Archive

I run into an interesting website the other day. It’s called “My Parents Just Joined Facebook”. The site is edited by Jeanne and Erika who love their parents dearly (their words). I laughed while reading the cuts of conversations between the young kids and their parents over Facebook. If I were a mother of a teenage kid I would for sure visit this site often hoping to find new conversations and opinions every time I visited it. But, I do have other good reasons. As a marketer, I want to understand the new generation of “digital born”. I often ask myself the question: Do I get them?

Jeanne and Erika are good examples of the customer 2.0. Born digital, the customer 2.0 can be very different from what we’re used with. Why? Because, in most of the cases, we are simply just not well prepared to start a dialog with them. Customer 2.0 is asking us to make their experience with our brand fun, social and engaging. They are asking us to participate in a conversation with them. They can and do express their opinions about our brand freely and with as sophisticated tools as we do. This way, contributing in building our brand’s image. So, ready or not, we have jumped on the same boat with our customers and started a dialog.

Holding their attention
Here comes the next question: If we’ve handled the dialog part well and they use our services today, does that mean they will use them tomorrow? Holding the attention of the customer 2.0 means we need to hold the attention of their friends and community too.

We must learn more about communities and try understand how social networks work. Take churn in telecoms as an example. It has been shown that churn is highly viral. The churn behaviour within one’s community affects the individual’s churn decision. Combining information about social networks with the more traditional information about customers such as demographics and behavioural data in churn prediction can maximise the total number of saved customers through loyalty campaigns.

“Can customer 2.0 be a loyal customer” is also the topic of Sari Aapola’s speech at the 9th Customer Retention Forum on June 23rd in Singapore.

Further resources about social network analytics and solutions can also be found at our website: www.xtract.com.

An Alpha is similar to a champion or an influencer. Nevertheless, in addition to having high influence and links in the community, the Alpha also has high probability to buy some of your products, acquire more customers or churn away. So, there are different kinds of Alphas and today I want to tell you how we find the Acquisition Alphas.

For Customer Acquisition, we use highly advanced analytics software that can process billions of data points. This powerful software is highly accurate. We find exactly those subscribers that have the highest potential to recruit new ones from another network. We take into account their influence level and number of off-net (out of your network) friends.

The illustration shows subscribers that have high or low influence and that might have off-net friends. Xtract identifies that the subscriber with the green circle has off-net friends (the ones in yellow) as well as high recruiting influence. This means that she would be the best target for your member-get-member campaign.

Finding the Alphas in your customer base is like finding gold in a goldmine. Remember! You need your Alphas and you need to treat them well.

This week, the University of Antwerp has been hosting the ECML-PKDD conference. It is a good opportunity to hear the newest thinking in machine learning and knowledge discovery, and talk directly to researchers. The organizers have worked very hard to make the conference a success. One of their many good ideas is to have every paper be presented both as a talk and as a poster, so if you have questions that were not answered in the talk, the author can explain the work again using the poster as an aid.

On Tuesday I had the opportunity to chair the Matrix Factorization session, arguably the highest-quality research session at the conference, since out of the four papers presented one received the Best Paper in Machine Learning award, and another one the Best Student Paper in Knowledge Discovery award.

To those of us who didn’t take Linear Algebra 101, Matrix Factorization may sound imposing, but really it is a beautiful, unifying idea behind many techniques such as community discovery, document classification (e.g. into spam and non-spam emails), and collaborative filtering, which is what Amazon or Netflix does when they recommend an item for you based on your previous purchases compared to those of other customers.

In the session, Ajit Singh gave a talk on how the matrix factorization idea encompasses several methods that might not look like matrix algebra on the surface. Alexandros Karatzoglou explained several improvements on Maximum Margin Matrix Factorization, one of the hottest collaborative filtering methods around. Pauli Miettinen discussed factorizing binary matrices, which is quite a different problem from usual linear algebra methods, and Bin Cao et al.’s paper was about a new adaptive way to compute a similarity metric for collaborative filtering.

Dessert served in style at the conference banquet on Wednesday

Tomi Ahonen has really an interesting post in Communities Dominate Brands blog. I agree very much with him. Anyway, I would like to emphasize that these digital identities work also on profile levels, so that you don’t have to know individuals or their phone numbers, but you can know customer profiles and then link or predict to which profile each individual belongs to. And this we have implemented many times.

The upstairs of Grand Café in the center of Barcelona offered a warm atmosphere for Mobile Monday’s gathering. Very untypical for the Mobile World Congress glamorous experiences, this networking dinner was low key, more like a traditional southern wedding. Participants, obvious for Barcelona during the MWC, came from all around the world and the bunch consisted of MoMo chamber members, start ups, large corporations, industry analysts, VCs as well as officials, all having one thing in common – passion and appreciation for social networks between peers.

Dr. Juha Vesanto from Xtract held a guest lecture at Helsinki University of Technology about the data analytics process and how to turn data into solutions to business problems. Other guest lectures were Jyrki Alakuijala from Google.

I was with the engagement marketing guru Alan Moore in Library House’s Media Tech event in London this week. Alan presented Xtract and Social Marketing Intelligence to the audience, which included many people from the leading media and investment companies. Advertising and social networks were the main topics in most of presentations this year. Antti Örling from Blyk (Xtract’s customer) especially emphasized the value of targeting and relevance in mobile advertising. He mentioned that Blyk achieved 12% to 43% response rate in their mobile campaigns, compared to the norm of 3% to 6% in mobile and 0.2% in the internet.

People still have a lot of questions, e.g. “how will social network, mobile advertising and business models be like in the future?”. But people see the value of targeting and relevance in digital advertising, and understand the “hot media” needs social marketing intelligence.

You can also read more from Alan’s blog.

One interesting problem in social networks is to identify the various roles of people. Some people forward information between communities, others are the central players in communities and some are peripheral persons with only a small social network.

At Xtract, we have studied the roles in social networks for a long time. Our Alpha User concept is directly related to finding the central players in networks. During the years, also other network roles have been analyzed by us.

In the academics, there is also same interesting research being done on the topic of finding network roles. One interesting paper is Classes of complex networks defined by role-to-role connectivity profiles (pdf) by Roger Guimerà et al. They separate the roles in networks into six groups.

Another interesting paper with quite similar aims is Node Roles and Community Structure in Networks (pdf) by Jerry Scripps et al. They have given descriptive names to the different types of people found in social networks. Big fish are the persons who have many friends but belong to only one community. Ambassadors have many friends and belong to multiple communities. Bridges connect communities and loners have only a few friends and participate in just one community.

The scientific work done on finding network roles provides both inspiration and directly applicable methods that can be used to help clients understand their social neighborhoods.