The Data Analysis and Optimization (DAO) course extends the core Decision Analysis curriculum and prepares students to perform quantitative analysis at a level consistent with (or exceeding) expectation for MBA interns in positions where advanced analytical skills are required. DAO exposes students to a variety of tools in business analytics and management science such as data analysis, statistical inference, generalized linear regression models, and time series forecasting, as well as the suite of optimization methods and techniques.
This popular elective is offered in the first year of the MBA program and attracts about 150 of Darden’s 300+ first-year students and, like all first-year electives, DAO is also offered in the second year. Taking DAO has historically been a signal of student’s above-average performance at Darden. In 2006-2011, approximately 80% of Shermet Award winners elected to take DAO in the first year.
The DAO course was taught by Phil Pfeifer in 2005-2009, and is currently taught by Anton Ovchinnikov.
Hollywood has the Walk of Fame, the National Hockey League has the Hall of Fame… and the DAO course has its Webpage of Fame!
The DAO Webpage of Fame has been celebrating the summer successes of Darden students since 1995. Each year upon completing their summer internships, DAO students are encouraged to describe how they used the tools and techniques learned in DAO to make a real difference for their respective companies. A panel of judges (currently Bodily, Ovchinnikov, and Pfeifer) selects the best submissions for induction into the Webpage of Fame.
Company: LinkedIn (Mountain View, CA)
Category: Data visualization, Regression
LinkedIn is using the power of big data to connect talented individuals with economic opportunity. Today, the largest portion of LinkedIn's revenue comes from a flagship Software-as-a-service (SaaS) product designed to help recruiters identify and recruit candidates. The software offers advanced search, time-saving contact, and collaboration features above and beyond ordinary premium subscriptions. Andrew interned within LinkedIn's Business Operations team analyzing usage patterns to support product changes and feature development within this flagship Recruiter product.
In order to start the analysis, Andrew first worked with a team to compile a dataset of over 5 million records summarizing June 2013 feature usage for tens of thousands of users. After categorizing detailed features into high level feature sets, Andrew created an interactive dashboard of histograms showing the percent of users using each feature set. The dashboard has parameters for changing the histogram bin size in order to understand features that were frequently used as well as those that were infrequently used. The analysis showed some surprising results and helped drive a number of suggested product changes, with one change accepted on the spot to improve usability.
The second part of this project was comparing feature usage to Net Promoter Score (NPS). NPS is the likelihood that a customer will recommend the product to a colleague, a strong indication of customer satisfaction. Andrew filtered the population for those with NPS, used a goodness of fit test to determine if the sample was representative and converted the feature usage to binary variables for each user. Next, he ran a stepwise linear regression to pinpoint which features correlated with higher (and lower) NPS scores. A few major features had statistically significant relationships, leading to discussions of customer education for positively contributing features and development for negatively contributing features.
Company: Cushman & Wakefield (Washington, DC and Arlington, VA
Categories: Pivot tables, time series models, multiple regression
Chris worked in the Corporate Finance group at Cushman & Wakefield, a real estate services firm with a customer base ranging from small businesses to Fortune 500 companies. The Corporate Finance group provides consulting services for global corporations that need help optimizing their real estate portfolios and deciding when, where and how to change their real estate footprint. This decision process can be complex, as real estate changes must be planned far in advance due to construction, relocation, etc.
Chris was tasked with identifying changes and trends in real estate use in the technology sector, with the goal of helping some of the company's larger clients make more educated real estate decisions. To this end, Chris collected detailed real estate portfolio and financial data for 25-30 representative companies, mostly from the services, hardware and software segments of the technology industry, covering a 7-year timespan. Metrics included financial statement items and data on square footage, employee headcount, facility numbers, and type of facility.
First, Chris used Pivot Tables/Pivot Charts and time series analysis to compare and analyze the data in intuitive, flexible ways that his managers and their clients could process quickly. This allowed the audience to see, for example, how certain metrics were changing over time by company, by sector, and across the industry as a whole. Next, he aggregated all of the data in one large spreadsheet and used dummy variables to identify each line of data by sector, company, year, revenue category, and other items. Using regression analysis, he identified factors that correlate with expansion or reduction in square footage levels and employee numbers.
