Marketing Forecasting at Post Foods division of Ralcorp Holding

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I delivered this presentation at the IBF conference in Orlando in October. I describes how Post Foods Marketing forecasts demand for major brands.

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Marketing Forecasting at Post Foods division of Ralcorp Holding

  1. 1. Marketing Forecast At Post Foods, Division of Ralcorp Holdings. 1Fostering Demand Planning and Forecasting for Nearly 30 Years!
  2. 2. Main Topic The topic of this presentation is forecasting fast moving consumer goods from a Marketing point of view. Examine the business drivers and spending that impact a brand performance and how to quantify them into a forecast. 2Fostering Demand Planning and Forecasting for Nearly 30 Years!
  3. 3. Agenda • Forecasting at Post Foods • How We Build the Forecast • S&OP Consensus Process • Marketing Forecast Tools – Inputs • The Drivers – Driver Details • External Factors • Forecast Accuracy Measurement & Actions • Measures of Success • Challenges and Solutions • Next Steps and Questions 3Fostering Demand Planning and Forecasting for Nearly 30 Years!
  4. 4. Post Foods • Founded by C. W. Post in 1895 • Was part of General Foods and then Kraft Foods until 2008 when Post was acquired by Ralcorp Holdings • Headquartered in Parsippany, NJ • Over $1 Billion gross revenue - ~40% of Ralcorp net revenue • #3 Ready to Eat Cereal, grown by 125% over the last 6 years • 4 Plants plus several co-packers and 6 warehouses • 103 SKU’s • ~8 primary brands • Customers include major grocery stores, mass merchandisers, club, drug stores and Dollar stores • David Zatz – Marketing Forecast Planner 4Fostering Demand Planning and Forecasting for Nearly 30 Years!
  5. 5. Forecasting at Post Foods • Scope • Consumer goods – Ready To Eat Cereal; US Domestic only • Short term and medium term – current quarter through next fiscal year – this is Tactical S&OP, not Strategic • Longer range forecasting is done less frequently for capacity analysis and is outside the scope of this presentation • Focused on Brand but calculated at SKU • Results drive Production Planning, Deployment and Financial forecast • Methodology • Monthly Cycle and monthly buckets • Strive to arrive at “one number” • Avoid changing the forecast for next month 5Fostering Demand Planning and Forecasting for Nearly 30 Years!
  6. 6. How We Build the Forecast• Marketing drivers forecast impact vs. year ago by brand• Sales force bottom up forecast used for the short term one to four month time period, built mostly by customer and geography• Statistical forecasting at the SKU level used for scheduling and deployment• All the voices come together in a monthly S&OP process to arrive at one number to drive the business and report up to corporate 6Fostering Demand Planning and Forecasting for Nearly 30 Years!
  7. 7. S&OP Consensus ProcessDifferent• Drivers• Goals• Units of measureOne Number 7Fostering Demand Planning and Forecasting for Nearly 30 Years!
  8. 8. S&OP Consensus Process (cont.) • Risks and Opportunities are discussed but not included in the forecast – our version of a “range” forecast • Gaps in our numbers are used to alert management to issues which may drive policy decisions to guide us to one number • This method works for Post Foods and other fast moving consumer goods brands because a large portion of customer sales are driven by trade promotion, and consumer consumption is driven by advertising and promotion. The Marketing Manager is the general manager of the brand. Sales and Marketing report separately to the President. 8Fostering Demand Planning and Forecasting for Nearly 30 Years!
  9. 9. Marketing Forecast Tools The Marketing Forecast uses several techniques to arrive at the numbers presented during the S&OP meetings. •Expert opinion and judgmental approach; reliance on the expertise of others  Nielsen syndicated data  Consumer Insights – Marketing Mix Analysis  Sales and Customer behavior •Time series and trend projections using market changes to predict turning points •Planned Marketing programs quantified into impact on expected customer shipments •The use of Nielsen market data to compare drivers to year ago statistics 9Fostering Demand Planning and Forecasting for Nearly 30 Years!
  10. 10. Inputs • Monthly shipment history by SKU – Customer level history is used to examine outlier data • Nielsen syndicated consumption data • Other Marketing and consumer insights analysis for each brand 10Fostering Demand Planning and Forecasting for Nearly 30 Years!
  11. 11. The Drivers • Equity – Advertising – Consumer Promotion – Base Velocity • Innovation – New Products • Price / Merchandising – Merchandising – Base Price – Distribution • Other Channels – Wal-Mart, Club, Dollar • Other – Trade Inventory These use a combination of Art and Science 11Fostering Demand Planning and Forecasting for Nearly 30 Years!
