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London, UK and Helsinki, Finland: Xtract, the leader in social advertising intelligence today announced it has closed new funding of EUR 3,5 Million led by Creandum, a leading early stage technology investor together with renewed commitment from existing investor ETF III, advised by Eqvitec Partners.

Xtract answers a real need in the advertising industry which is making the advertising in social media and mobile effective. By using Xtract, customers have seen the average ad income increase with as much as 82% and in mobile campaigns response rates have increased with 30%.

“Our solutions provide genuinely innovative ways to ensure delivery of the right audience to the right commercial message at the right time and to accurately report on the effectiveness of any campaign in an automated way, something that up until now was not possible,” said Kimmo Kiviluoto, CEO of Xtract. ”We are delighted to have Creandum join the company which clearly underlines the confidence in our offering and in the management team,” he added.

Staffan Helgesson, Managing General Partner at Creandum, said: “We are very impressed with Xtract’s success in the telecom sector where leading mobile operators use Xtract to increase revenues and reduce churn. We also believe that Xtract’s approach is unique from the traditional approach in mass advertising. The transition from the traditional static advertising to the more dynamic advertising is going to happen and the brand managers and advertising agencies understanding this change will be the ones surviving the transition to digital advertising. Xtract has a unique position to be one of the leaders driving this change.”

Xtract board member Juha Mikkola of Eqvitec Partners said he was pleased by Creandum’s decision to invest. “This investment opportunity had a lot of demand and clearly marks the very interesting market and expansion opportunity that Xtract represents in the fast growing social intelligence market”

About Xtract
Xtract is the global trusted partner and innovator in Social Advertising Intelligence.
We have the technology and competence to turn large user data into cash flow. Our solutions create accurate and automated consumer profiles for mobile and online advertisers based on social interactions, behavioural and demographic data.

Xtract operates across Europe and Asia, working with over 50 companies with intensive customer data ranging from global leaders such as Nokia, T-Mobile and Vodafone to innovative challengers such as BLYK and Fonecta. Headquarters are in Helsinki, Finland and London, UK. www.xtract.com

About Creandum
Creandum is a Nordic venture capital firm investing in early-stage technology companies. The firm has 120 million Euros under management and is today the fastest growing independent partnership of its kind in the Nordic region. Creandum invests in innovative companies in high growth markets led by outstanding entrepreneurs. All investment professionals at Creandum has started companies or been part of successful startups. For more information, please visit www.creandum.com.

About Eqvitec Partners Oy
Eqvitec Partners Oy is one of the largest technology focused venture capital and private equity firms in Northern Europe. The company was founded in 1997 and employs 15 investments professionals in Helsinki and Stockholm. Eqvitec Partners currently advises five funds with a total capital base of 440 million euros. The funds invest in technology companies in all phases from start-up to buyouts. At the moment the funds have investments in 40 companies and have carried out 35 exits. More information can be found at www.eqvitec.com.

Around one month ago I moved from the research team here at Xtract to marketing. This change is an interesting one, since often people consider research and marketing to be opposites: marketing deals with presenting concepts to customers in a concrete fashion as quickly as possible, while researchers work on abstract concepts which might take years to develop into something useful.

What is surprising is that marketing and research are in many ways quite similar and even many of the skills required are the same: in both jobs, one needs to be able to innovate, present ideas effectively and communicate by writing. Also, in both jobs one has to have a vision on where the business is moving and how we can help our customers with our tools and skills.

Now I just need to get some grasp of the marketing speak, which is quite different from the technical jargon I am more familiar with. Instead of “mixture models”, “bayesian inference” and “functional programming”, I now need to be able to talk fluently about “permission marketing”, “value propositions” and “behavior targeting”. In a way, this is like learning a new languge.

I shall start with a question: “Is there still snow in Oulu?”. To this one I happen to know the answer.

Most of my last year was spent in studying methods for finding communities in social networks. Although interesting, writing the thesis was quite a job, since I wasn’t familiar with the area of study.

This is why I was flattered to receive an award for “The Best Pattern Recognition Master’s Thesis in Finland in 2007″ by the Pattern Recognition Society of Finland. The only challenge was that to get the award, I had to travel to Oulu, a city up north where I had never been before.

Despite my prejudices, the people I met in Oulu were friendly and it was fascinating to hear about the research they do on pattern recognition, machine learning and robotics. They had a lot of co-operation with industry partners, such as Nokia, in implementing intelligent mobile solutions and building robot cars.

The presentations by the other award receivers provided a wide scope into the different disciplines combining intelligence with data analysis. The other award receivers were Esa Rahtu (Doctoral thesis), Mathias Creutz (Doctoral thesis), and Kimmo Palander (Master’s thesis).

