Tag Archives: reputation analysis

Reputation Analysis

Corporate Reputation Analysis at Scale

The rationale for Corporate Reputation Analysis Automation

Reputation

We live in an age where news stories are no longer a primary, but an added concern for businesses trying to build and maintain a strong reputation. Individual experiences are reaching a global audience in a matter of minutes, thanks to the internet, which has made way for an immense volume of spontaneous and real-time information. There is no doubt that companies have to navigate the voices of traditional and contemporary media sources, both of whom contribute significantly to their social standing. Reputation crises can spark at any moment, and traditional reputational audits can not help to mitigate them. Consequently, it is more important than ever before to keep track of reputation in real-time.

The MeaningCloud Corporate Reputation Analysis API is a new service that enables companies to take advantage of social networking platforms, forums, blogs, surveys, and news sources in a bid to do just that. While we have been running a service in this field since 2011, our new API employs a distinct approach to tackle the task of Corporate Reputation analysis.

Reputation management fundamentals

The MeaningCloud Reputation API uses a categorization scheme based on the work by Charles Fombrun and Cees van Riel. In 1999, they founded The Reputation Institute (now, The RepTrak Company™), the world’s leading reputation data and insights company. In collaboration with Harris Interactive, The Reputation Institute developed Reputation Quotient (RQ) in 1999, replaced in 2005 by the RepTrak® model.

The RepTrak Company™ has become highly esteemed on a global scale for its publication of reports on corporations’ reputation (as well as countries and cities). Furthermore, it has developed models that have provided companies with the autonomy to qualify their reputation in a meaningful way. It is important to note that other well-known frameworks do exist. The MERCO Indexes, for instance, are particularly renowned in Spain, Portugal, Italy, and across Latin America for their “multi-stakeholder methodology, composed of six evaluations and more than twenty information sources”. MERCO has been analyzing reputation since the year 2000.

To analyze Corporate Reputation in a piece of text, we follow the seven dimensions proposed in the foundational work of this field, globally considered to be the most important drivers of reputation:

  1. Citizenship:
    Both a company that actively strives and one who is idle in their efforts to be environmentally responsible, ensuring they support good causes and positively influence society, are equally susceptible to a change in their reputational outlook.
  2. Governance:
    A company needs to be open and transparent, ethical and fair in order to boost its reputation.
  3. Innovation:
    Innovative companies are market leaders and adapt quickly to change generally have a better social standing.
  4. Leadership:
    Suppose a company has inspiring/motivating leaders who display personal integrity, competence and knowledge, effective communication, and a clear vision for the future. They are likely to have a positive effect on the company’s repute.
  5. Performance:
    A measure of determining how profitable a company is, whether they exceed expectations and have solid prospects for the future. All of which are qualities of a reputable company.
  6. Products and Services:
    High-quality products/services that meet customer needs and are fully backed by the company undoubtedly contribute to a company’s social status.
  7. Workplace:
    A company must reward employees fairly, show genuine concern for them, and respect diversity to maintain a good reputation.

Our new Corporate Reputation API

The MeaningCloudReputation API is now available in 57 languages. The core processing is carried out in English. For other languages, the source text is translated first with the MeaningCloud Machine Translation API. For example, a user can throw an API request with a text in German. He/she can specify the source language (if known) as a parameter in the call or select “autodetect” to let our algorithms discover the source language, translate it into English (if needed), and carry out the reputation analysis on the translated text.

The MeaningCloud Corporate Reputation API goes beyond the automatic discovery of a mention related to a particular dimension in a Corporate Reputation model:

  1. It first detects the language of the text under analysis and translates it into English if needed.
  2. It disambiguates the mentions of entities in the text. Users can extend the (vast) resources updated daily in the platform with new companies via user dictionaries.
  3. It discriminates the reputation dimensions evoked in the text.
  4. It analyzes each dimension’s polarity (positive, negative, neutral, or non-existent) in a reputational context.
  5. It attaches the pair dimension/polarity to one of the entities mentioned in the text or to a declared default entity.

