A Decision Support System for Maximizing Brand Equity using Social Interaction Analysis

Peres Renana , HUJI, School of Business Administration, Marketing

Research-based tools for maximizing social interactions on a brand 



Social interactions among consumers, both offline and through social media, have proven to be a central driver in the success of brands. Firms and brand managers are constantly looking for ways to monitor these social interactions, enhance them, and understand how these interactions are translated into sales.

This task is far from being trivial. Social interactions are complex and dynamic. It is hard to know what makes a topic "catch" and create buzz, how this buzz can be influenced, and whether it creates an economic outcome. Currently brand managers rely on experience and intuition but lack solid, quantitative guidelines.




Over a number of years, data was compiled on the online and offline word of mouth occurrences of hundreds of leading US brands. Complex system modeling was employed together with statistical inference methods to answer the following questions:

  • What brand characteristics are associated with a high level of WOM occurrence?
  • Why do some brands generate plenty of buzz, while other brands, from the same product category, are less talked about?

The research identified a "DNA" backbone of 12 brand characteristics which have proven to influence social interactions on a brand. Using a set of algorithms, managers and consulting companies can be provided with a decision support tool that will enable them to develop their brand in order to maximize social interactions.


Our Innovation

A decision support system for creating talkable brands has been developed. The system combines state-of-the-art algorithms with a compilation of big data analysis. Using this system, brand managers can fine-tune perceived characteristics to maximize social interactions on their brand (offline and through social media.)


Key Features

  • A decision support system which provides guidelines to brand managers on how to create talkable brands. Knowing how brand characteristics influence WOM, brand managers can adjust these characteristics to enhance WOM online and offline.
  • The algorithms also make it possible to predict a brand’s average level of WOM based on its attributes and to diagnose whether its actual WOM fulfils its potential.
  • Until now, most WOM strategies were driven by intuition and gut feeling, and focused on promotion rather than the actual brand development. The current research provides brand managers with an actual quantitative understanding of how a brand’s characteristics affect its WOM, and marketers can see what characteristics to emphasize in online and offline channels to increase WOM.
  • The system has already been calibrated using a large training set of hundreds of US brands and millions of word-of-mouth messages both offline and on social media

Contact for more information:

Anna Pellivert
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