Chris's analysis provided data-driven evidence for various hypotheses that helped the company advise clients more effectively. His analysis was included in client presentations, and he was awarded "Best Presentation" for the 2013 intern class for his work on this project.
Nathan (Nate) Weir
Company: The Washington Post (Washington, DC)
Categories: Pivot tables, data visualization (leading to risk analysis)
Nate's job this summer was to analyze a project that The Washington Post was contemplating launching into a new business. To understand this business, the first portion of the analysis was to determine the market size. He used pivot tables and data slices to summarize unstructured industry data (over 40k rows) about different markets. He took this information and divided it into strategic subgroups for market sizing, then used it as a constraint in the financial modeling. Second, he created a number of interactive charts that helped illustrate how different variables (such as staffing, sales productivity etc.) affected key performance indicators of this specific new business (such as Customer Lifetime Value and compensation). Finally, he performed the risk analysis around various model inputs to find opportunities to reduce risk. By designing proactive downstream decisions that would change staffing and sales efforts based on how the business reached its growth targets, the worst case became much more appealing. Because of this, this project became more viable going forward. Ultimately, his efforts led to clear understanding of the potential of this new business and drivers of its success.
Company: Target (Minneapolis, MN)
Category: regression, statistical inference
Target’s ability to value an entire market of current and future stores has become more coveted as big box retail stores saturate the United States. Being able to value an entire market could help Target plan it prioritize markets and plan its build out strategy within each market. Liz’s first goal was to identify how much sales Target could get from a market if they built out to saturation.
To work on this, Liz first summarized a great amount of data from Target’s Market Research and Analytics teams to understand what metrics Target currently used to evaluate stores. Next, to normalize for market population and different levels of income, she identified the percentage of a market’s income Target was capturing as sales in the current stores (share of income). Liz then ran numerous multiple regressions to determine which metrics were most predictive of the share of income factor. Two of the factors identified in the regressions were store density and share of general merchandising square footage. She segmented the data for the approximately 160 markets Target has more than one store in into four categories using high/low store density and share of space. To verify these categories were statistically unique, Liz ran two sample t-tests assuming equal variance. They were indeed unique, so she used two dummy variables for these categories to see how predictive this categorization alone was on the share of income Target attained. The adjusted R-squared for this regression indicated there were many other variables that would help predict the share of income received, but individual variable statistics showed that the categorization was statistically significant.
To summarize her findings, Liz used the 75th percentile market within each category (based on share of income) to benchmark other markets within that category against. Scaling up for population and income in the market, she identified potential sales Target could obtain in every market. This potential sales number gave Target an idea of how close they were to market saturation.
Company: Tough Mudder (Brooklyn, NY)
Category: logistic regression
Tough Mudder is a company that puts on military style obstacle course endurance events, a video on the company’s website front page [www.toughmudder.com] shows what these events are. Started only in 2010 by 2012 it had almost half-a-million participants (customers) attending its events, and projected revenue around $70M.
Saul’s job was to analyze Tough Mudder’s customer base. He first organized their customer records. Then he analyzed some basic KPIs with respect to customer characteristics (demographics, purchasing history, etc.) and built customer profiles. Finally, he set up a number of logistic regression models that estimated the probabilities of certain customers to engage in certain behaviors: for example, which customers are most likely to attend a second event, which event participants are most likely to recruit their friends, which participants are most likely to register for an event 9 months before, 6 months before, 1 month before, and many others.
This led to recommendations about who to target with advertising and when, ultimately allowing the company to cut a good amount of advertising spend simply from not targeting the audiences that were unlikely to convert during a particular time period. Additionally, Saul recommended a number of new, potentially very high ROI marketing tactics based on the identified customer behavior patterns.
Saul’s work over this summer that had a big impact on the company, helping the company become more data driven and better understand customer behavior.
Company: Interbank (Lima, Peru)
Category: multiple regression, histograms
Interbank is the fourth-largest bank in Peru focusing primarily on retail banking with some 250 branches all over the country.
Luis’s job this summer was to help Interbank understand how profitable a given branch could be given its location, area demographics, size, opening hours and other characteristics of the local business environment. As a first step Luis aggregated the necessary information about the branches profitability and the underlying environment characteristics in one convenient data set. Then he built a multiple regression model that predicted branch’s profitability based the characteristics of its local business environment. This model allowed identifying “outliers” in either direction, suggesting candidate branches for more detailed analysis of the business practices, and ultimately identifying best practices and areas for further improvement.
