SOLUTIONS
Even in the same industry, solutions to the same business challenge can take different forms. Cambio does not mandate a customer fit in to our solutions, but rather, we provide custom solutions based on the Client’s individual goals, parameters and nuances of the particular challenge. You can rest assured with a starting point of a complimentary strategy discussion, you will at the very least get expert advice and if you choose, a total solution that may include hands on implementation. We believe in teaching, not just talking or doing it for you. Here are some examples of how analytics solves business challenges:
Some Quick Examples of How Analytics Solves Business Problems
| Problem: | An automaker has a manual rules based warranty processing system relying heavily on static rules which did not include sophisticated indicators for fraud and which were a significant burden of time, resources and investment. |
| Solution: | By analyzing millions of historical data records, advanced modeling techniques were applied to develop sets of new rules, which made the process more automated, improved accuracy and reduced staffing, providing extreme return on investment. |
| Problem: | Our analysts spend 80% of their time marshalling and scrubbing data just to prepare it for analysis. This is an inefficient use of a very valuable and costly resource. |
| Solution: | The right analytics platform can provide a simple way of "operationalizing" analytics. Software solutions can be used so that the data management tasks that used to take so much time can now be practically automated. |
| Problem: | I am a business manager and am using a tremendous amount of time managing data in Excel just to manipulate it and even then, can not make connective evaluations. I can get statistics, but cannot use it to do actual evaluations. This takes me a lot of time and even then, it is not all that accurate. |
| Solution: | Using the right analytics platform, even a business manager with limited analytics expertise can expertly manipulate data and models, create business scenarios, and report on them. The software enables the manager to focus in on the business factors, while the software takes care of the "heavy lifting" computations. |
| Problem: | A company buys debt portfolios but does not have a tool to run it through in advance to see what they could, or should, be able to expect in return. The decisions they make to buy or not buy are labor intensive and ultimately not accurate enough on the expected ROI. They are either missing profit opportunities, or buying a portfolio and not realizing the profit they expect. |
| Solution: | A combination of the right models, combined with a software platform that will allow those models to interact with data, can yield surprisingly accurate collection/liquidation forecasts and point out arbitrage opportunities that would otherwise go unnoticed. An experienced company with access to data can easily leverage their data repositories, combine it with a platform like ModelWeb and reap the rewards--by buying smart, efficiently creating cash flow from their portfolio, and promptly liquidating assets. |
| Problem: | A start up collections company is doing many tasks manually because of cost. This includes pulling credit reports, having agents, not analysts, make judgment calls on which contacts to call or not call, manually entering data in to the CRM/CMS system. While they thought they were saving money, this was a typical "false economy". Their decisions were not accurate, or optimized. The labor for the manual processes, along with significant lost revenue, ended up costing much more than software automation would. Ultimately, they were not recovering the ratio they needed. |
| Solution: | With ModelWeb and Credix, they were able to automatically pull the credit reports, have the data parsed by 1200 attributes, have the data automatically fed in to their CRM and the agents could then spend time calling on an optimal groups of contacts based on their own parameters, statistics and variables. This saved a small company over $40,000 and provided them further ROI as they didn't need to hire as many agents as they grew. |
| Problem: | In an environment in which market factors and regulatory changes are drastically affecting consumer behavior, a manager of a financial product struggles with maximizing profit on frequently changing prices, without jeopardizing sales volume. |
| Solution: | Using DemandWeb, the manager is able to stage up a consumer demand model and optimize the pricing of his products while managing business constraints and accounting for regulatory changes. DemandWeb's optimization engine "solves" for the maximum price that the manager can target that won't jeopardize the volume of sales of his product |
| Problem: | A team of research analysts work primarily with SAS to analyze large data sets and create models, but struggle with the ability to output information that the business users can easily digest. In addition, the business managers are constantly asking for "what if" scenarios, which is somewhat cumbersome with such tools. |
| Solution: | Using ModelWeb, the analysts are able to create an easy-to-use environment where the data can quickly interact with their SAS models and business scenarios are quickly crafted and reported on. The analysts distribute dashboards and reports via a URL that the business managers can quickly get to through the browsers on their tablet PCs. The analysts are now solving business problems in real time, as opposed to waiting for job assignments. |