predominantly
6. At the same time, threats are spreading to new media. We have access to
ever increasing volumes of consumer data – mobile, browser, soon machine-
7. Users in general expect an ever increasing amount of relevancy in
advertisement. This is broadly true
8. Yet, concerns about privacy are distributed unevenly within our user base
9. Net-net: e have to engineer more selective approaches for using user data

4. Our Interim Approach
1. Protect the user: Anonymous data
2. Accept that there is a trade-off and will be inaccuracies
3. No matter what: No GUID-UID mapping

5. User Expectations: Web-Scale Personalization =
Machine-Learning + Real-Time
• Top of the hill
constantly moves;
manual optimization
is not sufficient
• Move with the target,
considering all the
information up this
exact moment
• And automate
iteration. Letting your
ML algorithms do the
hill-climbing for you
Last week‘s
optimum
Now … user’s in-
session optimum
ML algorithm’s
starting point
And this
morning…
Manual competitors
‘shoot behind the duck.’
Their services feel
programmatic & ‘stale’

6. High-Level Architecture of our Platform
ABS ICM/GMS SERP Launcher
Android
(in-app)
Avira Offers real-time personalization engine
• Given everything we know about an individual, what is the next best action?
 Display recommendations or advertisements
 Offer deals or coupons
 Customize a behavior
 Re-rank a search result
• Built on latest open-source nosql architecture to ensure scalability
Comparison
Shopping
Dynamic
Coupons
Deals
Avira’s own
paid offering
Avira’s own
free offering
User-impressions
Loyalty
Programs
Avira.com
Inventory to be optimized

7. Phased Approach to Personalization
Phase Event Path User / business intent System requirements and privacy*
3. User-
item
profiling
Cyber
Monday
Bought Lange ski-racing
boots
• User intent: This guy really
likes sports and is upgrading
his equipment as he gains
skill
• Avira: deepen our reach in
category while we expand to
other categories
1. Crunch all behavioral data about all
users and persist first-class scores about
this specific individual, based on his
foundational intent and category
adjacencies
2. Data are stored for months anonymously
at atomic level and archived for years
3. Expand to ICM/GMS, OE, Launcher
New
Year‘s
eve
Viewed navy Peak
Performance R&D jacket
Last
Thursday
Bought Nike short-
sleeved running shirt in
florescent yellow
2. Session-
based
optimizatio
n
Now Browsing bikinis and
men’s flip-flops
• User intent: Going on beach
vacation, shopping for
himself and his girlfriend
• Avira: maximize conversion
with dynamic flow & content
1. Query a subset of live-session data and
reach an optimal decision at run-time
2. User data are cached a few seconds,
purged end of session
3. Expand to Avira.com
1. Item-
based
lookups
Now Viewed a specific item • User intent: Buy a standard
item (that happens to be in
the Billiger catalog)
• Avira: re-direct the purchase
to a Billiger partner retailer
1. SKU-level (or KW) look-up and find
better price
2. All data we handle are vendor data
(items and prices)
3. Billiger to ABS, proxied via Avira Offers
* Unless PII or financial information, user traffic is not encrypted (HTTPS)

8. Opportunities to Explore
1. Opportunity exists to make the tradeoff transparent for users. What product
behavior and features depend on what kinds of data elements
2. Could this be a point of differentiation for consumer IT companies,
especially those based in Europe with a different set of attitudes toward
privacy? Should it be reflected in our product roadmaps?
3. Is there a programmatic element to be tested: Does Avira have a unique
opportunity to educate our users about the uses and value of data we are
gathering? Why not use our incredible reach to do so?