peer analytics

Yep! You got it. The answer is Jack and Jill because they texted more frequently over a longer period of time.

You see, by using simple information like all that stuff you’ve probably seen on your parents’ phone bill, we can identify who’s better friends and who is most popular out of those friends. Once you know that, companies are better able to form relationships with people who want to buy what they sell.

Information spreads naturally through friends. Social influencer (people with frequent back and forth communication with many friends/groups of friends) spread information further and with more impact. When information is shared by an influencer, it is seen by their friends as evocative, trustworthy, and sticky.

By running your communication data through our intelligent algorithm, we find communication patterns within social groups, identify key influencers, and predict emerging social groups. Using that data, you can direct key messages to users of each group.

It is said rather famously that When a butterfly flaps its wings in one part of the world it may lead to a hurricane occurring in another part of the world, via non-linear 'knock on' effects.

The "non-linear 'knock on' effects" contained in this quote from Chaos theory, underpin how our software works as a very unique and powerful analytics engine.

It comprises a set of integrated algorithms for the processing of P2P data (or M2M, etc) which make possible the identification of smallish and tightly-knit peer groups in social network analysis (SNA) or similar datasets that otherwise remain buried and not able to be found any other way.

These peer groups are natural forming hotspots of peer pressure that can be leveraged to lift the response rate of marketing messages when the messages are seeded appropriately into a customer/subscriber base over time.

Our Machine Learning algorithms predicatively model key dynamical features and target points and thereby ensure the effective and accurate guidance of the progressively targeted seeding of a client’s marketing message – thereby maximising peer group pressures toward behavioral conformity and the client’s marketing ROI.

Our methods employ the principles of Complex Systems theory and Social Network Analysis (SNA) in the unique algorithms that we have developed and proven over many years for the dynamical modelling of the independency of activities and metrics development of ‘big data’ network properties.