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50 Business Problems I’ve Addressed with Predictive Analytics, Data Science, and Advanced Analytics

50 Business Problems I’ve Addressed with Predictive Analytics, Data Science, and Advanced Analytics

I was reading Vincent Granville’s recent blog post and thought I might add a few of the problems I’ve addressed in my career. While these are not all data science they do fall within advanced analytics.

  1. Estimating hybrid yields and crop characteristics across multiple geographies, soil types, climates, and ecosystems based on performance of limited field trials. Estimating the same for heretofore uncrossed inbreds.
  2. Accurately forecasting the demand for promotional items driving the market basket 18 months into the future in order to accommodate an extended supply chain.
  3. Estimating the brand capital associated with consumer brands in the marketplace. E.g. What is the value of a brand in the marketplace in terms of both goodwill on the balance sheet and an organizations ability to leverage the brand capital through marketing to deliver sales.
  4. Network optimization for optimizing supply chains. Optimizing supply chain routing in near real-time.
  5. Optimal scheduling of ship dates for seasonal goods based upon stochastic analysis of probable events along the chain in order to assure supply without clogging the pipe.
  6. Call center optimization.
  7. Forecasting the demand for retail store associates across a large retail chain and optimizing the schedule based upon that forecast.
  8. Predicting what goods are most likely to be purchased during a hurricane warning.
  9. Predicting the annual sales of a prospective retail site based upon demographic, market, competitor and other data.
  10. Optimizing locations for retail site selections using game theory and accounting for cannibalization and impact on key competitors.
  11. Optimal routing for transportation fleets given uncertainty.
  12. Pricing optimization, including setting pricing strategies for every day pricing.
  13. Estimating the impact of pricing changes on demand for high affinity items. Understanding expected impact of promotional pricing on over-all revenues based on affinity items and additional trips.
  14. Estimating the impact of operational activities on competitors and the likelihood of cannibalization.
  15. Estimating sell through dates for seasonal goods and understanding the risk profile for lost sales opportunity and clearance/write-off.
  16. Identifying potentially fraudulent transactions at the register and in the back office regarding cash and check deposits.
  17. Identifying potentially fraudulent workman’s comp claims.
  18. Identifying primary causes associate with employee injury accidents and prioritizing limited resources to prevent and mitigate lost productivity, workman’s comp expenses, and long term liability associated with miss-handled claims.
  19. Identifying potential tax savings due to missed tax benefits across multiple tax jurisdictions.
  20. Analyzing production data to identify root cause of quality issues and productions slow downs in manufacturing environments.
  21. Analyzing clinical trial data for safety and efficacy of treatment protocols.
  22. Analyzing system performance logs to understand bottlenecks in production and analytical lab computing environments.
  23. Analyzing consumer behavior to understand the impact of marketing message theme, channel preference, pricing sensitivity, seasonal good purchase cycle, brand affinities, product affinities, loyalty engagement, net promoter score, customer satisfaction, lifetime value, purchase driver, style preferences, color preferences, size preferences and other brand specific factors.
  24. Integrating consumer behavior data with attitudinal and demographic data to make  cohort level inferences regarding behavior.
  25. Response and uplift modeling to understand the impact of direct marketing efforts in a test and learn environment.
  26. Establishing value of information models to map response/uplift to the financial benefit they bring to the organization.
  27. Establishing a champion/challenger approach to model deployment and consistently measuring the impact the model brings to the business.
  28. Leveraging machine learning to assess when a model needs to be re-scored, refit, remodeled, or replaced.
  29. Integrating insurer, practitioner, and population data, formulary status, and negotiated pricing levels for branded prescription medicines to analyze the impact on long term sales and profits.
  30. Applying artificial intelligence techniques to assist in optimum model selection across predictive analytics solutions.
  31. Forecasting sales and returns in the publishing industry this requires forecasting at the distributor and retailer level and understanding revenue recognition, probability of returns and the publisher’s liability associated with returns.
  32. Integrating omni-channel data in order to model customer response to brand treatments across multiple touch points.
  33. Estimating the most likely customer segment for cash baskets in a retail environment with a high percentage of cash transactions.
  34. Optimizing the retail supply chain for demand driven pull.
  35. Individual store assortment planning for large chain retailers based upon customer behavioral profiles.
  36. Dealer performance estimation and visualization (GIS) in the automotive industry.
  37. OMNI Channel retail performance marketing delivering uplift modeling in a champion/challenger environment and integration into a marketing automation system.
  38. Estimating the customer response to postpaid plan upgrade offers (propensity/uplift) by micro audience and offer theme.
  39. Marketing mix modeling at the individual store level for a larger retailer.
  40. Leveraging early IOT data sources to improve forecasting results.
  41. Analysis of disparate data sources on Hadoop to understand the impact on quality of management decisions.
  42. Automation and application of artificial intelligence techniques in the champion and challenger process for on-going predictive model performance management.
  43. Stochastic optimization of project outcomes (based on data science generated predictors) for the purpose of project portfolio management.
  44. Preparing stochastic based financial projections based upon known cause and effect and predictive relationships (tactics driving KPI’s driving financial performance) for complex businesses.
  45. Estimating future gross margin contribution across a large research and development portfolio for new technology introduction, new product introduction, and continuous product improvements. Application of those estimates to identify key success drivers. Stochastic modeling of the key drivers and estimation models to allocate and optimize annual R&D budgets.
  46. Optimizing balance sheet and off-balance sheet labor costs in a manufacturing environment based on key production predictors including outside economic variables, internal scheduling constraints and forecast demand.
  47. Estimating future resource utilization based on forecasts, historical forecast accuracy, sales pipeline, and existing contract terms.
  48. Predicting customer churn and developing optimal retention policies to prevent it.
  49. Identifying cross sell and up-sell opportunities in a business to business environment.
  50. Identifying root cause of sub optimal brand performance across a portfolio of brands and determining optimal corrective action to improve financial performance.

Patrick McDonald HeadshotAbout Patrick McDonald 

Patrick McDonald is an Associate Director with Protiviti focused on advising clients in the Retail, Manufacturing and Telecommunications industries on analytical solutions. Over a 20 year career in advanced analytics, Patrick completed tours in a big four firm and leading analytics technology and software companies.



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