Reputation Management

Tools for PR, communication, marketing, and social listening companies

MeaningCloud is just a tech company specialized in Natural Language Processing. We do not collect information over the internet to assess the reputation of companies. Instead, we build the technology that permits third parties to develop and leverage NLP technology to provide actionable insights into:

  • The overall perception of the company.
  • The impact that news and social media content have on the company’s reputation.
  • The areas of the company requiring special attention.
  • The areas of the company making a good impression.
  • Third companies that are mentioned jointly and how their respective reputations compare.

The limits of the technology

As always with NLP solutions, and despite our continuous efforts, this API cannot be entirely free from errors or biases coming from different sources:

  • Our interpretation of the dimensions of reputation, that we know through the widely available literature on this subject.
  • Limitations in our algorithms for linguistic analysis and translation models.
  • Lack of adaptation to a particular domain or industry. Our general-purpose API can be improved to interpret reputational aspects more precisely when analyzing companies in a particular industry: utilities, finance, retail, telecom, healthcare, etc.

Our Corporate Reputation API derives from:

  • Our experience in customer feedback analysis, using the same basis as our Voice of the Customer analysis models.
  • The version of the API for Spanish, which has been public since 2012 (at textalytics.com until 2015 and at meaningcloud.com since then.)

Free Corporate Reputation Analysis

Our API is now published in beta version. For now, the pricing is the same as for other premium APIs: one request (or credit) is charged per 125 words or fraction. Two credits if translation is required. However, temporarily, there is no need to pay a flat fee for usage, as is the case for our Voice of the Customer/Employee APIs.

Follow this link to learn more about our Corporate Reputation API.

Furthermore, remember that you can test the API extensively, analyzing up to 20,000 texts for free per month, just by registering at meaningcloud.com.

Disclaimer

To be completely transparent about our credentials, we have to make it clear that:

Corporate Reputation ReviewReferences

An excellent source of information about this field is the journal Corporate Reputation Review. Launched in 1997, it publishes empirical and conceptual research on reputation management and closely related fields, such as strategic/corporate communication, corporate social responsibility (CSR) communication, corporate identity, and organizational identity.

Fombrun, C. and Shanley, M., 1990. What’s in a name? Reputation building and corporate strategy. Academy of management Journal, 33(2), pp.233-258.

Fombrun, C. and Van Riel, C., 1997. The reputational landscape. Corporate reputation review, pp.1-16.

Fombrun, C.J., Gardberg, N.A. and Sever, J.M., 2000. The Reputation Quotient SM: A multi-stakeholder measure of corporate reputation. Journal of brand management, 7(4), pp.241-255.

Fombrun, C.J., Van Riel, C.B. and Van Riel, C., 2004. Fame & fortune: How successful companies build winning reputations. FT press.

Ponzi, L.J., Fombrun, C.J. and Gardberg, N.A., 2011. RepTrak™ pulse: Conceptualizing and validating a short-form measure of corporate reputation. Corporate reputation review, 14(1), pp.15-35.

Fombrun, C.J., Ponzi, L.J. and Newburry, W., 2015. Stakeholder tracking and analysis: The RepTrak® system for measuring corporate reputation. Corporate reputation review, 18(1), pp.3-24.

Pallarés Renau, M. y López Font, L., 2017. Merco y RepTrak Pulse: Comparación cualitativa de atributos, variables y públicos, Icono 14, volumen 15 (2), pp. 190-219.

 

Janine Garcia, Nadine Shallow, Maria Jose Garcia, Concepcion Polo, and Jose Gonzalez

Active social listening to protect Online Reputation (Part 2)

EDITOR’S NOTE: This is a guest post by Leopoldo Martínez D., a researcher and consultant on social media corporate intelligence and lecturer at UCV and IESA (Venezuela), and it was originally published on his blog (in Spanish).

 

  1. Introduction

As I stated in the first part of this post, I will show how online reputation evaluation was used in a real situation related to the tourism industry.

  1. The unexpected event: A shooting at a music festival in Playa del Carmen, Riviera Maya

January 6-15, 2017, a music festival was scheduled to be held in Playa del Carmen, as well as a series of events related to both music and the tourism industry. On January 15, a shooting occurred in a well-known bar where people were celebrating the end of the festival.