  12. 12. The Drivers (cont.) FY 11 FY 11 FY 11 FY 11 FY 11 FY 11 FY 11 FY 11 FY 11 FY 11 10/26/2010 Oct Nov Dec Jan Feb Mar Apr May Jun Jul 2010 Actual $ 15.6 $ 14.5 $ 14.6 $ 15.0 $ 16.7 $ 16.3 $ 16.1 $ 14.3 $ 17.6 $ 14.0 2011 $ Prior Call $ 15.3 $ 13.5 $ 14.2 $ 16.9 $ 16.9 $ 18.3 $ 17.2 $ 14.8 $ 16.9 $ 16.9 2011 Curr Call $ 12.3 $ 10.7 $ 11.3 $ 16.9 $ 16.9 $ 18.4 $ 17.5 $ 15.0 $ 16.9 $ 16.9 $Chg vs. Prior Call $ (3.0) $ (2.8) $ (2.8) $ - $ - $ 0.1 $ 0.3 $ 0.2 $ (0.0) $ (0.0) Bridge Items: $ - $ - $ - $ - $ - $ - $ - WM Growth $ (2.7) $ (3.0) $ (3.1) $ - $ - $ - $ - $ - $ - $ - Club $ (0.1) $ (1.0) $ 0.4 $ 0.1 $ 0.1 $ 0.4 $ 0.1 $ 0.1 $ 0.1 $ 0.0 Advertising $ (0.9) $ (0.6) $ (0.1) $ 0.3 $ (0.1) $ 2.1 $ (0.2) $ (1.3) $ (0.4) $ 1.2 Merchandising $ (1.0) $ 0.1 $ 0.1 $ (1.3) $ (1.7) $ (0.2) $ 0.9 $ 0.9 $ (0.7) $ 0.8 New Products $ (0.1) $ (0.2) $ 0.1 $ 1.0 $ 0.7 $ 0.8 $ 1.0 $ 1.0 $ 0.9 $ 0.9 Consumer Promotions $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - Base Velocity $ 0.3 $ 0.3 $ 0.3 $ 0.1 $ 0.1 $ 0.1 $ 0.1 $ 0.1 $ 0.1 $ 0.1 Base Price $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - Distribution $ (0.1) $ (0.2) $ (0.2) $ (0.1) $ (0.1) $ (0.1) $ (0.1) $ (0.1) $ (0.1) $ (0.1) Inventory $ 1.4 $ 0.3 $ (0.1) $ 1.0 $ (0.4) $ (0.6) $ 0.0 $ (0.3) $ 0.4 $ (0.4) Other $ (0.2) $ 0.5 $ (0.7) $ 0.7 $ 1.6 $ (0.2) $ (0.3) $ 0.4 $ (1.0) $ 0.3 Sum of the Drivers $ (3.2) $ (3.8) $ (3.3) $ 1.8 $ 0.2 $ 2.1 $ 1.4 $ 0.8 $ (0.8) $ 2.9 2010 Actuals + Drivers $ 12.3 $ 10.7 $ 11.3 $ 16.9 $ 16.9 $ 18.4 $ 17.5 $ 15.0 $ 16.9 $ 16.9 12Fostering Demand Planning and Forecasting for Nearly 30 Years!
  13. 13. Driver Details Equity Advertising and Consumer Promotions are calculated based on planned spending and packages sold on consumer promotion. We’re developing a tool to break that down to flavors and SKU’s. Innovation For New Products, we include the first 12 months of shipments as new products volume and multiply that by an incrementality factor. Early ships are a challenge for new products. 13Fostering Demand Planning and Forecasting for Nearly 30 Years!
  14. 14. Driver Details (cont.) These drivers are calculated for past months using Nielsen syndicated data: Merchandising Base Velocity Base Price Distribution For Trade Inventory, we compare monthly customer shipments to a year ago and monthly consumer consumption to a year ago Other channels (Wal-Mart, Club, Dollar) are fed directly from mangers of those businesses. 14Fostering Demand Planning and Forecasting for Nearly 30 Years!
  15. 15. External Factors • We use factors to control the results – Incrementality – Elasticity – Velocity Weight Factor – Competitive Activity • As the marketplace changes, we use these factors to adjust the drivers • For example, over the last two years we’ve seen a much greater sensitivity to price changes so we can adjust the elasticity to reflect this market change. 15Fostering Demand Planning and Forecasting for Nearly 30 Years!
  16. 16. Forecast Accuracy Measurement & Action • Every month we measure forecast accuracy at the brand, top customers and SKU levels and use that to make adjustments going forward. • For example, will the missed forecast last month result in something that will continue for future months or was it a one-time event that will result in the opposite affect in the short term? • When the absolute error is greater than the threshold, both customer shipments and consumer consumption are examined closely 16Fostering Demand Planning and Forecasting for Nearly 30 Years!
  17. 17. Measures of Success • Spend our Marketing and Trade Promotion dollars to maximize returns and drive our business forward as planned – making revenue and profit targets • Maintain inventory levels and customer fill rate targets while minimizing production disruptions and costs • Anticipate the impact of business decisions • Allow Marketers to focus on Marketing 17Fostering Demand Planning and Forecasting for Nearly 30 Years!
  18. 18. Challenges & Solutions• Forecasting new categories • Use similar products and Bases• Deviating from the focus of forecast • Focus on one number and emphasize accuracy for other business needs best guess; adjust inventory policy for like inflating the forecast where tight capacity; better utilize risks and capacity is tight opportunities• Getting timely and accurate input • Develop and adhere to a firm schedule; from all systems and people get senior management support• Converting the forecast into different • Define the standard conversion rates, units of measure, levels of product maintain and use them for reporting aggregation, geographies, and and integrating results more… 18Fostering Demand Planning and Forecasting for Nearly 30 Years!
  19. 19. Challenges & Solutions (cont.) • Nielsen data is split into months of • Where necessary spread the monthly 4-4-5 weeks but shipments and shipments into a 4-4-5 pattern for forecasts are on calendar months comparison to consumption • Determining trade inventory levels • Combine industry standards with estimates and past patterns • Expanding complexity into other • Fold other categories into existing grocery categories (different systems manufacturing, lead times, etc.) • Profitability by brand and SKU is • Develop the calculation for customer calculated and used to drive profitability – partner more closely decisions but profitability by with customers customer is unknown 19Fostering Demand Planning and Forecasting for Nearly 30 Years!
  20. 20. Next Steps • Deeper analysis for Risks & Opportunities followed by appropriate action • Develop Sales tools and expand their role in the S&OP process • Expand SKU level bottom up calculation in the marketing forecast models to include – Lift – Distribution changes – Other channels – Advertising campaigns and promotional events 20Fostering Demand Planning and Forecasting for Nearly 30 Years!
  21. 21. Questions DLZatz@gmail.com 21Fostering Demand Planning and Forecasting for Nearly 30 Years!

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