Business Analytics & Big Data Solutions

Transform Your Big Data Into Big Value For The Enterprise
Radiare believes in taking a holistic approach by bringing Technology, Business and Domain expertise in working towards building superior decision enabling solutions for the Enterprise. Our Business Analytics solutions takes a problem solving approach that helps Enterprises in improving the sales, controlling the costs, identifying the associated risks, optimizing internal measures among others. Further, the analytics services leverages the workings of a Digitial Enterprise by unravelling the hidden insights from both Structured Data and Unstructured data related to the Enterprise.
Business Consulting Services
Our SMEs and Data Scientists work with clients in problem identification and provide consultative approach towards solving business problems by leveraging the data (both structured and unstructured) in a holistic manner. The outcome of this exercise would be to deliver definitive outcome based analytical solutions that enables managers to make better decisions in their realm of work.
Customer Analytics Solutions
Our Customer Intelligence solutions are targeted at the complete Customer Life Cycle Management that addresses: Winning the customer, building customer stickiness, cross-selling and up-selling to the customer, managing customer churn and winning back from competitors. Our Customer Insight professionals combine both structured and unstructured data for delivering effective customer analytics to the enterprise.
Social Media Monitoring
Our point solutions built around Social Media Monitor that helps in understanding the social chatter among the Online social channels such as Facebook, Twitter, Blogs, Discussion Forums among others. It helps in identifying the Brand Perception, Competitive Intelligence, Campaign Tracking, Issue Tracking among others.
Forecasting And Predictive Modelling
Our forecasting solutions uses the past available data to determine future outcomes such as: How much inventory to keep?. The Predictive Analytics solutions looks for new trends in the data that helps in better understanding of customer behaviour that influences promotions and sales.
Model Management
Any good analytics solution stays good only if the underlying model stays relevant. With the continuous change in variables, the analytical models are kept updated on a regular basis by our team.
BIG DATA Solutions
Our team of data analysts works closely with the data that is available both inside the enterprise like the traditional ERPs and data relevant to the enterprise residing outside the enterprise like the social media to provide insightful solutions. The team has significant experitse in deriving insights from structured and unsructured data.
Success Stories
Analysis of Customer Satisfaction Index Scores
Project Description:
Client: India based Large Financial Services Company
Marketing Solution using Predictive Analytics
Project Description:
The predictive model solution involves:
- Churn Management: For the Churn Management, the solution built provides output in the form of churn probability scores for the subscribers. The Subscribers are sorted in descending order of churn scores to divide them into ten deciles (groups). The top 3 deciles capture 88% of churners. The model is refreshed every 7 days.
- Segmentation Management: In case of Segmentation management, the model output divides the subscribers into 6 clusters with subscribers within the same cluster having similar characteristics. The subscribers identified by the models are targeted through promotion campaigns. Effectiveness and efficacy of campaigns to be determined by having control groups
Potential business benefitted from the above exercise resulted in revenue increase of around USD 1.8 Million.
Client: Large India-Africa Telecom Service Provider
Demand Forecasting of Merchandise
Project Description:
The Sales data contains:
- Sales past data for variant SKUs
- Contains different number of sold SKUs in past
- Contains inventory product id
- Contains SKU id
- Contains color, brand, discount, dimensions columns to calculate color index, brand index, discount band
Demand calculation:
Demand is forecasted by taking sales value of product, counts of product sold earlier, brand index, discount band, price band, topic values (deal text data), seasonal index and color index. Most of these values are a value of strings but we converted them to numbers by using index using moving averages (in case we have sales data for all the 52 weeks for that brand or color).
Topic modeling and forecasting and validation:
Topic modeling is done by using text mining using tools like R, Pyhton and SAS using the text information from deal text data, like color, brand, shape, size etc. The solution looks at clustering similar words into different cluster (we call them topic1, topic2 etc), this is done by latent drichlette allocation.
The prediction window is for 7-15 days, the prediction monitored everyday using web based tableu account and the model is refreshed in every 3 months.
- The benefit of having a forecast model is to ensure higher customer satisfaction in terms of delivery time and availability of product.
- The forecast model also helps the company to ensure to have to correct amount of inventory at disposal.
- The liquidation cost is also reduced by a large amount and hence saves lot of cost.
Prediction of Propensity Score for Advt Clicks
Project Description:
Key challenge is the processing of click through rate data is difficult to perform on standalone system due to large volume of data. Also it leads to Increase in cost of infrastructure due to space usage and processing time.
The solution proposed include:
- Hadoop is used to distribute the big data across various systems connected
- Cloudera is used for managing the big data with integration of R and Hadoop
- R is used to as an analytical tool to build the business model based on CRISP-DM
The customer was greatly benefitted in terms of:
- Reduced costs as all the tools are open source
- Better efficiency due to increased processing of big data in real time
Propensity to Renewal ans Cross-sell Modeling
Project Description:
Radiare built Analytical Models for Propensity to Renewal and Propensity to Cross-sell that helped to Optimize campaigns and in identifying the most potential customers.
The work involved
- Identify modeling variables and model dataset development
- Data examination and preparation for rectifying anomalies
- Bivariate analysis for identifying important variables
- Logistic regression analysis for developing the final models
- Apply the models on new data for identifying potential customers
Social Media Intelligence
Project Description:
The Solution helped in :
- Monitoring Buzz/Online activity related to the Client’s brands and products.
- Identification of features likely to have an impact on choice
- Identification of possible online opinion leaders / influencers for targeted messages / updates on product features
- Structuring / Restructuring of market survey questionnaires based on buzz research
Client: India based Home Appliances Major