Consumer Sentiment Analysis
As the consumers are spending more and more time on the various social networks, we are experiencing an explosive growth in the consumer generated content in this digital ecosystem. As a result, more businesses are spreading their footprints in the social arena and trying to communicate with their existing and prospective audience. As the interaction between audience and brand is increasing day by day, it’s becoming quite challenging for the businesses to interpret and act on this wealth of unstructured data.
Here are a few standard questions we have been getting from our clients lately:
- What is the reputation of my brand across different digital properties?
- What kind of frustrations or challenges our audience is facing with regards to our service offering?
- What attributes of the product are negatively impacting our sales?
- What is the impact of the negative sentiments on the brand image?
- Can we group our social audience in meaningful clusters based on their perception about our brand and services?
- Can we identify interesting topics for future posts and tweets?
- How can we leverage the negative sentiments of the consumers and optimize our online storefront?
- How can we merge the social sentiments data with online survey and reviews & ratings data and get a better understanding of consumer needs and challenges?
Nabler’s approach for consumer sentiment analysis
Benefits of Nabler’s consumer sentiment analysis services
- Our Predictive Modeling team is proficient with both open source and enterprise level statistical packages in the industry, i.e., “R”, “SPSS” and “Python”.
- We have consulted various B2B and B2C clients in Text Mining and Sentiment Analysis initiatives, and our team is quite well-versed with Machine Learning Models like Decision Trees, Linear Regression, Bayesian Network, and Hidden Markov.
- Our team has also leveraged the out-of-the-box Sentiment Analysis APIs under R and Python.
- We can take both Predictive and Non-Predictive approaches to tackle the Sentiment Analysis challenge.
- We assist you in defining a new communication strategy based on the outcome of the Predictive Modeling exercise.
- We can pull unstructured data from various sources and store it in a cloud-based Datawarehouse for cross-channel sentiment analysis.
- Dedicated Predictive Analytics team: At Nabler, we have a dedicated predictive analytics team which comprises of statisticians, data modelers, behavioral science experts, and business domain experts. Our core team can provide end-to-end consulting service ranging from conceptualizing the business problem, data profiling, exploratory analysis, cohort analysis, variable reduction, model estimation, model creation, and model validation.
- Experience across different industry verticals: Our predictive analytics team has worked with both lead generation and ecommerce businesses across different industry verticals including Telecommunication, Retail, Finance, Legal, Automotive, Insurance, and more.
- Dedicated Software Services Team: Our software services team complements our predictive analytics team and assists in extracting data from multiple sources and building a customized cloud based Datawarehouse. Besides this our software services team assists our predictive analytics team in data cleansing and adding a visualization layer on top of the Datawarehouse using QlikView and Tableau.
- Dedicated Consulting Services Team: Our consulting services team provides business context while building predictive models, and is proficient with test based optimizations, behavioral targeting, personalization, and usability studies. Besides this, the team plays a vital role during implementation and upgrade initiatives.