UserSat.com value proposition

•

1 gostou•218 visualizações

Julia Kiseleva

Dados e análise

VAMP: Valuable Analytical
Platform for Mobile Environments

usersat.com | Julia Kiseleva
UserSat Product
2
MobileDesktop
User Experience on Mobiles is different

usersat.com | Julia Kiseleva
What UserSat Product Does?
3
?

usersat.com | Julia Kiseleva
What UserSat Product Does?
4
Where?
Why?
How to
ﬁx?

usersat.com | Julia Kiseleva
Who Are Our Customers?
• Mobile Application Owners
• Mobile Application Developers
• Mobile Experimentation Platform
5

usersat.com | Julia Kiseleva
How Big is The Market?
6
500.000
1.000.000
1.500.000
2.000.000
Jul’
08
Jun’
16
Number of Apps in
Apple Store

usersat.com | Julia Kiseleva
How Big is The Market?
7
2.200.000
2.000.000
669.000
600.000
234.500
Google
Play
Apple App
Store
Windows
Store
Amazon
App Store
Blackberry
World
+ Mobile Web

usersat.com | Julia Kiseleva 8
Growth in Digital Media Time Spent
Total Times (MM) Why Are They Need Us?

usersat.com | Julia Kiseleva 9
Share Time Spent on Mobile vs. Web
Share of Mobile Time
Why Are They Need Us?

usersat.com | Julia Kiseleva 10
Unique Visitors
Average Monthly Audience:
Top 1000 Mobile Aps vs 1000 Mobile Web Sites
Why Are They Need Us?

usersat.com | Julia Kiseleva
Why Do They Need Us?
• Mobile Applications/Web have to understand and
predict user needs:
• to retain users
• to earn user loyalty
• to increase they KPIs
11
That’s what our produce EXACTLY DOES!!!

usersat.com | Julia Kiseleva
What is Wrong With Current
Solution?
12

usersat.com | Julia Kiseleva
Why Is It Wrong?
Or Mobile is Diﬀerent
13
Photo credit: Delwin Steven Campbell via Visualhunt.com / CC BY

usersat.com | Julia Kiseleva
Why Is It Wrong?
Or Mobile is diﬀerent
14
Photo credit: Delwin Steven Campbell via Visualhunt.com / CC BY

usersat.com | Julia Kiseleva
How UserSat Solution is
Diﬀerent?
15

usersat.com | Julia Kiseleva
How UserSat Solution is
Diﬀerent?
16

usersat.com | Julia Kiseleva
Our “Competitors”
17
SCALABLE
GESTURE
ANANLYSIS
INTERPRABLE
METRICS
MIXPANEL
GOOGLE
ANALYTICS
APPSEE
USERSAT

usersat.com | Julia Kiseleva
What Our Technology Does?
• Sense
• Plan
• Act
18

usersat.com | Julia Kiseleva
Sense: Scalable Data
Collection
19

usersat.com | Julia Kiseleva 20
Sense: Scalable Data
Collection

usersat.com | Julia Kiseleva 21
Plan: Deep Learning

usersat.com | Julia Kiseleva
Plan: What Technology Can?
22
Collect More
Data
Op.mize
User
Experience
Improve Our
Product

usersat.com | Julia Kiseleva
Act: Understand Users
23

usersat.com | Julia Kiseleva
Overview
24

usersat.com | Julia Kiseleva
Business Model
25
APP
Marketing
Analytical
Service
Interactive
Dashboard
CUSTOMER
3.IMPROVMENT
1.  DATA
[users/month]
2. SATISFACTION FEEDBACK
KPIs Growth
4. SUBSCRIPTION
[pay as you go]

usersat.com | Julia Kiseleva
Team
26
Dr. Julia
Kiseleva, CEO
Vadim Denisov,
CTO
Steve Seuntjens,
COO
Prof. Maarten
de Rĳke (UvA)
Research Support
Dr. Jaap
Kamps (UvA)
Business Development
Jonathan
Steen (MBA)
Kevin Matz
(MBA)

usersat.com | Julia Kiseleva
Traction
27
STW Valorization Grant — 40 K (Feb 2016)
and many more to come …
App Store apps

usersat.com | Julia Kiseleva
Thanks!
• UserSat solution is
• VERY timely
• EXTREMELY innovative
• WELCOMED at the market
• EASY to enter
28

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Abstract—In many web information systems like e-shops and information portals predictive modeling is used to understand user intentions based on their browsing behavior. User behavior is inherently sensitive to various contexts. Identifying such relevant contexts can help to improve the prediction performance. In this work, we propose a formal approach in which the context discovery process is defined as an optimization problem. For simplicity we assume a concrete yet generic scenario in which context is considered to be a secondary label of an instance that is either known from the available contextual attribute (e.g. user location) or can be induced from the training data (e.g. novice vs. expert user). In an ideal case, the objective function of the optimization problem has an analytical form enabling us to design a context discovery algorithm solving the optimization problem directly. An example with Markov models, a typical approach for modeling user browsing behavior, shows that the derived analytical form of the optimization problem provides us with useful mathematical insights of the problem. Experiments with a real-world use-case show that we can discover useful contexts allowing us to significantly improve the prediction of user intentions with contextual Markov models.

