LatentView can help you unlock the potential of your customer data by using analytics to discern complex patterns of customer behaviour, and predict the best possible course of action at every step in your interactions with your customers. Irrespective of where you are on the Analytics adoption curve, we can help you exploit its power for effective decision making.

Model Development
LatentView's end to end expertise in predictive analytics enables you to create and deploy high quality, highly accurate applications that address your unique business needs:

• Expertise spanning a variety of data mining techniques - including Multivariate Regression Models, Clustering Algorithms, Neural Networks & Decision Trees
  


• A Best Practices-based approach to delivery built on industry standard processes (like CRISP-DM) for reduced risk
  


• Deep domain expertise in a variety of industries, including Financial Services, Insurance, Retail & Telecommunications
  


• Flexible Engagement Model with Proven delivery capabilities, including robust processes to ensure confidentiality of your data and quality of deliverables
  

Making Models Actionable
Models are useful only when they are utilized in everyday decision making process.

For example, a credit scoring model might generate output in the form of scores ranging from 100 - 1000 that indicate the risk of default for an account. To make effective decisions, managers need to devise appropriate ways of response for the underwriting teams to different ranges of scores. In this example, respondents with a score of more than 700 can be given automatic approval of loans, whereas those with a score of less than 200 can be automatically rejected.

LatentView can help make your models actionable by:

• Helping your business managers interpret the model output in terms of business outcomes, ensuring increased end-user adoption
  


• Assisting in formulating appropriate strategies and codifying them into business rules. This will enable your employees to utilize the results of your model in day to day decision making
  


• Champion / challenger contests to systematically test current strategies against alternatives yielding empirical results that can help you refine your strategies
  

Evaluating Model Performance
Models need to be monitored and tracked to evaluate any changes in factors that require updation of the model.

For example, in a consumer lending scenario, changes in competitor marketing strategies or economic conditions can have an impact on the profile of prospects applying for a loan. Monitoring is concerned with measuring and reacting to such changes, potentially including additional factors in the model.

Model Tracking is used to assess whether model predictions are coming true. For example, if a credit scoring model is used to evaluate credit risk, model tracking can help determine the performance of the model in ranking risk properly. The payment behavior of customers scored by the model can be observed to determine the accuracy and precision of the credit scores.

LatentView can help create processes to track and monitor the results of the predictive models over a period of time. Regular tracking and monitoring can provide insights into model performance and can be used to enhance the models or make decisions regarding development of new models.

Data Management
Data management involves processing of data into formats that are highly efficient for the development of models and for ad-hoc reporting. Our data management methodology encompasses the following activities as components of building predictive modeling solutions:

• Assimilate data from multiple sources - transactional, credit bureau, demographic, point of sale or other organizational / syndicated data sources.
• Identify important elements - dependent and independent variables, predictive elements
• Data Discovery - understand the quality and characteristics of data, perform statistical data analysis and discover relationships