Across industries, companies are taking advantage of data resources and analytics capabilities to cut costs and target customers more effectively. In the consumer goods sector, companies have started to see how big data can be used to find new visions that drive consumer goods business.
We believe that market research, operations excellence and analytics cannot work in silo for any large consumer business. As the digital world becomes more complex, leadership teams become increasingly dependent on data and analytical methods to guide actions and decisions.
Business Brio has worked for one of the largest consumer goods organizations to understand the demand, feedback and the competitive perception of their brand. This was accomplished through advance semantic algorithms for social media feeds. The back end sentiment analytics algorithm was then built into an IT system with adaptive front end enabling desktop as well mobile based tablet screens for quick decision making.
At Business Brio, we have used Single Exponential Smooth, Double Exponential Smooth, ARIMA and back-propagation neural network. We did find Neural network to have better forecasting performance than the classical forecasting algorithms in case of wind energy forecasting for a particular project and won the NASSCOM Analytics Innovation award in 2015 for effectively using the same for business.
Methods like Goal Node, Integer Linear Program, Simplex Method and Interior Point Method are used depending on the context and relevance. At Business Brio we use Lindo as a tool for optimization.
Unsupervised and supervised learning methods like regression, support vector machines (SVM),KNN, K-means, PCA are used to recognize patterns and make data-driven predictions or decision outputs. We extensively use Python and R for applying the algorithms.
CART, CHAID, Random Forests, mathematical and computational techniques are used to aid the categorization and classification of a given data information. Apart from programming tools, we also use WEKA for decision trees.
Opinion mining or emotion AI refers to the use of natural language processing, text analysis, and computational linguistics to systematically identify, extract, quantify, and study affective states and subjective information. In past projects, we have heavily used LSE, HSA, text mining for semantic algorithms.
Social Media Analytics
Forecasting Demand
Campaign Performance
Customer Value Analytics
Marketing Channel Effectiveness
Applications
Leverage your data to comprehend customer dynamics and increase revenues
Scalable Solutions
Process Study
Success Criteria
Engagement Model
Product Agnostic
Adaptive Dashboards
Tuned Models