Factor analysis can help summarize the information captured by this large number of correlated habits and preferences into a more manageable number of factors, which can then be used as inputs to cluster and discriminant analysis.
Financial analytics modeling requires the integration and assessment of a broad spectrum of data that may potentially affect a company.
Some of the data the analytics may take into account include; which customers provide the company with the most profit; how the company’s customer-base is spread out geographically, and which product brings in the most profit. Once the data has been collected, it can be displayed in charts or graphs so that complex and diverse information can be easily visualized.
Predictive analytics is an analyst-guided (not automatic) discipline that uses data patterns to make forward-looking predictions, or to make complex statements about customers by evaluating multiple data patterns. Data mining searches for clues and predictive analytics delivers answers that guide you to a “what next” action. Data mining is often the initial stage in developing a predictive model.
This solution allows our clients to predict the health of their assets using certain variables already collected though their inventory process. We combine notions of age, location, make, model and other information to accurately predict the heath of certain assets and can integrate with our clients work ordering system to give them a proactive approach to asset maintenance.
The cost of fixing an asset when it has already failed is significantly higher than the cost of proactively taking action to avoid that failure. b3Intelligence’s Asset Intelligence solution is designed to facilitate that proactivity. Using general linear modeling and predictive analytics we are able to help operations personnel diagnose problems, and enable rapid remedial action to issues that occur.
We used transactional, service profiles and demographic (anonymous) data from the client’s database to uncover segments, which varied in terms of these variables via cluster analysis. An algorithm was developed to “score” the respondents by using discriminant analysis.
The client has successfully used the algorithm to not only track how their segment mix is changing over time but also modifying their service offerings to appeal to these segments for maximal impact.
- Identification of information needs
• Acquisition and creation of information
• Analysis and interpretation of information
• Organization and storage of information
• Information access and dissemination
• Information use
The benefits from managing information strategically are centered on key standard objectives such as how to reduce costs; reduce uncertainty or risks; add value to existing products or services; or create additional value through new information-based products or services. b3Intelligence looks at all these factors when taking on your information management needs.
b3Intelligence also studies medical practitioners from close quarters as part of pharmaceutical research. This helps in identifying their prescription practices, the way they subscribe medicines, and how the drugs get a market.
For example, b3Intelligence’s Affinity Analysis can provide medicine and drug makers a road map on how to position their products, find new sales channels and reduce R&D costs.
This method is key to execute for companies in the pharmaceutical industry, as consumers are very cautious about the drugs they purchase and concerns about product combinations, chemical concentration, and other preferences.
b3Intelligence delivers the insights that the pharmaceuticals can leverage to develop the product mix that consumers favor.
Rather than using cell weighting, which would be practically impossible when dealing with multiple variables in their entire granularity, we used propensity-weighting schemes to balance the groups.
These multivariate weighting techniques generally employ logistic regression models to generate the weighting schema.
This analysis showed our client their position relative to the competitors as well as the ideal point in terms of the market perceptions. This analysis also helped address the issue whether they should invest in the additional feature, which they thought would bring their product closer to the market ideal – the answer in this case was no.
The insights revealed that price adjustments rather than investing in an additional feature was the optimal option.