When presenting this work to top level executives Luis was able to efficiently use histograms for presenting the variability in the branches’ performance and environment characteristics. By seeing both the benchmark from the model and dispersion in characteristics, Luis was able to suggest where resources should be focused and how the unprofitable branches can be made profitable. Some of the results Luis presented were a revelation for the top management as they broke certain existing preconceptions about what drives the performance of different kinds of branches.
Company: LivingSocial (Washington, DC)
In just under three years, LivingSocial has run around 85,000 deals across more than 300 US and Canadian markets. Traditionally, Sales has relied on 50% commission as a starting point when inking new merchants. Furthermore, they’ve depended only on the revenue side of deals (or “net revenue” which is their actual take from the deal) and have neglected the costs. Without considering the cost side of the equation, LivingSocial as a company cannot understand deal profitability. Furthermore, what commission rate should a Sales rep aim for? Can we provide operations – the people responsible for accepting and scheduling deals – additional data about the profitability of deals?
With close to 80,000 deals’ worth of data, Ryan first computed the Net Contribution after Cost, or profit. Revenues were easy. Costs, generally, included operational and promotional costs as well as credit card fees and refunds – none of which were easy to calculate given the database structure. Furthermore, LivingSocial had scant data on the actual number of recipients for each e-mail blast, affecting any calculation of per user figures. Given a deal’s “list size” (the number of e-mails sent for the deal), Ryan calculated the net contribution after cost for every deal in the US and Canada. Then, he ran log-regressions for net contributions for each market, which allowed him to compare the residual (unexplained) contribution of a deal to that of any other deal regardless of market/concept. LivingSocial tags all deals with a concept – massage, for instance – so while we know an “average” deal in a market given an e-mail list size, we can also look at the relative performance of Massage versus Lasik in a given market. Lasik, as it turned out, had relatively high refund rates, which impacted profitability. But, Lasik deals have historically low commission rates. Therefore, LivingSocial could make some adjustments (based on averages of the actual residuals) when looking at market/concept pairs.
To make it easier for the Sales and Operations force to utilize, Ryan used the regression analysis to forecast expected profit given a list size for a market/concept in combination with the historical commission rate to ultimately determine what commission rate was required to achieve an “average” deal for a given market/concept pair. So, if Massage has traditionally outperformed other concepts in DC and has traditionally been sold at 38%, it could be sold at 32% commission and still yield a deal with average profitability. On the flip side, under-performing concepts need to be sold at higher-than-normal commission rates to achieve average profitability – an input of a high value to the Sales and Operations teams. Additionally, Ryan calculated the commissions required to simply breakeven: the critical “Do Not Run” points below which Operations should never go.
In sum, given the imperfect deal data and a handful of assumptions, Ryan was able to utilize regression analysis to better equip LivingSocial’s Sales and Operations units to set optimal commission rates and drive more profitable deals for the company.
Company: A.T. Kearney (Washington, DC)
Category: Pivot tables, regression
Jennifer worked in a strategic sourcing role with a global food and beverage client. She evaluated two different initiatives for the client to determine which could have the greater financial impact on 2012 cost savings.
The first initiative was investigating maintenance and repair spending. The client had a large amount of data from a number of different sources and required PowerPivot in order to combine all the information into one dataset. PowerPivot is a free add-in that enhances the capabilities of PivotTable and allows linking data from multiple sources through a common identifier. Once she linked the data Jennifer was able to use pivot tables to sort the data by type of repair and analyze the difference in maintenance costs by location to determine which repair shops were providing consistent, low-cost service.
The second initiative was estimating the size of an opportunity to improve auction sales for delivery vehicles. Jennifer analyzed the client’s historical data to understand correlations between the selling price and the characteristics of the vehicle such as make, model, year, and mileage, as well as auction characteristics such as date, location, and floor price. Using multiple regression analysis, she identified opportunities for increasing auction sales by moving vehicles to a different location and maintaining a consistent floor price based on certain vehicle characteristics.
