The serendipity algorithm
Reducing uncertainty while increasing the entertainment value of e-commerce.
ser·en·dip·i·ty /,ser n’dip de/ - The occurrence and development of events by chance in a happy or beneficial way. “a fortunate stroke of serendipity”
The intersection between the demand creation (R&D, marketing and sales) and demand fulfillment (sourcing, production and logistics operations) has long been the source of variability and uncertainty in planning and execution. Operations planning accuracy has long been the result of chance and art rather than of science. While sales & operations planning (S&OP) and integrated business planning (IBP) processes seek to reduce the dependence on serendipity, or luck, to improve planning accuracy, very few operations managers rely on their output when making daily execution decisions. My colleague, Rich Sherman, likes to say: “Forecasts that rely solely on historical data most often produce hysterical results.” Planning focused on history does not account for marketing’s objective and initiatives to cause history to change.
Harmonizing planning and execution with AI Online sellers, retailers and, increasingly, B2B vendors employ “offering analytics” to determine the sales offerings that will most cost-effectively drive sales performance above past performance. In this context, the sales offering consists of the core offering (the primary product or primary service) and the extended offering, including fulfillment, warranties, pricing, promotions, payment terms and other services. Offering analytics techniques include the following:
Attribute analysis. Attribute analysis determines customer preference based on product and offering attributes such as color, price point and application.
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ser·en·dip·i·ty /,ser n’dip de/ - The occurrence and development of events by chance in a happy or beneficial way. “a fortunate stroke of serendipity”
The intersection between the demand creation (R&D, marketing and sales) and demand fulfillment (sourcing, production and logistics operations) has long been the source of variability and uncertainty in planning and execution. Operations planning accuracy has long been the result of chance and art rather than of science. While sales & operations planning (S&OP) and integrated business planning (IBP) processes seek to reduce the dependence on serendipity, or luck, to improve planning accuracy, very few operations managers rely on their output when making daily execution decisions. My colleague, Rich Sherman, likes to say: “Forecasts that rely solely on historical data most often produce hysterical results.” Planning focused on history does not account for marketing’s objective and initiatives to cause history to change.
Harmonizing planning and execution with AI Online sellers, retailers and, increasingly, B2B vendors employ “offering analytics” to determine the sales offerings that will most cost-effectively drive sales performance above past performance. In this context, the sales offering consists of the core offering (the primary product or primary service) and the extended offering, including fulfillment, warranties, pricing, promotions, payment terms and other services. Offering analytics techniques include the following:
Attribute analysis. Attribute analysis determines customer preference based on product and offering attributes such as color, price point and application.
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