Business Analytics For Consumer Packaged Goods

Analytics for Consumer Packaged Goods

Most CPG companies are facing increasing complex environments that make it extremely difficult to construct strategies for the future. Their Customers and Competitors have driven them to maintaining more SKUs, more product and services bundling, more promotions, and more complex pricing approaches. Add to this a multitude of distribution channels they employ. It’s not surprising that many CPG companies focus on historical trends to base their decisions on, but the world-class companies look past yesterday and today turning their vision to the future through the use of Predictive Analytics.

Price elasticity predictions and optimization

  • Test and optimize your preferences of revenue maximization vs. profit maximization
  • Across Portfolio and Categories
  • Predict return on investment (ROI) comparisons based on different optimization scenarios
  • Predictive Analytics replaces the need for lengthy consumer price testing, in assessing the impact of price changes
  • Predict and identify miscalculations in pricing. Identify and revise these before too much damage is done

Product Assortment optimization

  • Predict and optimize discounting vs. cross-sell opportunities (e.g. if we offer 30% off this product, what’s the likelihood that shoppers will also purchase related products that are priced at X?)
  • Predictive market basket analysis can also provide rich insights into potential up-sell or cross-sell opportunities with customers (e.g. predict which combination of products will most likely be bought together?).
  • Branded vs. private label decisions: Predict optimal combinations of Branded vs. Private label product offerings to maximize revenue, profitability, market share, etc.
  • Analyze the above by Geographies, Market Segments, Categories, etc.

Sales / Distribution Channels analysis

  • Make strategic plans and decisions on the future viability of your current channels, and the need to alter them
  • Execute predictive optimization scenarios to decide between potential new channels
  • Analyze optimization scenarios to assess the likelihood of success of new channels to accommodate new product introductions

Macro-economic predictive analysis

  • Have the insights to anticipate changes in consumer behavior, and postulate new approaches to promotions, pricing, sales channels, product development, etc. driven by anticipated changes in macroeconomic conditions
  • Utilize predictive optimization models to test which scenarios will provide the desired behavior in maximizing revenue, profitability, market share, etc.

For more information on CGN's Business Analytics practice, contact Syamala Srinivasan at Syamala.Srinivasan@cgn.net