Company: Norfolk Southern
As a necessary part of shipping intermodal freight, Norfolk Southern’s (NS) customers reposition their empty containers around the country in order to move them to markets where the freight is reloaded for shipment. Customers reposition these empties without regard to NS cost structure. In order to capture the loaded freight business, NS was shipping such empty containers at special low rates and was incurring significant losses on this aspect of the business.
To address this problem, Tripper created an optimization model that maximized NS’s margins by optimally routing the empty containers in such a way that clients’ requirements are satisfied at no increase in the cost to clients. He started with NS’s three largest clients: each week the clients send in their empty distribution plan for the following week – terminals they plan on using and volumes by lane. Within five minutes (utilizing VBA macros) NS sends them an optimized distribution plan with suggested lane changes. Their planning and cost is unchanged and NS margin is maximized. By influencing their forecasted plan, NS was able to immediately capture these gains going forward.
The model was implemented for the last 4 weeks of Tripper’s internship and immediate margin improvements of $65,000 were recognized. He then trained NS employees to run the model and it will continue to be used on a weekly basis to influence the distribution of virtually all empty containers moving on Norfolk Southern railways. It is estimated that the total annualized margin improvement from implementing the Tripper’s model will exceed $3 million.
Company: McKinsey & Co
Category: Optimization with binary variables
In periods of excess market capacity, Paul’s client did not properly utilize its six production facilities: all facilities continued to run at slower rates regardless of the financial impact and without considering alternate asset optimization strategies.
Paul built a Solver model that determined the optimal operating rate for each facility under various market conditions. Given the fixed and variable cost structures of the different facilities, some were better off running at full capacity while others should be slowed or shut down, minimizing costs and maximizing profitability. The model optimized the production decisions (modeled continuous variables) and the facility closure decisions (modeled as binary variables) utilizing the facility costs, supply, and demand inputs out to 2020.
The model resulted in significant benefits to the client: given the flexibility of the inputs (forecasts, costs, shutdown costs, pricing, etc.) and the speed at which it calculates (under 1 second), the management considered multiple market scenarios and set on a robust course of action. Best of all, the model enables the operations and finance departments of a $2 billion business unit to flex their operations to save millions (potentially hundreds of millions) of dollars a year in excess supply situations. It also helped to guide future capital expenditure decisions as it brought to light how important the supply/demand balance really is to the organization.
Category: Time series forecasting, regression, pivot tables
Ryan worked for Accenture in their Supply Chain Management Consulting practice where his client was a major cable provider in the US. Ryan’s task was to improve client’s forecast accuracy for cable boxes: low forecast accuracy has been driving excess equipment purchases and unnecessary inventory levels.
Ryan analyzed the client’s historical demand data, to understand correlations between the demand for different cable box types and technologies and ultimately built a model for the client to use when creating their forecasts. His analysis included taking the large amount of data, using pivot tables to sort the data into meaningful and useable data segments, regression analysis to understand correlations between product types, and ultimately the development of time-series forecasting models using ARIMA methodologies.
Keeping a portion of the data in the hold-out validation sample, Ryan established that his model improved forecast accuracy from the client’s historical 20-50% up to about 80%. Such improved forecast accuracy is estimated to result in some $30M reduction in excess inventory among other cost savings such as reduced storage and transportation costs.
Category: data reporting, pivot tables, optimization
Whit worked for CNN.com’s Internal Strategy Group, focusing on Product Strategy & Partnerships. This group did not have an accurate picture of the value exchange between CNN.com and 18 strategic partners that provide content to the “highest margin” sections that are essential to CNN Digital’s long term growth.
These content partnerships were initially structured in an ad hoc manner that focused on driving site traffic and sharing content, but didn’t initially consider the financial value derived from each. Whit created a quantitative way to analyze the performance of these partnerships, treating each as though it was a separate company.
Whit used the available site statistics to establish a final relative value measurement that identified which partnerships were highest or lowest performing and also identified which ones needed restructuring.
After quantifying the value exchange between partners, Whit developed a package of five “what-if” analysis models to help CNN further understand partner value creation mechanics, restructure deals, negotiate favorable deal terms and inform staffing decisions based upon the restructured agreements.
Quoting CNN’s Digital Partnerships Manager, Chris Finan: “Whit has changed our approach to partnerships: taking us from a reactive stance to a more proactive and strategic approach… [His] model will help us determine the success and revenue implications for each of our partners across CNN.com.”
