Our Methods
In the world of big-data, you can no longer afford to rely on sentiment analysis and media metrics alone.
As companies are all looking for better ways to reach and persuade those who matter most, the reality is that persuasion rarely works. Instead, a much more powerful approach is to lead with facts followed by action.
With deep insights from Dartmouth scientists – the same mathematical models used to isolate which attributes among species project to survival and extinction – now, for the first time, are being applied to corporate data sets.
We make it easy for reputation teams to isolate the reputational attributes that impact performance and drive the greatest financial risk to their company, resulting in more influence internally and externally.
Your reputation
1) Rank & Measure
Published reputation measures like Harris RQ, BrandZ, Most Admired, Future Brand, RepTrak, and others, all capture some element of reputation: whether public perception, C-suite perspective, financial performance, or customer sentiment. Our research led us to use a meta-analytic approach to blend the best of these and dozens of other metrics into a single holistic score that reflects the sentiments and values from your most important stakeholders.
The Data Pipeline
2) A Multivariate Approach To Improve Stakeholder Trust
10 years of data are held in cubic matrices with thousands of companies and millions of data points from various constituencies.
We have spent years aggregating data from various sources: 10K reports, SEC reports, proprietary data partners, licensed data, news articles, social media, press releases, screen scraping. This data-set represents one of the largest sources of quantitative insight in the world.
The Analysis
3) Gap Analysis, Perception vs. Reality
Positioning qualitative data on sentiment and reputation against quantitative data on the organization highlights gaps between perception and reality. In an ideal world, authenticity drives alignment
The Analysis
4) Attribute Discovery
The world of big data is upon us and Artificial Intelligence cannot compete with algorithms designed to handle enormous quantities of information.
The Analysis
5) Adaptive Landscapes
Evolutionary Biology has produced the most accurate mathematical models for understanding adaptation. Knowing how your competitive environment dictates which attributes should be prioritized will help optimize resources and improve efficiency.
We use adaptive landscape modeling to understand how interactions among attributes lead to variation in performance. On the rugged landscape shown here, attribute combinations that move the client uphill on the landscape are brought into focus. The right balance among key attributes will help you emulate the best competitors in your industry.
The Solution
6) Adapt
Having discovered the set of attributes that differentiate the most purpose driven companies in the world, MAHA performs a competitive analysis of your performance against your key competitors to show you where the most efficient and effective changes can be made.
Scenario Planning
7) The Plan Ahead
The MAHA data set provides opportunities for scenario planning using another set of adaptation algorithms from our evolutionary scientists.
Understanding the relationships among attributes and how past events have shaped success and failure, we can model alternative actions to predict how these might influence stakeholder satisfaction, corporate reputation, and even financial success.
8) 1st & 3rd party data
We incorporate your data to provide the most granular picture of how our results can influence your key metrics. Together, this data incorporation provides a unique evidence-based approach to track how our recommendations are positively impacting your business.