Having spent so much time in the Entertainment and Hospitality industry, I see everywhere the key systems being CRM and Revenue Management systems. Yield Management is a market segmentation strategy to capture the consumer surplus, widely used in the Entertainment and Hospitality industry. Airlines, hotels, theme parks, car rentals, cruise ships, broadcasting (TV, radio), and Utilities (telecom, electricity), etc. always used segmentation and peak-load pricing. But with the advent in computing power of systems this technique is now able to do excessive and data intensive calculations with linear programming to optimize revenue. Just like airlines realized long ago that a flight flying with an empty seat is forgone revenue, many companies use historical data to optimize differential revenue gains. American Airlines had added $1.4B additional revenue over three-year period in the early 1990s. Hertz added 1-5% revenue annually and so did companies like Marriott Hotels, Royal Caribbean Cruise Line, etc. Computer algorithms can use variables like time of purchase/usage (advance/spot purchase, day-of-week/season), purchase restrictions (cancellation options, minimum term, Saturday night stay), purchase volume (individual vs. group), and duration of usage (single night/weekly rate) to get the right price to the right customer. Next time you are stuck an airport and an airlines mentioned over-booked flight, know that some computer algorithm was doing some marginal analysis based on capacity of flight, typical cancellation rate, revenue from booking seat and cost of denied boarding (these days with the pressure airline are also adding costs like loss of goodwill along with the free flights they give you).
Other analytic systems within an organization depending upon what the goals of a business or its units are.
- Monte Carlo Simulation – This technique uses data to establish a pattern between a domain of possible inputs. The calculations generate inputs randomly from a probability distribution over the domain, perform computations on these and aggregate the results. This tries to minimize reinventing the wheel by reuse of research results. In the Pharmaceutical industry, for example, during the Discovery phase of their value chain, data mining is used to search contextual information based on secondary relationships,
- Regression Analysis – This is a set of techniques used for modeling and analyzing several variables and establish a relationship between a dependent variable and one or more independent variables. It is widely used in Marketing and Sales part of a company’s value chain since it can provide deep insight on customer behavior and provide an enhanced decision-making for future customer interactions. Companies are using industrialized analytics which uses closed-loop promotion and data mining to optimize marketing budget allocation (i.e. optimizes marketing channel and product mix). The closed-loop-promotion ensures feedback of mission-critical data to marketing and provides on-demand access to marketing decision-support. This helps creates an optimal marketing mix (as Jerome McCarthy called this the 4P’s – product, price, place, and promotion). It started with improvements in systems for statistical analysis that helped understand why certain things were happening in the business environment. These were heavily used to do web traffic analyses during the emergence of eCommerce. Then these systems were extended to forecasting and extrapolation to see what would happen if the trends continued. Then the era of predictive modeling built systems what could try to predict what will happen next, based on empirical data and heuristics. Of course all these systems are used to help with optimization of spend and maximize the revenue. As an example, within the financial services industry, specifically Banks, such models are used to predict Retention & Loyalty, perform Portfolio Analytics, help with Fraud (Transaction/ Payment) and Anti Money Laundering efforts. They help answer questions like Bank Servicing – Which customers/ segments are at risk? Which ones are profitable? How do I retain them? How do I win back customers? They can also help with Loyalty Programs – What drives loyalty for my customer base? How do I design an effective loyalty program? They are used for Cross Selling and Upselling – Which deposit account holders would be interested in an auto loan? Can we sell insurance along with auto loan? Which products can be sold on credit card welcome call? Which ‘Gold’ customers would increase their spend if we upgrade them to ‘Platinum’? Can I identify customer requiring short term loan? It helps with Campaign Management with segmentation modeling, profitability analysis, retention campaigns, win-back campaigns, etc.
- Neural Network Analysis – Apart from the obvious impactions in the study of real biological neurons, this technique is used in areas like distribution & logistics, sociology, economics, etc. Any company that produces a product is chartered with getting that product to its customers (whether that’s the final customer, retailer, wholesaler, etc.) in the least expensive manner. They are constantly trying to reduce total inventory while maintaining service levels of supply at each warehouse or distribution center location. Neural network models help with optimizing with the variables involved: number of warehouses to have, location of each warehouse, size of each warehouse, allocation of products to the different warehouses, allocation of customers to each warehouse, etc. The objective is to balance service level of supply against production/ purchasing costs, inventory carrying costs, facility costs (storage, handling and fixed costs), and transportation costs.