Rahtu spoke about methods for aligning images and Creutz gave an presentation on methods for finding the structure in written text automatically. Palander spoke about methods for aligning microscope images for 3d modeling. Quite exciting, indeed!

Finally, to answer the question, no, sadly there wasn’t any snow left in Oulu! Oh well, maybe next time.

… is the topic of my master’s thesis for HUT . I use “possibly” when speaking of modeling affects from mobile user experience data, because I’ve gotten the feeling these last weeks that the “recognising affects”-part just might be the topic of my PhD Now roughly in the middle of my thesis, I give you a summary of what’s happened.

What field is this exactly?

The fields concerned are affective computing, behaviorism, personality psychology and social psychology.

What do I call “mobile user experience data”?

Tom Guarriello defines in his article “Experiencing Experience” the ultimate experience is a cluster of smaller experiences. Consequently, he continues that measuring the user experience is best attained when considering as many elements in the cluster as possible.
For example, if you would consider a romantic dinner, what makes it a great experience is the cluster: candles, the dim light, the flower, the view, the slow background music, the quietness and sharing it with the other person? Take one of these alone doesn’t make the romantic. Two, maybe a little. The more element the better the experience. This is an analogy to multimodality. The more the merrier. Multimodality should be at the core of accurate affect recognition models, as emotions are intrinsically multifaceted. Having multiple modalities not only brings the accuracy of triangulation, but also by not fusing the behavioral components of moods allows to present the emotional context and maybe help to draw other conclusions if complemented by other contextual information.

How do you recognise emotions?

Recognising the user’s emotions can be done through many modalities, the most popular ones being: heart rate, skin galvanic resistance, speech tones and patterns, facial expressions, body postures and self-report. But baring in mind the current mobile phones and that the goal, being to get as large amount of users as possible with as little trouble as possible, it wouldn’t make sense to collect that kind of data, so I am focusing mainly on behavior data. This will also give a lower accuracy in time, so considering the spectrum of affective phenomena (Fig. 1) with time as main differentiating variable, I will focus on moods rather than emotions. Privacy being a concern, it is essential to compute private data in the mobile phone at low processing and battery cost.

Fig.1: Spectrum of affective phenomena (from Oatly, 2006)

These phenomena can be categorized in:

• episodes of emotions (seconds)
• moods (from hours to weeks)
• emotional disorder (from months to years)
• personality traits (lifetime)

In the context of advertising and communication, moods give a good emotional context for mobile ad targeting and filtering.

How do you recognise personality traits?

There are 2 main theories of personality: Eysenck’s model of personality (P-E-N) and the Big Five personality traits. Both have are based on the concept that there are few (respectively 3 or 5) broad dimensions or factors to describe the personality. Two factors both models have in common is neuroticism (tendency to experience negative emotions) and extraversion (tendency to enjoy positive events, especially social events).

Data I am focusing on to recognise personality traits is mobile phone usage, communication behavior, mobile internet behavior and social networks characteristics.

General mobile phone usage:

• How much people personalise their phone (wallpaper and ringing-tones)
• Time spent playing with the phone

Communication behavior:

• The time spent making calls
• The amount of incoming calls
• Time spent sending and receiving SMS messages
• Preference of voice communication or messaging
• How many people are around the person when he speaks on the phone
• The time laps people take to respond to a missed call or SMS
• Speaks on the phone without headset while driving
• The amount of sent MMS
• How often one checks if he has missed any calls/SMSs

Internet behavior:

• Time spent on mobile Internet
• Time spent for social purpose
• Time spent searching

Social network characteristics:

• Size of social network
• Amount of contacts

Once personality traits are modeled, positive and negative moods could be extracted using relevant theories of personality (Gray, Eysenck or Newman) relating to mood, personality and behavior draw a positive correlation between extraversion and positive mood, and between neuroticism (emotional stability) and negative mood (Gomez, 2000).

Triggers to Negative mood:

• Frustrative nonreward (Gray)
• Punishment (Gray)
• Novelty (inversed) (Gray)
• Displeasure (Eysenck)

Triggers to Positive mood:

• Reward (Gray)
• Nonpunishment (Gray)
• Pleasure (Eysenck)

What could correspond to such stimuli for a mobile user?
= What makes you in a good / bad mood when you use your phone?

E.g.

• Out of credit = displeasure
• Often receiving happy smileys = pleasure, reward
• Calls not returned = frustrative nonreward
• Regularity in patterns (or entropy of life) = novelty