When the shooting happened, messages quickly spread through social networks to give information and comment on the context in which the incident and how it happened. Some conversations revealed an interesting fact: The shooting was not an isolated event but stemmed from the “situation of crime that the Riviera Maya went through in 2011″.

Could this affect the Riviera Maya’s reputation as a tourist destination? Could “several years in a situation of crime” have already influenced the tourism industry’s image? These are some of the questions that the public and private actors that provide services and products in this tourist area might have been asking themselves.

Continue reading →

Active social listening to protect Online Reputation (Part 1)

EDITOR’S NOTE: This is a guest post by Leopoldo Martínez D., a researcher, consultant on social media corporate intelligence and lecturer at UCV and IESA (Venezuela), and it was originally published on his blog (in Spanish).

 

1. Introduction

In this previous post, I suggested that conversations taking place in virtual communities fostered by a digital marketing plan generate feedback that is useful for assessing and monitoring a digital’s marketing strategy’s performance.

This feedback could generate a huge amount of valuable data (Big Data) which enables the creation of a knowledge base for the topic being talked about, who is participating, who is having the greatest impact on brand image, products, people, or organizations.

This knowledge base can also be fed by discussions arising from unexpected events which are not part of the communication plan but deal with the virtual community’s topics of interest.

To specifically assess the conversation’s impact, it is necessary to pay attention (beyond listening) to what is being said through metrics (qualitative and quantitative) that reflect the online community’s perception on brands, products, people, or organizations. After all, this perception is a way to measure an online reputation.

With this need in mind, the purpose of this post is to show how to use the active listening of conversations in social networks to evaluate your online reputation.
Continue reading →

Monitor corporate reputation with our new API

Do you need to understand the impact that social media and news have on your corporate reputation (or your customers’)? Now, a new MeaningCloud API enables you to analyze all that information in real time and structure it according to the dimensions of the most common reputational schemes.

Some customers have been demanding us a precise way of understanding the impact that opinions from social media and other channels have on a company.

Corporate Reputation APISocial monitoring tools provide a basic sentiment analysis that in the best of cases identifies that a certain comment (e.g. a tweet) expresses a positive or negative opinion about an entity, and use the aggregated data in diagrams and temporal evolutions. Nevertheless, analyzing a so multifaceted reality as the reputation of a company requires a more granular opinion analysis.

Although it is usually identified with online reputation, corporate reputation is a different concept: it consists of an aggregate of the opinions and perceptions that the public has about a certain company. And it’s a multidimensional characteristic, since those opinions are assessed around a series of more or less standard axes:  Financial situation, Product quality, Innovation, Strategy…

A more social, real-time reputation analysis

So far, corporate reputation has been measured by interviewing and conducting surveys to customers and analysts, and it has been provided in the form of static periodic reports. But the number of customers who are not satisfied with a “snapshot” a posteriori -based on a few opinions- is constantly increasing. Now they wish to add to the picture the impact that social and traditional media have on their reputation, and access that information in a more up-to-date and actionable way to detect potential reputational crises well in advance.

With this MeaningCloud update we have added a new Corporate Reputation API, which enables to include in the reputational analysis the huge amount of spontaneous opinions expressed in all kinds of media (social networks, forums, blogs, news websites) in real time.

New corporate reputation API

This API performs a reputational tagging of text: it receives a document (tweet, piece of news, comment in a survey), detects the mentioned companies, identifies the reputational dimensions involved and extracts the polarity that affects each one of them. All this with a high level of accuracy and granularity, distinguishing opinions about different companies than can coexist in a single sentence. The result is presented in the form of standard tags that can be used to aggregate, relate, detect trends, generate alerts, etc.

Under the hood, the corporate reputation API uses a highly optimized pipeline that incorporates information extraction, automatic classification and polarity analysis techniques. In addition, it is based on standard interfaces and features SDKs that enable to integrate it into any available monitoring application or tool. More information here.

Using this API you can complement the traditional reputational studies with a more agile and immediate analysis of the impact of social media, news, etc., and thus manage more dynamically such an important asset of your company.

And don’t forget that with this MeaningCloud update we include two APIs from our previous product, Textalytics: Linguistic Analysis and Text Proofreading.

Use it for free