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Categories: Events

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.

 

Categories: Predictive Analytics

How Can I Increase the Value of My Marketing Investments Using Predictive Analytics – A Real Life Use Case

In my last blog, I discussed how predictive analytics can increase your marketing bang for the buck by giving you clear insights into where to spend your marketing dollars. In this entry, I’ll give you a real-world example of how a retail department store chain with multiple product categories decided who to target for a store mailer using analytics and customer segmentation.

To determine their target audience, the retailer wanted to gain a better understanding of their customer segments and where money was being spend across those segments. The first step was determining which customers shop which categories. Working together, we mapped over 2 million customers, identified spend by product category and then clustered customers by product category and category spend.

Predictive Product Category

To download full PDF and Continue Reading…

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.

Categories: Predictive Analytics

How Can I Increase the Value of My Marketing Investments Using Predictive Analytics?

If your marketing strategies cost more than they earn, they obviously aren’t good long-term marketing strategies. One of the most useful tools at your fingertips for ensuring and increasing your marketing investments’ value is predictive analytics. Specifically, using predictive analytics to anticipate an individual customer’s needs and wants. Predictive modeling can provide profound insights into customer preferences and trends, allowing you to tailor your strategies around the customer. This is customer experience optimization. Customer experience is a major revenue driver!

If you understand which questions you’re trying to answer or issues you’re trying to resolve from a business perspective, you can build models that will help you understand a customer response to a particular treatment, allowing you to address those key business questions and engage customers more personally.

Some key questions or issue you might want to begin with are:

  • Not enough customers
  • Customers not buying enough
  • Engaging the wrong customer
  • Haven’t found the right customer
  • What new markets can we engage and how

To download full PDF and Continue Reading…

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 analytic solutions. Over a 20 year career in advanced analytics, Patrick completed tours in a big four firm and leading analytics technology and software companies.

Categories: Predictive Analytics

SAP BI Running Embedded Into SalesForce

I come across a lot of situations where customers run SalesForce and ask me whether they can combine their SalesForce data with other corporate data. A most recent request was to combine SAP BW Copa data (profitability analysis) with SalesForce data on clients with payment risks. Today this can be easily be done using data blending techniques of the SAP BusinessObjects BI Suite. More in particular, I used the SAP Lumira salesforce IDBC connector to connect to the customer’s SalesForce data and blend it with their SAP BW data.

But customers these days require more than just combining their SalesForce data with other data. They require to have the full SAP BusinessObjects BI Suite running embedded into their SalesForce environment. With the use of Protiviti SalesForce Connect for SAP Analytics, this can be done.

Read the rest of Iver van de Zand’s article here!

Categories: BusinessObjects

How Does SAP HANA Change the Traditional Enterprise Information Management (EIM) Process?

In the traditional EIM process, typically there are separate ETL, Replication and SDA processes and functions. Traditional processes introduce latency to the data in the data warehouse, as well as other challenges to getting data into one location seamlessly. Today’s world is moving faster, and the need for clean, real-time data is imperative.

Chances are, if you’re reading this blog, you’re no stranger to the power of SAP HANA. But to recap, the SAP HANA platform removes the burden of maintaining separated legacy systems and siloed data. It hold capabilities to transform and cleanse data in real-time from multiple sources.

SAP HANA also has features that enable you to both manage data and improve your data quality. These options offer real-time transformational possibilities that were unthinkable until recently. These features allow you to transform data in real-time with Smart Data Integration (SDI) or conform and cleanse data with Smart Data Quality (SDQ).

To download full PDF and Continue Reading…

contributor_don_loden_lgAbout Don Loden

Don Loden is an information management and information governance professional with experience in multiple verticals. He is an SAP-certified applications associate on SAP EIM products. He has more than 15 years of information technology experience in the following areas: ETL architect, development, and tuning: logical and physical data modeling; and mentoring on data warehouse, data quality, information governance, and ETL concepts. Don speaks globally and mentors on information management, governance, and quality. He authored book SAP Information Steward: Monitoring Data in Real-Time and is the co-author of two books: Implementing SAP HANA, as well as Creating SAP HANA Information Views. Don has also authored numerous articles for publications such as SAPinsider magazine, Tech Target and Information Management magazine.

Categories: HANA

Using People Analytics to Increase Employee Loyalty

The ability to attract and retain a loyal employee base and understand root causes for employee disengagement and disloyalty are key strategic objectives for every organization – big or small. If you want to improve employee productivity and/or decrease the cost associated with attracting and retaining employees, you need to move along the analytics maturity curve and start leveraging People Analytics.

What is People Analytics?

Put simply, people analytics is a predictive, data-driven approach to managing people at work. Analytics centered around your employees. It is used to address people-related issues, such as talent acquisition, performance evaluations, leadership positioning, hiring and promotion, job and team design, and employee compensation.

Increasing Employee Loyalty Using People Analytics

People analytics help you merge employee data, company data, and market data to predict and interpret valuable employees’ behaviors, as well as operations-level insights, giving you competitive vision for developing your retention strategies.

To download full PDF and Continue Reading...

 

John Harris Headshot About John Harris

John Harris, Senior Manager – Predictive Modeling and Advanced Analytics, has over 16 years of industry experience applying strategic thinking and advanced analytical skill set to optimize resources, improve processes and develop quantitative models that turn data into decision-aid information for all levels of leadership. Airline and energy utility employers have attempted to patent his deliverables related to predictive and optimization modeling.

Categories: Predictive Analytics