data mining

Who is the actual human we are targeting?

We begin by identifying every single person who is online. We do this by proving which social handles are connected to real people and which are simply junk accounts. The way to prove if a social handle is connected to a real person is to append extra data next to a social handle, such as other social handles, emails, addresses, phone numbers, the social handles of family members, job titles/bios from social sites (LinkedIn, Facebook, Twitter, Instagram, Vimeo). This is important because we want to analyze real human beings and their behaviors. The reason we want to study real people is to make accurate predictions about human behavior and the outcomes of behavior in different contexts. When we can predict behavior more accurately, then we are able to influence individuals, which is one of the ultimate end games of marketing, advertising and pubic relations.

When we have a healthy set of columns with this kind of information, then we can move on to using tools that study all of the content posted by an individual, as well as the way the individual describes himself/herself in bios on various sites. We can also study posts about individuals, some of which may include video footage, images, interviews, and sites that denote achievement. This set of software studies all of this content about an individual and classifies him/her as a specific persona type, along with the interests this individual focuses on. This is important because we want to know more about the type of person each individual is and what influences his/her behavior.

The next set of software we use derives insight from offline data, such as the data from credit bureaus, credit card companies, direct marketing companies, catalogue marketing companies, club membership research, background checks, etc. When we blend offline data with online data, we are able to demonstrate with more confidence the brand preferences, purchasing-buying styles, and many other classifiers related to an individual. This is important because online conversations, posts, and self-description in bios do not always give us enough to deeply understand the behavior of individuals. The blending of offline and online data results in a more complete portrait of the individual human being.

In short, we are able to more precisely influence what an individual human being will do in the future. For a brand, this long-term influence is very important as this will be how loyalty and sales are ensured. There are, of course, deeper goals for other organizations, such as governments, religious groups and media groups. These groups are often interested in wholesale culture change, particularly in enemy/competitive territory. The action of changing another culture is a top long term priority of groups that have been around far longer than the Unilevers and P&Gs of the world.

Customer Intelligence in a real-time social eco-system

The pressing need in customer intelligence is a solution related to a real-time eco-system. One deeper philosophical issue in this space relates to variable velocity, to quote Lee Bryant. Tools like PeerIndex and Recorded Future do a good job of segmenting and defining the truly staggering flow of data AND audiences interacting with that data. Datasift ( is a leader in architecting methodologies for analyzing the complex fabric of the social Internet.

For an important new study on Customer Intelligence Trends 2011, see the following Forrester Report: ‎”At the same time, the demand for insight — not just data — in real time creates a challenge but also a huge opportunity to extend the value of Customer Intelligence throughout the enterprise. Leading CI professionals who evolve and adapt to these trends will quickly find themselves at the nexus of the business.” ~from Customer Intelligence Trends To Watch In 2011 (

(Some basic info below)

Business intelligence (BI) refers to computer-based techniques used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or departments or associated costs and incomes.

BI technologies provide historical, current, and predictive views of business operations. Common functions of Business Intelligence technologies are reporting, online analytical processing, analytics, data mining, business performance management, benchmarking, text mining, and predictive analytics.

Business Intelligence often aims to support better business decision-making. Thus a BI system can be called a decision support system (DSS). Though the term business intelligence is often used as a synonym for competitive intelligence, because they both support decision making, BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence is done by gathering, analyzing and disseminating information with or without support from technology and applications, and focuses on all-source information and data (unstructured or structured), mostly external, but also internal to a company, to support decision making. (SOURCE)

The global business intelligence (BI) software market is projected to reach $12.4 billion by the year 2015, driven by the growing need to empower all stakeholders of businesses with right information at the appropriate time.

Uncertain economic conditions, intense competition and increasing volumes of organizational information are forcing enterprises to seek efficient means of deriving value from information for improving the overall efficiency of business processes. In this regard, BI technology is emerging as an essential tool for identifying new revenue-generation opportunities as well as to control unproductive expenditures. BI offers tools, processes and applications for facilitating organizations to analyze and consolidate data gathered from various sources for optimizing operational performance and for improving business decision-making. BI and analytics software helps organizations to analyze the information built up over the years, which resides in the enterprise systems. (Source)