This week KXEN launched its social network analysis tool, thus gaining a unique edge in being the first to launch social network tools for analytics. Having worked with KXEN as an analyst for a scoring model, I am aware of the remarkable innovations they bring to their premium products. In an exclusive interview, KXEN’s Vice President for Strategic Business Development, Françoise Soulie Fogelman agreed to share some light on this remarkable new development in statistical software development.
Ajay – Françoise, how does the Social Network Analysis module help model building for marketing professionals.
Françoise – KXEN Social network Analysis module (KSN) helps build models that take into account interactions between customers. This is done in 3 stages :
- The data describing interactions is used to build a social network structure (actually, various social network structures are usually built in one pass through the data). You can explore your network to understand better the behavior of a given customer and what is happening around him.
- From each social network structure, a set of attributes is automatically built by KSN for each node: it could be number of neighbors, average va…
This week KXEN launched its social network analysis tool, thus gaining a unique edge in being the first to launch social network tools for analytics. Having worked with KXEN as an analyst for a scoring model, I am aware of the remarkable innovations they bring to their premium products. In an exclusive interview, KXEN’s Vice President for Strategic Business Development, Françoise Soulie Fogelman agreed to share some light on this remarkable new development in statistical software development.
Ajay – Françoise, how does the Social Network Analysis module help model building for marketing professionals.
Françoise – KXEN Social network Analysis module (KSN) helps build models that take into account interactions between customers. This is done in 3 stages :
- The data describing interactions is used to build a social network structure (actually, various social network structures are usually built in one pass through the data). You can explore your network to understand better the behavior of a given customer and what is happening around him.
- From each social network structure, a set of attributes is automatically built by KSN for each node: it could be number of neighbors, average value of a given customer attribute among neighbors… Actually, you can have statistics on anything you have loaded into the system as customer node decoration. Usually, you’ll generate at this stage a few tens of social attributes per social network structure.
- You then join these social attributes to the existing customer attributes. After that, you build your model as usual.
Ajay – But how does the KSN module work, and which mathematical technique is it based on (or is it just addition of extra variables)? Are there any proprietary patents that KXEN have filed in this field (both automated modeling as well as social network analysis)?
Françoise – The KSN module uses (for extracting social attributes) graph theory. KXEN has not filed a patent in relation to KSN.
Ajay – There are many modeling software applications but very few that involve social network analysis, though many companies have expressed interest in this. What are the present rivals to KSN module specifically in software, and who do you think the future rivals will be?
Françoise – There are many software tools, but when it comes to the ability to handle very large graphs, not very many are left. We consider that our only real competitor today is SAS, which has an offer for Social Network Analysis, but this product is specifically targeted for fraud in banking and insurancing. There are also companies positioned in Telco, usually offering a consulting service, built around an internal product. We think our solution is unique in its ability to handle very large volumes (we’re talking here more than 40 M nodes and 300 M links) and to address all industry domains. As usual, we offer a tool that is an exploratory tool, giving the customer the ability to produce by himself as many models as he wants.
Ajay – Who would be the typical customer or potential clients for KSN module? In which domains would this module be not so relevant? Are there any specific case studies that you can point out?
Françoise – This is the first version, so we do not really know yet who the typical customer will be and cannot point yet to case studies. However, Telco operators have expressed a very strong interest and we already have a Telco customer with whom we’ve worked on marketing projects. So our first case studies will most certainly come from Telco. We are working on some research projects in the retail space. We think that banks (for fraud), social sites, and blog sites and forums will be our next customers. The sector where I do not see a potential is manufacturing industries.
Ajay – How would privacy concerns of customers be addressed with the kind of social network analysis that KSN can now offer to marketers.
Françoise – KXEN offers a tool to build models and is not concerned with the problem of collecting, storing and exploiting data: this is KXEN customer’s responsibility. Depending upon the country, there are various jurisdictions protecting the storage and use of data and those will naturally apply to building and analyzing Social Networks. However, in the case of Social Network Analysis, the issue of “ethical” use will be more sensitive.
Ajay –What kind of hardware solutions go best with KXEN’s software. What are the other BI vendors that your offerings best complement?
Françoise – KXEN software in general and KSN in particular run on any platform. When using KSN to build decent size graphs (with tens of millions of nodes and hundreds of millions of links for example), 64-bit architecture is required. A recent survey of KXEN customers shows that the BI suites used by our customers are mostly MicroStrategy and Business Objects (SAP). I would also very much like to mention Advizor Solutions, which offers data visualization software already embedding KXEN technology.
Ajay – Do you think the text mining as well as the data fusion approach can work for online web analytics, search engines or ad targeting?
Françoise – Of course, our data fusion approach can be very well suited for online web analytics and ad targeting (we have a number of partners that either are already using KXEN for this purpose or developing applications in these domains using KXEN technology). We would be more cautious about search engines per se.
Ajay – Are there any plans for offering KXEN products as a Service (like Salesforce.com) instead of the server based approach?
Françoise – We do not yet have plans to offer KXEN products as a service. But again, we have partners such as Kognitio that offer analytics platforms embedding KXEN.
Françoise Soulie Fogelman is responsible for leading KXEN business development, identifying new business opportunities for KXEN and working with Product development, Sales and Marketing to help promote KXEN’s offer. She is also in charge of managing KXEN’s University Program.
Ms Soulie Fogelman has over 30 years of experience in data mining and CRM both from an academic and a business perspective. Prior to KXEN, she directed the first French research team on Neural Networks at Paris 11 University where she was a CS Professor. She then co-founded Mimetics, a start-up that processes and sells development environment, optical character recognition (OCR) products and services using neural network technology, and became its Chief Scientific Officer. After that she started the Data Mining and CRM group at Atos Origin and, most recently, she created and managed the CRM Agency for Business & Decision, a French IS company specialized in Business Intelligence and CRM.
Ms Soulie Fogelman holds a master’s degree in mathematics from Ecole Normale Superieure and a PhD in Computer Science from University of Grenoble. She was advisor to over 20 PhD on data mining, has authored more than 100 scientific papers and books and has been an invited speaker to many academic and business events.
(Ajay – So it seems like an interesting software and with the marketing avenues for social networking growing, and analytics modelers exploring the last bit of data for incremental field – this is an area where we can be sure of new developments soon. I wonder what the response from other analytics vendors, including open source developers, would be, as this does seem to be a promising area for statistical modeling as well as analysis. What do you think? Can I search all data from Twitter, Facebook , search results on Indeed .com and Linkedin and add it to your credit profile for creating a better propensity model? 🙂 Will the credit or marketing behavior scores of your friends affect your propensity and thus the telecom ads you see while surfing…?)