Katherine Gordon Murphy
Category: Hypothesis testing, Pivot Tables, Monte Carlo simulations
Kate interned at UPS where she performed a strategic assessment of the international dangerous goods (IDG) shipping market. To date there is no central tracking of the movement of dangerous goods and UPS wanted to know the size, potential and its share in the IDG market. To estimate the market size, Kate used a global trade database and worked with experts to estimate what percentile range of each commodity was hazardous. She then ran a Monte Carlo simulation which produced risk profiles (histograms) of the complete IDG market size.
After analyzing the external environment of international dangerous goods shipping, Kate looked at the role it plays internally for UPS. UPS views IDG as a strategic product that 1) leads to incremental non-niche shipping and 2) attracts clients that are more profitable than “average” UPS clients. Interestingly, these hypotheses had never been tested. Kate analyzed UPS’ practices related to IDG and discovered that a “natural experiment” occurred in the data between 2008 and 2010: some of the clients that were pre-approved for IDG shipping by UPS never used the service. Kate used pivot tables and hypothesis testing to compare the differences in shipping volumes and profitability between the pre-approved clients who shipped IDG and those who did not. While the results must remain confidential, Kate’s work led to very interesting conversations about the future of the IDG product offering and plans for its expansion.
Company: Navy Federal Credit Union
Category: Time Series Analysis, Linear regression, Optimization
Over the summer Ilya worked in the Lending Department to improve the forecasting models of losses in consumer portfolio. The current approach was to first forecast the outstanding balance of the portfolio, and then use another model to forecast the loss as a percentage of the balance. This forecasted loss percentage was applied to forecasted portfolio balance to get the absolute loss number.
Ilya first attempted to create a single model that would forecast the absolute loss, but that attempt was not successful. He therefore kept the general structure with two models unchanged.
To forecast the outstanding balance he built separate models for the three main segment of the portfolio, Consumer loans, NAVCheck and Leasing, using time-series ARIMA models with auto-correlation, seasonality and trends. Aggregating the prediction of his models yielded a noticeable improvement in the quality of the forecast for outstanding balance. Interestingly, however, when Ilya applied the existing loss percentage model forecasts to his new outstanding balance prediction, the resulting forecast of the absolute loss became less accurate. As Ilya discovered, this was because the first existing model over-forecasted the balance, while the second under-estimated the loss percentage, so that when taken together they produced a forecast that was “just right.”
Ilya therefore also redid the model that forecasted the loss percentage. The existing model for losses was weighting the past 3 years of historical data equally. Ilya used Solver to find optimal weights minimizing the sum of squared errors; as expected Solver gave more weight to the recent data. These weights were used for the modified model for forecasting the loss percentage.
Ilya’s new model for outstanding balance combined with modified model for the loss percentage proved to produce a much better forecast in terms of squared errors than the current model in place. Ilya’s model was launched in test-run as a candidate to replace the current one; its performance will be evaluated at the end of the year.
Categories: Linear Regression, Optimization
Over the summer, Brandon Smit worked for the Shoes In-Stock Management team at Amazon.com on two projects: the Site Merchandise Forecasting Model and the Liquidation Model.
In the first project, he analyzed sales data for approximately 10,000 shoes and built four linear regression models to predict 20-week demand at the style-color-size-width level, one for each of the four demand segments. When tested on a holdout sample, his models outperformed those historically used by Amazon.com in terms of forecasting accuracy (as measured by Mean Absolute Percentage Error, or MAPE). Brandon’s models are currently used to inform procurement decisions.
In the second project, Brandon analyzed the economics of a liquidation decision. He built a model to determine at what point an item should be withdrawn from sale and sold to liquidators to recoup a fraction of the procurement cost. The model used a VBA macro that performed a Solver optimization across thousands of unique products to calculate the breakeven period demand and number of units to liquidate.
Brandon’s recommendations led to the liquidation of nearly 100,000 shoes; that freed up about 5% of warehouse capacity and provided capital to invest in products for the upcoming holiday season.
