Business network Analysis

Using social network analysis to induce business success

Why use network research?

By visualizing monitored links and all network endpoints simultaneously, we can map more complex processes that take effect on more than one level. Thus it becomes possible to understand highly sophisticated implications, to illustrate these clearly, to analyze the results comprehensively, and to draw previously unattainable conclusions. An effective and lifelike method to examine trends and anomalies in your most important asset: your client network.

About the professional background of the methodology

The C-Finder software that is able to identify the network's group structure is part of the FirmNet program package. The theoretical and methodological approach of the C-finder software (clique percolation method) was introduced in one of the most famous international natural-scientific journals, Nature, in June 2005. The results of customer group dynamics were announced in an article in Nature in 2007.

Business network analysis ensures assistance in three main fields:

• 1. Product / service diffusion
• 2. Customer group management
• 3. Recommendation system

PRODUCT / Service Diffusion

Accelerating the spread of services and products

In marketing, - centre points - are usually referred to as influential users, opinion leaders, the customers that matter - these are the people who speak more about a given product or service than your average user, and do it more openly. Because of their many aquanitances, they are the first to notice and use the experience of innovators. Although they themselfes are not innovators, they are key to the introduction of an idea or product.

Sociologists and marketing experts are aware of this phenomenon. But until recently, they thought about these central points as incomparable and impossible to detect. Social network models did not support the existence of central points. The structure allowed by the scale-independent networks was the first to ensure the deserved place of central points.

Customer group management

Costumer groups are identified based on links that are expected to grow, stagnate or disappear. If you know more about these groups, you are able to exert a stronger influence and faster, using fewer but well-identified business procedures.

• Which are the key-groups? What is their typical customer behavior?
• What are the signs that a customer group is likely to break up (eg. go to a new mobile company)?

By using business network analysis you can identify those customers who are likely to use another service provider.

Recommendation System

Online stores are already using customer purchasing data (customer basket analysis) to make recommendations that target the customers' needs based on his/her characteristics and purchasing patterns. With the help of new data analysis methods we are able to determine which service they would buy most likely. By casting the foregoing client segmentations on this network we can refine service recommendations.

• What service should we suggest to our clients?
• Which is the service we should make a discount on?
• What service should we advertise?

Furthermore, based on the purchased items and the correlation network, you will be able to determine which service is best used to sell a new group of services - one that has not been purchased before - to the clients you have selected.

Two major industries we work for

Banking and Finance

Compliance

• By analyzing transfer routes and cash transactions (withdrawls) through several nodes
• High-value transactions and money transfers can be effectively traced (chains and loops)

By identifying and analysing network patterns we are able to draw attention to transactions that are suspicious of being conducted for money-laundering or credit-card fraud purposes

Credit-domino: The monitoring of return credit installments

Through mapping and managing the risk associated with return credit installments, we project the characteristics of return credit installments onto the network of bank transactions. Then, with a multi-level view of connections in the network, we are able to model the long-term effects of possible difficulties around the payment of return credit installments. We can outline the so-called fold-chain; companies in a critical position can bring down others in financial liasion with them like a domino. Having projected return credit installments onto bank transactions we are able to list clients in need of credit insurance or liquidity management services.

Telecommunication

Mobile companies have data about thousand, millions of customers. Why not use this information to give personalized recommendations that make the probability of buying and customer loyalty higher?

A few important questions that we can answer for you:

• Which product is most likely to be bought next by customers who have already bought one of your products?
• Which customers have the greatest effect on the others in your customer network?
• Who are the opinion leaders?
• Which services should we offer in a package?
• Which services should we promote to the various customer groups?
• To which customer should we pay greater attention?
• How can we strengthen the loyalty of our customers?
• Which groups of customers are likely to leave us and use another mobile company?