Coolfarming Ideas through Knowledge Flow Optimization - Boosting Organizational Performance through E-Mail Social Network Analysis

Over the last fifteen years our research group at the MIT Center for Collective Intelligence, University of Cologne and University of Applied Sciences Northwestern Switzerland (FHNW) has studied hundreds of organizations through the lens of their social networks, extracted from the organization’s e-mail archive.

Among many others we have studied R&D organizations at car manufacturers, marketing departments at banks, sales teams at high tech manufacturers, medical researchers and doctors at large hospitals, and service delivery teams at large consulting and service provider firms. In addition, we have also looked at collaboration in open source organizations like Eclipse software developers, Wikipedians, and online communities on Facebook and elsewhere.

We have developed a 4-Step Process which we call “Knowledge Flow Optimization” to study and increase the performance of organizations, to “coolfarm ideas” (see figure below).

It consists of the four steps “Analyze – Predict – Mirror – Optimize”. To illustrate our approach, I describe the analysis of a fortune 500 high-tech company, where we compared e-mail communication of the organization with sales success of their sales teams in the different geographical regions.

Step 1: Determining Social Network Metrics and Communication Patterns

In the first step we analyze and quantify the communication patterns and social network structure embedded within organizational communication archives such as email, video conferencing and instant messaging. Quantified communication patterns include metrics such as average response time to messages, sentiment and contribution index. Contribution index is a measure of the balance of communication in terms of the messages sent and received by an individual. These are complemented by metrics computed using Social Network Analysis that are measures of social influence (or centrality) and their trends over time.

Step 2: Honest Signals: Comparing structural attributes with business success

In the second step we compare communication behavior found in step 1 with communication patterns that we have identified over a period of 12 years in over a hundred ONA projects carried out by our team. These patterns, also called “honest signals”, are indicators of better connectivity, interactivity and sharing among the individuals in the network. There are 6 honest signals that we look for, namely: Central Leaders, Rotating Leadership, Balanced Contribution, Rapid Response, Honest Sentiment and Innovative Language. Having calculated these honest signals from the data in the communication archives, we then correlate them with quantified success and failure criteria. The success and failure criteria vary significantly depending on the type of organization, the industry and the individuals being measured. In this example we measured sales performance of the sales teams in different geographic regions and for different products.

Step 3: Virtual Mirroring

In the next step we mirror the communication behavior we have identified for the different parts of the organization back to the teams and individuals. By showing them how they differ from the best practices we found in past projects, we help them to improve their behavior for better performance. Just like with a real mirror, looking at how a team “really” communicates can be an eye-opening experience for the team members, leading to fundamental changes in their behavior for the better.

Step 4: Devising a plan to optimize communication for greater success

Once we figure out which of the honest signals are correlated with success and failure, we developed a roadmap for the company to change sales communication behaviors leading to more successfully closed deals and more satisfied customers.

Why it is different?

When we do our analysis, we frequently get asked about the details of our method. There are a few key principles that I would like to point out that set our approach apart from other e-mail based and SNA approaches

Measuring “True Creativity” – our framework is based on the notion of COINs (Collaborative Innovation Networks). It has been field-tested in over hundred organizations to identify the communication patterns indicative of creativity. This includes far more than simply counting the number of e-mails of individuals and teams, rather, using the six honest signals of creativity listed above we have identified complex networking patterns of true creativity.

Know Cool People, and not just Hotspots – our semantic social network analysis tool Condor finds trends by finding the trendsetters. In the first step it works like Google, to identify who is using novel words and ideas, but then it finds the cool people, by measuring who uses novel words first, and how quickly they are picked up by others to grow into new COINs.

Anti gaming  - We are using social network analysis metrics such as “betweenness centrality” and time series of e-mail exchange, which are far more robust towards “gaming” by employees than simply counting e-mails sent and received.

Measuring organizational trust and satisfaction - We are not just counting complex words, to measure complexity in dialogue, and counting positive and negative words such as “great”, “wonderful”, “horrible”, “awful”, but through machine learning algorithms track word distribution and model positivity and negativity in context.

Understanding communication galaxies – we track the evolution of network positions of people, measuring how individuals change from being “stars” to becoming “galaxies”, as the most creative people and most highly functioning teams act as communication galaxies embedded into clusters of other teams.

Using E-Mail based Social Network Analysis gives an organization an unprecedented view into the nervous system of the organization, and allows it to predict flash points before they happen, leading to greatly improved performance and reduction in risk.