PRICING AND PROMOTION ANALYTICS in CPG and Retail industry

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VARIOUS PRICING AND PROMOTION ANALYTICS TECHNIQUES


Price Optimization Models

§ These are mathematical programs that calculate how demand varies at different price levels, and then combine that data with information on costs and inventory levels to recommend prices that will improve profits

§ Can be used to forecast demand, develop pricing and promotion strategies, control inventory levels, and improve customer satisfaction

WNS_Article_Unlocking the Power of Pricing Analytics for Higher Marketing Returns.pdf

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§ Also aid promotional price optimization (help set temporary prices to spur sales of items with long life-cycles, newly introduced products, products bundled together in special promotions and loss leaders)

Price Elasticity, Threshold and Gap Models

These models enable pricing managers to react better to retailer-led price changes and promotions, and incorporate the impact of these events into forecasting process

§ Cross Price Elasticity Models at a product-market level quantify the impact of price changes implemented by retail partners on own brands as well as competitor brands

§ Price Threshold Models identify the price bands beyond which sales show a significant drop in volumes

§ Price Gap Models factor the price difference between their products and competitor products to determine the optimal price gaps with respect to competitor products

Trade Promotion Optimization Models

§ These models create an optimal corporate / customer promotional calendar that generate the desired sales volume and / or profit without overspending the trade budget

§ Utilize highly configurable constraints focused on timing, frequency, product dep

Promotional Lift Models

§ Utilizing historical weekly data, lift models can identify and quantify the impact that discounted retail prices, different forms of merchandizing, seasonality, and other retail conditions have on weekly sales to the end consumer

 

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3.Robust and Comprehensive Modeling Capabilities

Disparate data sources as well as market and category contexts challenge a company's analytical maturity. A one-size-fits-all cannot produce analytical rigor. Companies should leverage statistically validated and robust modeling techniques for getting the desired business outcomes. The most frequently used techniques include multivariate linear model, multivariate log-linear model, multivariate log-linear model with Bayesian shrinkage property, and Hierarchical Bayesian model. These models help determine price elasticity, cross-price elasticity, and pricing corridors that can influence sales, direct marketing promotions, and other deals.