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Predictive Web Analytics is aimed at understanding behavioural patterns of users of various web-based applications: e-commerce, ubiquitous and mobile computing, and computational advertising. Within these applications business decisions often rely on two types of predictions: an overall or particular user segment demand predictions and individualised recommendations for visitors. Visitor behaviour is inherently sensitive to the context, which can be de ned as a collection of external factors. Context-awareness allows integrating external explanatory information into the learning process and adapting user behaviour accordingly. The importance of context-awareness has been recognised by researchers and practitioners in many disciplines, including recommendation systems, information retrieval, personalization, data mining, and marketing. We focus on studying ways of context discovery and its integration into predictive analytics.

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UserSat.com value proposition

1. VAMP: Valuable Analytical Platform for Mobile Environments

2. usersat.com | Julia Kiseleva UserSat Product 2 MobileDesktop User Experience on Mobiles is different

3. usersat.com | Julia Kiseleva What UserSat Product Does? 3 ?

4. usersat.com | Julia Kiseleva What UserSat Product Does? 4 Where? Why? How to ﬁx?

5. usersat.com | Julia Kiseleva Who Are Our Customers? • Mobile Application Owners • Mobile Application Developers • Mobile Experimentation Platform 5

6. usersat.com | Julia Kiseleva How Big is The Market? 6 500.000 1.000.000 1.500.000 2.000.000 Jul’ 08 Jun’ 16 Number of Apps in Apple Store

7. usersat.com | Julia Kiseleva How Big is The Market? 7 2.200.000 2.000.000 669.000 600.000 234.500 Google Play Apple App Store Windows Store Amazon App Store Blackberry World + Mobile Web

8. usersat.com | Julia Kiseleva 8 Growth in Digital Media Time Spent Total Times (MM) Why Are They Need Us?

9. usersat.com | Julia Kiseleva 9 Share Time Spent on Mobile vs. Web Share of Mobile Time Why Are They Need Us?

10. usersat.com | Julia Kiseleva 10 Unique Visitors Average Monthly Audience: Top 1000 Mobile Aps vs 1000 Mobile Web Sites Why Are They Need Us?

11. usersat.com | Julia Kiseleva Why Do They Need Us? • Mobile Applications/Web have to understand and predict user needs: • to retain users • to earn user loyalty • to increase they KPIs 11 That’s what our produce EXACTLY DOES!!!

12. usersat.com | Julia Kiseleva What is Wrong With Current Solution? 12

13. usersat.com | Julia Kiseleva Why Is It Wrong? Or Mobile is Diﬀerent 13 Photo credit: Delwin Steven Campbell via Visualhunt.com / CC BY

14. usersat.com | Julia Kiseleva Why Is It Wrong? Or Mobile is diﬀerent 14 Photo credit: Delwin Steven Campbell via Visualhunt.com / CC BY

15. usersat.com | Julia Kiseleva How UserSat Solution is Diﬀerent? 15

16. usersat.com | Julia Kiseleva How UserSat Solution is Diﬀerent? 16

17. usersat.com | Julia Kiseleva Our “Competitors” 17 SCALABLE GESTURE ANANLYSIS INTERPRABLE METRICS MIXPANEL GOOGLE ANALYTICS APPSEE USERSAT

18. usersat.com | Julia Kiseleva What Our Technology Does? • Sense • Plan • Act 18

19. usersat.com | Julia Kiseleva Sense: Scalable Data Collection 19

20. usersat.com | Julia Kiseleva 20 Sense: Scalable Data Collection

21. usersat.com | Julia Kiseleva 21 Plan: Deep Learning

22. usersat.com | Julia Kiseleva Plan: What Technology Can? 22 Collect More Data Op.mize User Experience Improve Our Product

23. usersat.com | Julia Kiseleva Act: Understand Users 23

24. usersat.com | Julia Kiseleva Overview 24

25. usersat.com | Julia Kiseleva Business Model 25 APP Marketing Analytical Service Interactive Dashboard CUSTOMER 3.IMPROVMENT 1.  DATA [users/month] 2. SATISFACTION FEEDBACK KPIs Growth 4. SUBSCRIPTION [pay as you go]

26. usersat.com | Julia Kiseleva Team 26 Dr. Julia Kiseleva, CEO Vadim Denisov, CTO Steve Seuntjens, COO Prof. Maarten de Rĳke (UvA) Research Support Dr. Jaap Kamps (UvA) Business Development Jonathan Steen (MBA) Kevin Matz (MBA)

27. usersat.com | Julia Kiseleva Traction 27 STW Valorization Grant — 40 K (Feb 2016) and many more to come … App Store apps

28. usersat.com | Julia Kiseleva Thanks! • UserSat solution is • VERY timely • EXTREMELY innovative • WELCOMED at the market • EASY to enter 28

29. usersat.com | Julia Kiseleva 29

UserSat.com value proposition

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UserSat.com value proposition