Charles (Chip) Hogge
Categories: Pivot Tables, Optimization
Chip Hogge worked with the domestic expatriate operations of a leading health insurance provider. The client did not have an accurate picture of its organizational processes or how resources were allocated across 15 business units and 10 global locations. In order to transition to a more competitive cost structure without sacrificing service levels, the client needed to determine ”who is where” and ”who does what” to facilitate an organizational and business process redesign. The original employee-level data were stored in an Excel file and tagged with cost center information. Chip used iterative lookup functions to assign process categorizations and outsourcing priority to each employee. Several pivot tables were then used to organize the data and provide different views of the overall organization, including by line of business and process, location and employee type. Chip then developed an optimization model that minimized compensation spending based on numerous constraints established through interviews with Business Unit leaders across the organization. The client used this information and recommendations to initiate vendor discussions and develop a reorganization plan consistent with its strategic priorities. These initiatives had an immediate impact on profitability by reducing compensation spending by 30% and total SG&A spending by 10%.
Company: Delta Airlines
Stefan Talman worked in Delta Airlines’ Revenue Management department. Because its existing inventory demand forecasting system was based on historical data, it couldn't capture/plan for the demand upswings for new large events. For example, because the Super Bowl is in Honolulu (airport code HOU) this year, demand forecasting for flights to HOU based solely on historical data would be clearly inaccurate. Stefan used multiple regression to describe the change in revenue for the days before and after an event (accounting for day-of-week and seasonal effects) using a set of independent variables (event size, market share, airport size, event type, etc.) for some 150 events in various market types (e.g., hub and spoke) and event types. The final model was based on event size adjusted by market share and airport size (both in seats/week). Stefan’s model was adopted by the inventory management team and will continue to be refined as new large events occur.
Categories: Pivot Tables
Although Target had high-level chain-wide visibility into the future profitability of its Pharmacy business, it did not have a good understanding of how the Pharmacy P&L varied across each of its nearly 1,600 stores. Using pivot tables, Bryan Lieber analyzed historical prescription ("script") sales data by store to understand what store characteristics (store size, age, region/geography, etc.) had the biggest impact on sales growth. Ultimately, he developed a sales-maturity curve based on age for each of Target's four regions. This allowed him to create five years of annual sales projections for each of Target's approximately 1600 pharmacies. Bryan combined these projections with store-specific financial data and long-term industry trends to develop a model that projects annual profitability for each of the next five years on a per-store basis. He then segmented the store population into four classifications based on their profitability and growth levels: Stars, Cash Cows, Questions, and Dogs/Sub-Optimals. This segmentation enabled much more granular reporting about how each of these various segments contributed to chain profitability, particularly highlighting the large gap between the best- and worst-performing stores. Finally, Bryan reviewed the time-of-day purchasing patterns for the "Dog/Sub-Optimal" stores to identify candidates for the firm’s hour reduction initiative and estimated the annual EBIT improvement from this initiative.
Going forward, Target will use the Pharmacy Segmentation model in three ways. First, segments can be incorporated into monthly reporting to provide additional visibility into Pharmacy profitability. Second, the model will be used to refine the target list of stores for hour reduction programs and calculate potential savings. Third, the Pharmacy team will use the segmentation to be more selective when rolling out new strategic initiatives. Rather than deploying new strategies to all stores in a given geography, with the help of Bryan’s model, they can target the type(s) of stores in the areas that best match their objectives.
Categories: Data Reporting, Pivot Tables
Geoff Coffield worked with the manufacturing IT group of a large pharmaceutical company on two projects. In the first project, he was tasked with figuring out how and where money was being spent on contract employees and identifying opportunities for savings. From the company’s disorganized data, he created large pivot tables and charts using translation matrices. From the results, he was able to determine how, where, and by whom money was being spent on contract employees and to a certain extent what the employees were doing. Based on that analysis, he identified an addressable opportunity of about $18 million in annual spending and developed an approach to realize these savings. This realization process is currently under way and on track.
In his second project, Geoff supported a global reorganization affecting thousands of stakeholders around the world. He built a multidimensional employee survey to track progress across the different operating groups and geographies. He then created a comprehensive dashboard that allowed the client to analyze data across relevant dimensions and identify opportunities for improvement. In particular, because the client’s manager who will own this moving forward is not experienced with Excel, Geoff had to automate nearly every aspect of the process to the point that the manager need only copy and paste data into the dashboard template. Since then, the survey and the dashboard have been used twice to help identify a number of opportunities the company is currently pursuing.
