LONDON--(BUSINESS WIRE)--A global data analytics and advisory firm, Quantzig, that delivers actionable analytics solutions to resolve complex business problems has announced the completion of their latest sentiment analysis engagement for a leading food service firm. During the course of this engagement, the analytics experts at Quantzig helped the client to address their challenges by adopting a holistic approach to sentiment analysis.
Sentiment analysis has grown from being a simple tool for collecting customer perceptions to an opportunity to gain business critical insights and make quick and effective decisions. By augmenting sentiment analysis with predictive capabilities, businesses can accurately forecast what their customers are likely to do. Sentiment analysis involves the use of regression models and advanced analytics techniques to provide a holistic view of customer perceptions and their future actions based on transactional, social-media, and other relevant data.
The Business Problem: The client is a Fortune 500 US food service industry player who recently transitioned from a brick and mortar store to a mainstream, online business model. A compelling opportunity to leverage customer data to answer strategic questions about their services and business performance is what made them approach Quantzig. The client wanted to leverage sentiment analysis to gauge customer sentiments and most importantly gain a better understanding of the potential opportunities for improvement.
Our expertise in the delivery of sentiment analysis solutions can help you better understand various customer needs. Get in touch with our experts for more insights.
“Sentiment analysis coupled with advanced analytics can help organizations analyze and quickly draw inferences from burgeoning unstructured datasets obtained from social media and other sources, and convert it into meaningful insights,” says a sentiment analysis expert from Quantzig.
The Solution Offered: To help the client address their core business challenges, the sentiment analysis experts at Quantzig developed a holistic approach to sentiment analysis, which was segmented into three phases. The initial phase involved a detailed review of the organization’s online presence to identify the different online channels where they were being discussed. Also, by leveraging sentiment tracking and advanced analytics the client was able to analyze unstructured datasets and categorize customer sentiments.
The use of sentiment analysis models can help businesses to comprehend huge volumes of unstructured data and gain detailed insights into customer perceptions. This success story is a classic example of our sentiment analysis solution helped a client to optimize their marketing strategy. Request a free proposal to know how sentiment analysis can help you gain a leading edge in the market.
Quantzig's sentiment analysis solutions helped the client to:
- Adjust their high-level messaging and marketing campaigns to meet customer needs.
- Gain in-depth insights into customer perceptions and their online presence.
- Request a free demo to know how you can leverage sentiment analysis models to address your business needs.
Quantzig's sentiment analysis solutions offered predictive insights on:
- Developing suitable marketing strategies to counter the decline in sales
- Leveraging the use of statistical modeling techniques to access real-time customer sentiments.
- Sentiment analysis can open up a world of new opportunities for your organization too. Want to know how? You're just a click away! Request for more information now!
About Quantzig
Quantzig is a global analytics and advisory firm with offices in the US, UK, Canada, China, and India. For more than 15 years, we have assisted our clients across the globe with end-to-end data modeling capabilities to leverage analytics for prudent decision making. Today, our firm consists of 120+ clients, including 45 Fortune 500 companies. For more information on our engagement policies and pricing plans, visit: https://www.quantzig.com/request-for-proposal
