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SKIM webinar "Product Portfolio and Revenue Optimization"

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Ever wondered if you should increase your product price or decrease your pack size? Among the many options your pricing, strategy, and research teams face in a competitive consumer environment, this is one of the most common. In our webinar we explore ways to optimize your pricing and your product portfolio composition to maximize overall revenue.

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SKIM webinar "Product Portfolio and Revenue Optimization"

  1. 1. Product Portfolio and Revenue Optimization Juan Andres Tello Scott Garrison SKIM Director Americas Today’s webinar host j.tello@skimgroup.com s.garrison@skimgroup.com SKIM Webinar May 24, 2012 | “Product Portfolio and Revenue Optimization.”
  2. 2. Outline Motivation for Revenue Optimization (RO) RO requires a MR shift from insight to forecast Building blocks of an RO system a) Consumer behavior models b) Demand forecasting c) Constrained optimization approach Some RO strategies Delivering optimization results to clients2
  3. 3. Revenue Optimization - Motivation• Maximize: Revenue = f(Pricing, Product portfolio composition | Selling channel)• Turns data into actionable foresight tools for clients • Determine optimal pricing/portfolio strategy within given constraints• RO pioneers: fixed capacity industries
  4. 4. How to charge the max willingness to pay to each customer?  marginal cost Demand C d(p) 1,000 A B 0 $5 $10 $15 Price• Solution  price differentiation (sometimes controversial)
  5. 5. RO requires a MR shift from insight to foresight90% of consumer-facing companies have a Consumer Insights (CI)function in early stages of development (1) or (2) 4 BCG’s CI stages of development 3 Strategic foresight 2 Strategic organization insight Business organization 1 contribution Traditional team MR function MR as an Consumer insight order-taking as a source of function competitive advantageSource: BCG Consumer Insight Benchmarking (May 2009)
  6. 6. Building blocks of a RO system1. Quantitative models of consumer behavior  Choice based Conjoint (CBC)2. Demand forecasts  Market simulator3. Constrained optimization tools  Search algorithm of optimal solution within market constraints
  7. 7. 1. Choice based Conjoint• Proven and unbiased research technique to model consumer preferences and market heterogeneity• Rooted in Utility Theory (Von Neumann–Morgenstern)• Preferences estimation process has evolved over time: 1. Aggregate Logit model (one size fits all) 2. Latent class (segmentation) 3. Hierarchical Bayes (individual level)• Choice task resembles purchase behavior process
  8. 8. 1. Choice tasks within a competitive context
  9. 9. 2. Market Simulator: from consumer preferences tomarket shares, to revenue forecasting• Volumetric adjustments and calibrations Input prices• Ability to test unlimited pricing Change /portfolio strategies and portfolio potential competitive reactions composition Market share output• In its simplest form, the Revenue simulator is a “show of hands” output from respondents given a number of choice options
  10. 10. 3. Searching for the optimal: define the feasible space first Total space of possible Constrained space of solutions feasible solutions Sample of solutions within constraints
  11. 11. 3. Searching for the optimal: define objective function &apply search algorithm Max Revenue (Optimal solution) Revenue Surface
  12. 12. 3. It’s not only about finding the winning solution, but about the patterns observed• While the main goal is to uncover the strategy that maximizes revenue, ask yourself: Focus on • What makes it the optimal solution? Max Rev gain = 8% upper right quadrant • Are there alternate strategies with different tradeoffs yielding positive results?• In this example: • 80,000 scenarios generated • 40% yield gains in both revenue and share. Cluster analysis is used to further group and interpret
  13. 13. Some RO strategies1. Maximize volume share profitably (capping revenue loss) Balanced “investment” strategy to grow customer base2. Maximize revenue while capping volume loss Ideal situation, not always feasible; will depend on price elasticity3. Game theory strategies: competitive reactions
  14. 14. Delivering optimization results to clientsA few insights for a successful deployment:• Involve key stakeholders from different functions early in the game• Plan accordingly • Kick-off: constraints from every function are expressed and discussed • Delivery: results are discussed in a workshop style• Dynamic session • Create tools that allow clients to interact with the data (e.g. ability to activate/deactivate constraints, rank and select scenarios) • Don’t be afraid to show the “raw” data; involve stakeholders in the analysis• As always, be clear about the model’s assumptions and limitations
  15. 15. contact us or follow us online! Juan Andres Tello Scott Garrison SKIM Director Americas Today’s webinar host j.tello@skimgroup.com s.garrison@skimgroup.com

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