In this increasingly interconnected and competitive market scenario for companies, the brand reputation is a strategic asset that determines their success or failure.
The reputation of a company is not only related to its direct activities, but also to the ones carried out by counterparties.
Nowadays, being able to evaluate your business partners, distributors, resellers, agents, etc. is a necessity to protect the company’s results, values and reputation: intelligent monitoring and sentiment analysis can help you spot any risk with accurate and timely analysis.
However, the main solutions on the market only collect data from social media, in order to basically help the marketing team monitor the direct perception of the company, disregarding the “counterparts”.
New alternative data sources on the web enhance the risk analysis and create new synergies with the business.
Why you need sentiment analysis: advantages and applications
Through ADI (Alternative Data Intelligence) you can collect data from different online sources – news on specialized magazines, branded news, reviews, publications, comments on social media, etc. – to detect sentiment for your specific purpose, according to your needs.
This methodology has many applications. Mainly you can:
- monitor the competition and follow industry trends, to be always up-to-date and in step with the market;
- minimize exposure to distribution counterparty risk, solving a typical problem for big brands in the retail world and their products’ distribution in multi-brand physical or online stores;
- monitor the ESG risks of companies for investment purposes.
Sentiment analysis and AI-based risk monitoring: 3 success cases
For Aramix, these three main applications turned into three success stories in three different industries.
Natural Language Processing: AI for brand reputation
In the first case, one of the world’s largest manufacturers and distributors of transportation equipment was interested in mapping industry news to identify emerging trends and the perceived sentiment of current and potential customers regarding their services.
In particular, they wanted to monitor their competitors and check the general health of the business through its online reputation.
After a timely data collection, Artificial Intelligence helped understand the main topics, analyze, segment and classify the news, thanks to our proprietary algorithms based on Natural Language Processing. The output was a queryable dashboard and a weekly summary newsletter for a “reasoned press review” of the brand.
3rdEye to see the invisible pattern beyond data to understand the risk for third party distributors
In the second case, a top player in the consumer electronics industry needed to minimize the risk related to third party distributors, monitoring news and reviews about them, in order to support risk managers in identifying critical issues that involved specific partners and points of sale.
Our 3rdEye solution is a true Natural Language Processing framework for the analysis of alternative data. It is based on a proprietary algorithm to process large volumes of information about each store, in particular news and customer reviews, to identify risk signals on a daily basis.
There are 4 levels of analysis:
- news filter: in scope / not in scope
- classification of contents into categories of interest
- sentiment analysis of users who buy in stores divided by single category of interest
- proprietary scores calculated for every reseller, highlighting critical issues and specific opportunities
3rdEye provided a daily ranking of the risk associated with each retailer and shop, along with alerts about critical events detected in real time.
It also guarantees total transparency and security for the client and users: in fact, 3rdEye is registered according to the TULPS (Italian Consolidated Text of Public Safety Laws) to ethically collect and analyze online data.
Sentiment analysis and ESG monitoring to invest on listed companies
In the third case, an Italian company operating in asset manager and equity research needed to constantly monitor ESG risks relating to the companies in its portfolio, preparing a quarterly report on their ESG performance.
We developed a custom project based on a proprietary ESG framework and the daily collection of news data from different web sources to:
- alert clients to specific ESG risks related to their investments according to a sentiment score
- provide clients with news that have a greater impact on reputation
- perform ESG analysis by combining internal and external data
Our customer could monitor ESG risks and corporate reputational critical issues on an intuitive dashboard and a quarterly detailed report, combining ESG performance with the risks related to the news.