Category: Regression and Pivot Tables
The forecasting system Ryan Rayborn constructed to predict sales of Coke products in Target stores outperformed their legacy system and is currently being subjected to side-by-side testing prior to roll out. More
Ben Potter used regression analysis to make inferences about the extent to which lower prices led to higher online sales for Geico relative to Progressive. More
Category: Pivot Tables
To quickly and accurately quantify the benefits to Campbell Soup of implementing his suggestion to classify correctly that portion of trade liabilities that are non-current, Josh Burke applied a pivot table to data on the prior year’s 60,000 separate trade promotions. More
Company: Natural Resource Partners
Category: Time Series
Natural Resource Partners (one of the country’s largest owners of US coal properties) used to conduct simple best case/best guess/worst case analyses of projects until Kevin Graney came along and showed them how to use Crystal Ball to simulate a complete time series of future coal prices. The new, more sophisticated, approach allows NRP to better quantify the value of projects –especially those involving contingent contracts. More
To decide how best to process 150+ million pieces of mail each day, Sid Sinha built Merkle a Solver optimization model. More
Company: Kraft Foods
Roberto Brillembourg used regression analysis to help Kraft Foods measure the effectiveness of its TASSIMO coffee continuity program in increasing consumption of TASSIMO coffee. More
Category: Pivot Tables
During his summer internship with BCG, Shankar Raja segmented the US healthcare market using Pivot Tables and developed a comprehensive, market based, bottom-up revenue forecast to inform the post-merger, strategic planning of a client firm. More
During his internship with Bain, Anand Rao used pivot tables to create first-ever organizational charts based on the reporting relationships and cost center assignments contained in a client firm’s 2500-record employee database. More
Company: Bain & Company
To help a client firm price its transportation offering, Christopher Richins used regression analysis during his internship with Bain & Company to build a model forecasting refrigerated trucking rates for shipping produce across the U.S. More
In the process of creating Target’s first inventory of water consumption across all facilities, Will Teichman used regression analysis to identify the primary drivers of water usage for the purposes of prioritizing future water-saving initiatives. More
In order to select the best options for growth across five business units of an Indonesian energy company, Anand Veeraraghavan used a stochastic optimization model (built using Crystal Ball’s Optquest) that accounted for 25 key uncertainties and linkages across the business units. More
Category:Pivot Tables and Regression
Sarah Glass used Pivot Tables at Merck to consolidate patient-level prescription data for the $2B cholesterol drug ZETIA into 30+ sources of business as the basis for her regression-driven, bottom-up sales forecast. More
Alex Holsenbeck used regression to forecast stock-option exercise rates as a key component of his Crystal Ball model to forecast cash proceeds, taxes, and share dilution resulting from EMBARQ’s outstanding options. More
Category: Pivot Tables
Bobby Jordan used pivot-table generated daily performance metrics to both guide and validate performance improvements his consulting team implemented for a distribution center shipping more than $120M annually. More
Kevin Kosefeski used Solver to identify the optimum production schedule for a Danaher factory in China making hand-held digital test strip readers. More
Category: Pivot Tables
Because Gautam Sukumar used pivot tables to summarize the profitability of over 500K orders filled by Dell, the company now has an accurate understanding of configuration profitability.” More
Saul Yeaton used data analysis to convince TARGET that the 75% higher cosmetic sales in stores with in-store beauty advisors was due, for the most part, to how the advisors were assigned to stores. More
Chris Langbein used regression to forecast monthly working capital
needs for ConocoPhillips. More
Company: L-3 Vertex Aerospace
Mike Lorence showed L-3 Vertex Aerospace how to use logistic regression and decision analysis to decide which projects to pursue. More
Company: Titan Virginia Ready-Mix
Douglas Polen used regression analysis to determine the true source of profitability for Titan Virginia Ready-Mix’s business. More
Brian McCahill built an optimization Excel model the Danaher Corporation used to create a manufacturing strategy for a new $100MM OEM program. More
Company: Home Depot
Category: Pivot Tables
Matt Kirby used a Pivot Table to help Home Depot save millions of dollars. More
Category: Time Series
Rick Ramsey built a time series forecasting model to help Gilbarco forecast monthly sales of its main product, gas dispensers. More