Client: A bulge bracket investment bank's sell-side research analyst who wanted to upgrade his team's financial modeling capabilities to better quantify their qualitative analysis.

Challenge: The head analyst was an industry expert who understood the drivers of growth in his coverage universe but wanted to improve his team's financial models to better quantify their catalysts. In addition, their models weren't standardized despite the consistency in the coverage companies' reporting metric and analysis required. The mandate was to "build me the best model on the Street for my sector."

Solution: We sat down with the analyst and his research associates and translated knowledge of key drivers into quantitative analysis by building an extremely scalable, robust model to facilitate sector coverage. Our success was validated when almost immediately after transitioning their coverage universe to the new template, they were the first team to identify a $2 billion liquidity shortfall for one of their names.

While we agree with the ages-old adage of "garbage in, garbage out," if you know your stuff inside and out, we'll build you the best model to go along with it.

Client: A large buy-side asset manager facing rapid growth in AUM who wanted to manage headcount growth without sacrificing quality.

Challenge: The challenge here was two-fold: (i) standardize best practices for each portfolio manager, and (ii) increase the number of coverage companies per research analyst while maintaining integrity of analysis.

Solution: A two-phase solution was implemented: first, WST provided financial modeling training courses to disseminate the best practices that we preach; then, we worked with the client to provide customized training & standardization for each team of portfolio manager and research analysts. A key hurdle that we overcame was that each team had their own approach and industry-specific drivers of growth. However, once the first phase (the 80% of the 80/20 rule) was implemented, the second phase was executed much quicker and painlessly than initially expected.

Client: A federal regulatory agency responsible for policing the United States securities industry looking to upgrade their staff's technical capabilities in data analysis.

Challenge: Individual examiners on the same team and across different offices were analyzing the same data in multiple ways and formats (Microsoft Excel vs. Microsoft Access). On top of a lack of standardization across the agency, massive data dumps were taking too long to sift through individually and manually. The client needed a uniform approach that was the most efficient and easy to implement for some of the seasoned professionals.

Solution: We learned the client's various existing analyses and approaches to data analytics and custom designed a standardized approach and methodology. This required merging the best components of their existing approach and designing further streamlined procedures. We then delivered the new processes and recommendations by training the client's staff across all regional offices and assisted in implementing system-wide efficiencies.

Client: A top 5 private equity firm that needed assistance in internal valuation.

Challenge: The CFO's team of accountants and valuation experts were constrained with their existing accounting system and financial reporting structure. The lack of customization functionalities meant that several high-profile and time-sensitive analyses could not be constructed in a timely basis. Furthermore, additional complexities included the dynamic nature of the data, multiple hierarchies and cross holdings that could result in double- and triple-counting and constant need to produce updated reports.

Solution: Our innate understanding of how private equity firms are structured allowed us to quickly learn the multi-dimensional constraints with the client's data. Then we custom-built analyses and models via close collaboration with key members of the CFO's team, leading to a visualization of the entire number-crunching process required and an end result that enabled the rest of the team to implement long-term solutions via their accounting system.

This is a classic example of process re-engineering: by first understanding the existing process and constraints, coupled with the "wish list," we helped enable the client's ability to respond to dynamic and intensive reporting environment.

Client: A Fortune 100 multinational conglomerate that is highly active in strategic acquisitions, joint ventures and divestitures of non-core assets.

Challenge: The client's global reach includes operations in more than 150 countries around the world. Small teams of corporate business development professionals are scattered across multiple locations and industry verticals. The challenge was how to efficiently disseminate a standardized, and yet customizable approach to the team's growing financial modeling needs.

Solution: We constructed custom-built modules on top of our most advanced merger modeling courses that mirrored the client's typical deal structures. We continue to train all their existing professionals and lateral and new hires, helping to further standardize the client's internal best practices. Thus far, we have trained the client's professional executives in all continents except Antarctica.

Client: A family office that controls the oil assets of an emerging market economy and wanted to diversify their investments beyond oil.

Challenge: The client has a solid understanding of the economics of the country's main commodity, but needed some guidance on quantifying and sensitizing the returns of the assets they wanted to diversity into and further expand. In addition, the returns needed to be shown on a standalone basis as well as a consolidated basis.

Solution: We worked with the client's staff and key consultants to understand the nuances of each non-oil asset in their portfolio and built them a fully dynamic, robust model that was easily scalable. This allowed the client to quantify the risks and rewards of their current portfolio holdings. Moreover, the approach we took with the client mirrored our strict methodology to model building: "build me a dynamic model, not a static analysis!"