Advanced Statistical Analytics
Better, faster, and deeper insight
Why Advanced Statistical Analytics?
Advanced statistical methodologies are the foundation for leveraging big data and meaningful analytics in order to make better and faster decisions that further organizational goals. Competitive Analytics empowers our clients with both core statistical processes as well as developing custom statistical platforms, dashboards, and reporting tools. Moreover, advanced statistical analytics involves six core disciplines: 1) evaluating and selecting the appropriate models, methods, and process to follow, and in some cases, developing custom methodologies from scratch due to unique scope characteristics – often a given statistical model (i.e. template) will not deliver target results; 2) determining the scope and complexity of a project based on timing of deliverables; 3) determine geospatial segmentation parameters (e.g. global, national, state, county, city, ZIP, TRACT, neighborhood, school district, drive time polygons, custom polygons, etc.); 4) deciphering demographic and psychometric parameters cross-tabulated by gender, age, income, household profile, tenure, buying behavior, etc.; 5) identifying and segmenting data into groups representing key common traits and attributes (e.g. k-mean clustering analytics); 6) correlating dependent and independent drivers in order to decipher key drivers, behaviors, practices, policies, and externalities affecting each segment, group, demographic, etc. The following client engagement summaries reflect the proven success of our “Cycle of Advanced Statistical Analytics” that illustrates the 16 functional areas of expertise we deliver to our clients and encapsulate our wide and deep experience with developing, delivering, and updating advanced statistical tools.
Competitive Analytics delivers statistical solutions that empower Toyota executives to make better and faster decisions. For the Service Parts & Accessory Operations (SPAO) division (that distributes to ~1,250 Toyota dealers, Lexus dealers, and private distributors, coordinates entire supply chain that ships and receives 350,000+ parts daily, and responsible for exporting to Japan, Europe, and Asia) Competitive Analytics designed an ensemble modeling process to generate accurate statistical trends, correlations, and forecasts via external economic indicators as well as myriad internal metrics. We also developed a suite of statistical models that significantly improved facility forecasts by 2.8X to 7.6X, developed interactive statistical dashboards that significantly increased operational efficiencies, and developed processes to integrate statistical forecast inputs back into databases, creating an efficient feedback loop.
Competitive Analytics delivers and updates a comprehensive interactive what-if 10-year industry statistics and sales forecast model applying business cycle scenarios, macro/micro economic drivers, age cohort statistics and forecasting, buyer segmentation and market share analysis, cannibalization analysis, and interactive reports to C-Suite.
The Boeing Company
Using a series of advanced statistical methodologies and custom statistical tools, Competitive Analytics designed and delivered a Long Range Business Plan (“LRBP”) model that involved myriad valuation metrics such as OpEx, CapEx, Land Valuation, and Revenue, to name a few. This LRBP is updated each quarter and typically involves 3 to 6 staff members taking upwards of 6 to 10 weeks to update. This LRBP created a more streamlined, accurate, and automated reporting process with deeper statistical and analytical reporting.
The Irvine Company
Competitive Analytics delivered a wide and deep range of custom in-house statistical, analytical, and business intelligence services to Irvine Company. These included developing a custom Rent-Value Optimization solution for optimizing multifamily rental units, optimizing renewal modeling, strategic forecasting, demand/supply, and numerous other analytical tasks. Using advanced statistical tools, we also developed a correlation model that identified, scored, and ranked renters based on the propensity of a resident to renew their lease. Other statistical-driven deliverables included designing and developing numerous business intelligence dashboards that provided deep and broad insight into operational performance on a portfolio level as well as by submarket, product, unit type, class, and down to the individual unit level. We also developed a custom price optimization modeling that evaluated over 53,000 apartment units’ rent levels based on over 300 unique unit characteristics and over 50 macro/micro demand drivers. Other successful projects included a massive data cleansing of several databases, help design and develop a custom renewal optimization model, develop a forecast model, and develop a custom renter segmentation model. Competitive Analytics’ recent forecast for Irvine Company’s Orange County portfolio realized only a 7 basis point variance with actual results.
National Powersport Auctions
Competitive Analytics developed and updates an advanced statistical predictive model that optimizes the price of motorcycles, boats, and other recreational vehicles that are auctioned each month. We have thus far improved NPA’s statistical pricing process from a 70% to 80% accuracy to over 90%.
City of Riverside, California
Competitive Analytics designed and developed an unparalleled quality of life index and planning tool called DECIPHER™ SEQOL (acronym for Significantly Enhancing Quality Of Life) in order to help states, counties, and cities decipher their own performance, growth, sustainability, and competitiveness. This dynamic and multi-functional index is unlike any other in terms of accuracy and usefulness. It is both an statistical index and planning tool. Other quality of life indices are typically based on a handful of static indicators at a specific point in time. Furthermore, there is typically very little visibility into methodology. In contrast, the SEQOL Index is an extremely precise and accurate measurement tool measuring thousands of drivers of quality of life falling within categories such as education, economy, crime, environment, innovation, etc. This allows government officials and decision-makers to drill down three levels – Level 1: A singular top-level score for the city, county or state; Level 2: Categorical rankings (e.g. Education, Crime and Safety, Economy, Environment, Innovation, Economic Development, Affordability, Technology, Demographics, and other categories); and Level 3: Each individual indicator that underlies the categorical and top-level score (e.g. SAT scores, misdemeanors, air quality metrics, etc). Moreover, government officials can use SEQOL as a strategic planning tool in that each department can drill down into the drivers that affect their area of responsibility for measurable performance benchmarking. Furthermore, SEQOL is a monitoring tool in that it tracks indicators over a period of time, allowing government officials to monitor the impact of their decisions over time. Finally, the SEQOL Index is interactive in that city officials are able to change certain aspects, components, and weights of the scoring methodology (e.g. the weighting of specific drivers). For example, with respect to city planning, DECIPHER™ SEQOL analyzes all specific categories of land use development along with economic and demographic indicators that correlate with these land use categories. These correlative indicators are then used to forecast demand/supply for residential housing (for-sale and apartment), retail, office, industrial, hotels, schools, transportation, etc. empowering local governments to make optimized decisions with respect to future land-use (based on forecast scenario models of residents, employees, employers, visitors, etc.). As an example, Competitive Analytics designed a comprehensive SEQOL index for City of Riverside, California. Click here to view Riverside’s SEQOL report.
City of Anaheim, California
Competitive Analytics develop statistical housing models by ZIP, TRACT, neighborhood, households, and specific development projects in order for the City of Anaheim to optimize residential planning and budgeting.
City of Ontario, California
Competitive Analytics developed myriad statistical valuation models for a multitude of real estate land uses, including for sale residential, apartments, office, and hotel. We also developed a 50 year long range demographic demand and supply forecast which utilized several proprietary statistical correlation and forecast models.
City of Santa Ana, California
Competitive Analytics developed advanced statistical models focusing on resident demographics and psychometrics in order to accurately track consumer intelligence. This involved aggregating, comparing, analyzing, and anticipating resident perceptions, suggestions, and recommendations via several targeted groups of individuals that live, work, and/or recreate within the city of Santa Ana as well as targeting groups within the surrounding sphere of influence. Secondary data and statistical analytics was supplemented with field surveys (i.e primary research) which provided an effective and cost efficient method of receiving insightful market intelligence at the qualitative and grass roots level.