This is a review of a chapter from ""Competing on Analytics"-
Analytical competitors look beyond basic statistics and do the following:
  • They use predictive modeling to identify the most profitable customers- as well as those with the greatest profit potential and the ones most likely to cancel their accounts.
  • They integrate data generated in-house with data acquired from outside sources for a comprehensive understanding of their customers
  • They optimize their supply chains and can this determine the impact of unexpected glitches, simulate alternatives, and route shipments around problems
  • They analyze historical sales and pricing trends to establish prices in real time and get the highest yield possible from each transaction.
  • They use sophisticated experiments to measure the overall impact or "lift" of advertising and other marketing strategies and then apply their insights to future analyses.  
Typical Analytical Applications in Marketing
  1. CHAID- h-square automatic interaction detection. Used to segment customers on the basis of alternative variables. 
  2. Conjoint analysis- Typically used to evaluate the strength and direction of customer preferences for a combination of product or service attributes. Determine which which factors (price, quality, location...) are most important.
  3. Lifetime value analysis- Analytical model to assess the profitability of an individual customer over a lifetime of transactions. Other models generate estimates of costs incurred by the customer in purchasing and expenses from call centers. 
  4. Market experiments- Using direct mail, changes in the Web site, promotions, and other techniques, marketers test variables to determine what customers respond to most in a given offering. 
  5. Multiple regression analysis- Most common statistical technique for predicting the value of a dependent variable (sales) in relation to one or more independent variables (number of salespeople, temperature, day of the month). While basic regression assumes linear relationships, modification of the modela can deal with non-linearity, logarithmic relationships etc...
  6. Price optimization- Also known as yield or revenue management, this technique assumes the primary causal variable in customer purchase behavior is price. Key issue is usually price elasticity. 
  7. Time series experiments- Experimental designs to follow a particular population for successive points in time. Could be used to determine the impact of exposure to advertising. 
Typical Analytical Applications in Supply Chains
  1. Capacity planning- Finding capacity, identifying and eliminating bottlenecks.
  2. Demand-supply matching- Determining intersection of demand and supply curves to optimize inventory and minimize overstocks and stock-outs. Typically involves such issues as arrival processes, waiting times, and throughput losses.
  3. Location analysis- Optimization of locations.
  4. Modeling- Creating models to simulate, explore contingencies, and optimize supply chains.
  5. Routing- Finding the best path for delivery around a set of locations.
  6. Scheduling- Creating detailed schedules for the flow of resources and work through a process. 

My Thoughts: This chapter has much of the meat that was lacking in the previous chapter. Again, notice that we are moving beyond just reporting. 


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